title
Python TensorFlow for Machine Learning – Neural Network Text Classification Tutorial

description
This course will give you an introduction to machine learning concepts and neural network implementation using Python and TensorFlow. Kylie Ying explains basic concepts, such as classification, regression, training/validation/test datasets, loss functions, neural networks, and model training. She then demonstrates how to implement a feedforward neural network to predict whether someone has diabetes, as well as two different neural net architectures to classify wine reviews. ✏️ Course created by Kylie Ying. 🎥 YouTube: https://youtube.com/ycubed 🐦 Twitter: https://twitter.com/kylieyying 📷 Instagram: https://instagram.com/kylieyying/ This course was made possible by a grant from Google's TensorFlow team. ⭐️ Resources ⭐️ 💻 Datasets: https://drive.google.com/drive/folders/1YnxDqNIqM2Xr1Dlgv5pYsE6dYJ9MGxcM?usp=sharing 💻 Feedforward NN colab notebook: https://colab.research.google.com/drive/1UxmeNX_MaIO0ni26cg9H6mtJcRFafWiR?usp=sharing 💻 Wine review colab notebook: https://colab.research.google.com/drive/1yO7EgCYSN3KW8hzDTz809nzNmacjBBXX?usp=sharing ⭐️ Course Contents ⭐️ ⌨️ (0:00:00) Introduction ⌨️ (0:00:34) Colab intro (importing wine dataset) ⌨️ (0:07:48) What is machine learning? ⌨️ (0:14:00) Features (inputs) ⌨️ (0:20:22) Outputs (predictions) ⌨️ (0:25:05) Anatomy of a dataset ⌨️ (0:30:22) Assessing performance ⌨️ (0:35:01) Neural nets ⌨️ (0:48:50) Tensorflow ⌨️ (0:50:45) Colab (feedforward network using diabetes dataset) ⌨️ (1:21:15) Recurrent neural networks ⌨️ (1:26:20) Colab (text classification networks using wine dataset) -- 🎉 Thanks to our Champion and Sponsor supporters: 👾 Raymond Odero 👾 Agustín Kussrow 👾 aldo ferretti 👾 Otis Morgan 👾 DeezMaster -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news

detail
{'title': 'Python TensorFlow for Machine Learning – Neural Network Text Classification Tutorial', 'heatmap': [], 'summary': 'This tutorial on python tensorflow for machine learning covers supervised learning, basics of machine learning, neural networks, implementing neural nets with tensorflow, using machine learning for diabetes prediction, data preprocessing, and text classification in tensorflow, achieving an 84% accuracy on the test data.', 'chapters': [{'end': 420.397, 'segs': [{'end': 107.335, 'src': 'embed', 'start': 20.737, 'weight': 0, 'content': [{'end': 27.6, 'text': "And then we'll learn how to use TensorFlow to create a neural net, and then use a neural net for some text classification.", 'start': 20.737, 'duration': 6.863}, {'end': 30.497, 'text': "Sound exciting? Let's get started.", 'start': 28.655, 'duration': 1.842}, {'end': 34.26, 'text': 'All right.', 'start': 33.96, 'duration': 0.3}, {'end': 45.29, 'text': "jumping straight into things, the first thing that we're going to do is go to colab.research.google.com, and it'll pull up a page like this", 'start': 34.26, 'duration': 11.03}, {'end': 46.772, 'text': "You're gonna click New Notebook.", 'start': 45.33, 'duration': 1.442}, {'end': 53.358, 'text': "This is going to create a new notebook, and let's retitle this to Free Code Camp Tutorial.", 'start': 47.352, 'duration': 6.006}, {'end': 61.438, 'text': "Okay, it doesn't really matter, you know what you actually rename it to, as long as you have a notebook open.", 'start': 54.611, 'duration': 6.827}, {'end': 65.923, 'text': "And just in case, you know, you have some future notebooks, that's why you want to rename all of them.", 'start': 61.799, 'duration': 4.124}, {'end': 75.655, 'text': "So the typical libraries that I'll import when I usually start a computer data science project are numpy.", 'start': 66.844, 'duration': 8.811}, {'end': 88.942, 'text': 'so import numpy as np pandas, import pandas as pd and then import matplotlib.', 'start': 75.655, 'duration': 13.287}, {'end': 102.473, 'text': "okay, so, because i'm going to be using tensorflow in this video, i'm also going to import tensorflow as TF and then import TensorFlow hub as hub.", 'start': 88.942, 'duration': 13.531}, {'end': 107.335, 'text': "Okay, now if you click Shift Enter, it'll run the cell.", 'start': 103.173, 'duration': 4.162}], 'summary': 'Using tensorflow for text classification in free code camp tutorial.', 'duration': 86.598, 'max_score': 20.737, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VtRLrQ3Ev-U/pics/VtRLrQ3Ev-U20737.jpg'}, {'end': 190.548, 'src': 'embed', 'start': 161.362, 'weight': 2, 'content': [{'end': 165.605, 'text': 'We can wait until this yellow thing goes all the way around.', 'start': 161.362, 'duration': 4.243}, {'end': 168.728, 'text': 'All right, so pause.', 'start': 165.625, 'duration': 3.103}, {'end': 171.398, 'text': 'All right, sweet.', 'start': 170.598, 'duration': 0.8}, {'end': 177.942, 'text': 'Now winereviews.csv has uploaded to this folder, which means that we have access to it in this notebook now.', 'start': 171.639, 'duration': 6.303}, {'end': 185.986, 'text': 'So now what we can actually do is import that CSV file as a data frame using pandas, which is this library here that we imported.', 'start': 178.562, 'duration': 7.424}, {'end': 190.548, 'text': "So what I'm going to do is say df equals pd.readcsv.", 'start': 186.786, 'duration': 3.762}], 'summary': 'Uploaded winereviews.csv to notebook, imported as data frame using pandas.', 'duration': 29.186, 'max_score': 161.362, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VtRLrQ3Ev-U/pics/VtRLrQ3Ev-U161362.jpg'}, {'end': 363.828, 'src': 'embed', 'start': 333.314, 'weight': 4, 'content': [{'end': 338.116, 'text': "Okay, I mean, we still see this because we didn't drop anything that column because it doesn't really matter to us.", 'start': 333.314, 'duration': 4.802}, {'end': 344.039, 'text': "Let's just quickly see let's plot like the points column to see the distribution of the points.", 'start': 339.056, 'duration': 4.983}, {'end': 346.44, 'text': 'So we can use matplotlib for that.', 'start': 344.659, 'duration': 1.781}, {'end': 349.301, 'text': "So let's do PLT dot hist.", 'start': 347.1, 'duration': 2.201}, {'end': 357.624, 'text': "So this is going to be a histogram, which shows the distribution of values, because it's only a one dimensional variable.", 'start': 350.561, 'duration': 7.063}, {'end': 363.828, 'text': "So Let's do DF dot points, which calls like this points column.", 'start': 358.245, 'duration': 5.583}], 'summary': 'Using matplotlib, a histogram of the points column in the dataframe is plotted to visualize its distribution.', 'duration': 30.514, 'max_score': 333.314, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VtRLrQ3Ev-U/pics/VtRLrQ3Ev-U333314.jpg'}], 'start': 0.229, 'title': 'Introduction to machine learning with tensorflow and data science project setup and data analysis', 'summary': 'Covers supervised learning using tensorflow to create a neural net for text classification, and also discusses the process of setting up a data science project, uploading data files, importing csv files as data frames using pandas, and visualizing data distribution using matplotlib.', 'chapters': [{'end': 65.923, 'start': 0.229, 'title': 'Introduction to machine learning with tensorflow', 'summary': 'Discusses supervised learning using tensorflow to create a neural net for text classification, which involves creating a new notebook on colab.research.google.com and renaming it for future use.', 'duration': 65.694, 'highlights': ['The chapter focuses on supervised learning using TensorFlow to create a neural net for text classification.', 'Instructions are provided on how to create a new notebook on colab.research.google.com and rename it for future use.']}, {'end': 420.397, 'start': 66.844, 'title': 'Data science project setup and data analysis', 'summary': 'Discusses the typical libraries to import for a computer data science project, uploading a data file, importing a csv file as a data frame using pandas, and visualizing the distribution of points in the dataset using matplotlib.', 'duration': 353.553, 'highlights': ['Typical libraries to import for a computer data science project are numpy, pandas, and matplotlib. The speaker mentions importing numpy, pandas, and matplotlib as the typical libraries to start a computer data science project.', 'Uploading a data file and importing it as a data frame using pandas. The speaker demonstrates uploading a data file and importing it as a data frame using pandas, specifying certain columns to include.', 'Visualizing the distribution of points in the dataset using matplotlib. The speaker uses matplotlib to plot a histogram showing the distribution of values in the points column of the dataset.']}], 'duration': 420.168, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VtRLrQ3Ev-U/pics/VtRLrQ3Ev-U229.jpg', 'highlights': ['The chapter focuses on supervised learning using TensorFlow to create a neural net for text classification.', 'Instructions are provided on how to create a new notebook on colab.research.google.com and rename it for future use.', 'Uploading a data file and importing it as a data frame using pandas.', 'The speaker mentions importing numpy, pandas, and matplotlib as the typical libraries to start a computer data science project.', 'Visualizing the distribution of points in the dataset using matplotlib.']}, {'end': 1337.142, 'segs': [{'end': 494.737, 'src': 'embed', 'start': 471.689, 'weight': 2, 'content': [{'end': 481.312, 'text': 'Well, machine learning is a subdomain of computer science that focuses on algorithms which help a computer learn from data without explicit programming.', 'start': 471.689, 'duration': 9.623}, {'end': 485.894, 'text': "For example, let's say I had a bunch of sports articles and a bunch of recipes.", 'start': 481.912, 'duration': 3.982}, {'end': 494.737, 'text': 'Explicit programming would be if I told the computer, hey, look for these specific words such as goal or player.', 'start': 486.814, 'duration': 7.923}], 'summary': 'Machine learning: computer learns from data without explicit programming.', 'duration': 23.048, 'max_score': 471.689, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VtRLrQ3Ev-U/pics/VtRLrQ3Ev-U471689.jpg'}, {'end': 675.613, 'src': 'embed', 'start': 648.078, 'weight': 1, 'content': [{'end': 659.22, 'text': 'we can stick this data into a model and hope that the model is able to then generalize to other future pictures of cats or dogs or lizards and correctly classify them.', 'start': 648.078, 'duration': 11.142}, {'end': 664.362, 'text': "Now there's also such thing as unsupervised learning.", 'start': 661.84, 'duration': 2.522}, {'end': 671.389, 'text': 'And in this case, it uses unlabeled data in order to learn certain patterns that might be hiding inside the data.', 'start': 664.542, 'duration': 6.847}, {'end': 675.613, 'text': "So let's go back to our pictures of our animals.", 'start': 672.69, 'duration': 2.923}], 'summary': 'Using models to generalize and classify data, including unsupervised learning for patterns.', 'duration': 27.535, 'max_score': 648.078, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VtRLrQ3Ev-U/pics/VtRLrQ3Ev-U648078.jpg'}, {'end': 747.451, 'src': 'embed', 'start': 724.201, 'weight': 0, 'content': [{'end': 733.545, 'text': "So in reinforcement learning, there's an agent that's learning in an interactive environment, and it's learning based off of rewards and penalties.", 'start': 724.201, 'duration': 9.344}, {'end': 737.907, 'text': "So let's think about a pet, for example.", 'start': 734.285, 'duration': 3.622}, {'end': 747.451, 'text': 'And every single time our pet does something that we want it to, so for example, some sort of trick, we give it a treat, such as in this picture.', 'start': 738.667, 'duration': 8.784}], 'summary': 'Reinforcement learning involves agent learning from rewards and penalties in an interactive environment.', 'duration': 23.25, 'max_score': 724.201, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VtRLrQ3Ev-U/pics/VtRLrQ3Ev-U724201.jpg'}, {'end': 837.89, 'src': 'embed', 'start': 805.29, 'weight': 3, 'content': [{'end': 814.275, 'text': "We have a series of inputs that we're feeding into some model, and then this model is generating some sort of output or prediction.", 'start': 805.29, 'duration': 8.985}, {'end': 823.519, 'text': 'And the coolest part is that this model, we as programmers are not really telling this model any specifics.', 'start': 815.175, 'duration': 8.344}, {'end': 825.941, 'text': "We're not explicitly programming anything.", 'start': 823.559, 'duration': 2.382}, {'end': 837.89, 'text': 'Rather, this model, our computer is trying to learn patterns amongst, you know, these input this input data in order to come up with this prediction.', 'start': 826.781, 'duration': 11.109}], 'summary': 'Model learns patterns from input data to make predictions.', 'duration': 32.6, 'max_score': 805.29, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VtRLrQ3Ev-U/pics/VtRLrQ3Ev-U805290.jpg'}, {'end': 1022.703, 'src': 'embed', 'start': 987.819, 'weight': 4, 'content': [{'end': 989.18, 'text': "So that's one hot encoding.", 'start': 987.819, 'duration': 1.361}, {'end': 1001.529, 'text': "It turns these different groups into a vector and I guess switches on that category with a one if that category applies and zero if it doesn't.", 'start': 989.24, 'duration': 12.289}, {'end': 1004.767, 'text': 'Now there are also other types of qualitative features.', 'start': 1002.605, 'duration': 2.162}, {'end': 1012.013, 'text': 'So something like age, even though a number might be associated with it, if we take different groupings of age.', 'start': 1005.227, 'duration': 6.786}, {'end': 1022.703, 'text': 'so, for example, baby kid, Gen Z, young adult boomer, et cetera, et cetera.', 'start': 1012.013, 'duration': 10.69}], 'summary': 'Hot encoding turns categories into vectors with 0s and 1s.', 'duration': 34.884, 'max_score': 987.819, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VtRLrQ3Ev-U/pics/VtRLrQ3Ev-U987819.jpg'}, {'end': 1078.863, 'src': 'embed', 'start': 1049.852, 'weight': 5, 'content': [{'end': 1053.953, 'text': "So even though it's qualitative, it has some sort of inherent ordering to it.", 'start': 1049.852, 'duration': 4.101}, {'end': 1056.314, 'text': 'So hence the name ordinal data.', 'start': 1054.313, 'duration': 2.001}, {'end': 1062.176, 'text': 'Now in order to encode this into numbers, we might just use a system like 12345.', 'start': 1058.215, 'duration': 3.961}, {'end': 1066.378, 'text': 'Quick note the reason why we use one hot,', 'start': 1062.176, 'duration': 4.202}, {'end': 1075.142, 'text': "encoding it for our nationalities but we can use 12345 for ordinal data is because let's think about things this way.", 'start': 1066.378, 'duration': 8.764}, {'end': 1078.863, 'text': 'Our computer knows that two is closer to one than five, right?', 'start': 1075.242, 'duration': 3.621}], 'summary': 'Ordinal data can be encoded using a simple numbering system like 12345.', 'duration': 29.011, 'max_score': 1049.852, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VtRLrQ3Ev-U/pics/VtRLrQ3Ev-U1049852.jpg'}, {'end': 1161.054, 'src': 'embed', 'start': 1119.033, 'weight': 6, 'content': [{'end': 1127.54, 'text': "So you can't necessarily say two, which I think, was I assigned to India, is closer to one USA than France,", 'start': 1119.033, 'duration': 8.507}, {'end': 1134.286, 'text': "which is for like a computer would think that, but just thinking about it logically, that wouldn't really make sense.", 'start': 1127.54, 'duration': 6.746}, {'end': 1141.812, 'text': "So that's the real difference between these two types of qualitative data sets is how you want to encode them for your computer.", 'start': 1134.726, 'duration': 7.086}, {'end': 1145.395, 'text': "When we're talking about features, we also have quantitative.", 'start': 1142.853, 'duration': 2.542}, {'end': 1149.852, 'text': 'features And quantitative features are numerical valued inputs.', 'start': 1145.891, 'duration': 3.961}, {'end': 1153.232, 'text': 'And so this could be discrete, and it could also be continuous.', 'start': 1150.432, 'duration': 2.8}, {'end': 1161.054, 'text': 'So for example, if I wanted to measure the size of my desk, that would be a quantitative variable.', 'start': 1153.753, 'duration': 7.301}], 'summary': 'Distinguishing between qualitative and quantitative data, with examples of numerical valued inputs.', 'duration': 42.021, 'max_score': 1119.033, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VtRLrQ3Ev-U/pics/VtRLrQ3Ev-U1119033.jpg'}, {'end': 1242.644, 'src': 'embed', 'start': 1211.563, 'weight': 8, 'content': [{'end': 1212.904, 'text': 'Over here, this is continuous.', 'start': 1211.563, 'duration': 1.341}, {'end': 1214.505, 'text': 'And over here, this is discrete.', 'start': 1213.224, 'duration': 1.281}, {'end': 1220.918, 'text': 'Those features, those are the different types of inputs that we might be able to feed into our model.', 'start': 1215.894, 'duration': 5.024}, {'end': 1225.941, 'text': 'What about the different types of predictions that we can actually make with the model?', 'start': 1222.078, 'duration': 3.863}, {'end': 1231.986, 'text': 'So there are a few different tasks that you know we have in supervised learning.', 'start': 1227.843, 'duration': 4.143}, {'end': 1236.609, 'text': "So there's classification, which means that we're predicting discrete classes.", 'start': 1232.546, 'duration': 4.063}, {'end': 1242.644, 'text': "For example, let's say we have a bunch of pictures of food.", 'start': 1238.442, 'duration': 4.202}], 'summary': 'Supervised learning involves classification to predict discrete classes from different types of inputs.', 'duration': 31.081, 'max_score': 1211.563, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VtRLrQ3Ev-U/pics/VtRLrQ3Ev-U1211563.jpg'}], 'start': 421.038, 'title': 'Machine learning fundamentals', 'summary': 'Covers basics of machine learning including supervised, unsupervised, and reinforcement learning, and discusses qualitative and quantitative data, one hot encoding, ordinal data, and types of predictions in machine learning.', 'chapters': [{'end': 777.835, 'start': 421.038, 'title': 'Machine learning basics', 'summary': 'Explains the basics of machine learning, including supervised learning, unsupervised learning, and reinforcement learning, and highlights the differences between artificial intelligence, machine learning, and data science.', 'duration': 356.797, 'highlights': ['Machine learning is a subdomain of computer science that focuses on algorithms which help a computer learn from data without explicit programming. Machine learning enables computers to learn from data without being explicitly programmed, leading to more flexible and adaptive systems.', 'Supervised learning uses labeled inputs to train models and make predictions, while unsupervised learning utilizes unlabeled data to discover hidden patterns. Supervised learning relies on labeled data to train models and make predictions, while unsupervised learning discovers patterns in unlabeled data, enhancing understanding and insights.', 'Reinforcement learning involves learning in an interactive environment based on rewards and penalties, simulating a process similar to training a pet. Reinforcement learning entails learning through an interactive process with rewards and penalties, similar to training a pet through positive and negative stimuli.']}, {'end': 929.007, 'start': 781.907, 'title': 'Supervised learning and qualitative data', 'summary': 'Covers the concept of supervised learning and qualitative data, focusing on using labeled input and output pairings for making predictions, and discussing qualitative data types such as categorical features and nominal data.', 'duration': 147.1, 'highlights': ['Supervised learning involves using labeled input and output pairings to make future predictions. The model utilizes labeled input and output pairings for future predictions without explicit programming.', 'Machine learning model learns patterns from input data to generate predictions without explicit programming. The model learns patterns from input data to generate predictions without specific programming instructions.', 'Discusses qualitative data as categorical with finite numbers of categories or groups, using gender as an example. Qualitative data is explained as categorical with finite categories or groups, exemplified by gender as a qualitative feature.']}, {'end': 1118.733, 'start': 929.547, 'title': 'One hot encoding and ordinal data', 'summary': 'Explains the concept of one hot encoding and ordinal data, with examples of encoding qualitative features into numerical values, such as nationalities and age categories.', 'duration': 189.186, 'highlights': ['One hot encoding represents different groups as vectors with a one indicating the presence of a category and zero indicating absence, e.g., USA as 1000, India as 0100, Canada as 0010, and France as 0001. One hot encoding transforms different nationalities into vectors, with a one marking the presence of a category and zero marking absence, e.g., USA as 1000, India as 0100, Canada as 0010, and France as 0001.', 'Ordinal data, such as age categories and rating systems, can be encoded using numerical values with inherent ordering, e.g., using a system like 12345 for rating systems. Ordinal data like age categories and rating systems can be encoded using numerical values with inherent ordering, e.g., using a system like 12345 for rating systems.', 'Explanation of why one hot encoding is used for nationalities, while numerical values are used for ordinal data, based on the inherent ordering of the data. Clarification on the use of one hot encoding for nationalities and numerical values for ordinal data, based on the inherent ordering of the data.']}, {'end': 1337.142, 'start': 1119.033, 'title': 'Types of data and predictions in machine learning', 'summary': 'Explains the difference between qualitative and quantitative data sets, the types of quantitative features, and the different tasks in supervised learning such as multi-class and binary classification.', 'duration': 218.109, 'highlights': ['The chapter explains the difference between qualitative and quantitative data sets Discusses the distinction between qualitative and quantitative data sets and the implications for encoding them for computer processing.', 'Types of quantitative features and their examples are discussed Details the nature of quantitative features, including discrete and continuous variables, with examples like measuring desk size and temperature.', 'Explanation of multi-class and binary classification in supervised learning Details the tasks in supervised learning, including multi-class classification with multiple discrete classes and binary classification with only two classes.']}], 'duration': 916.104, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VtRLrQ3Ev-U/pics/VtRLrQ3Ev-U421038.jpg', 'highlights': ['Reinforcement learning entails learning through an interactive process with rewards and penalties, similar to training a pet through positive and negative stimuli.', 'Supervised learning relies on labeled data to train models and make predictions, while unsupervised learning discovers patterns in unlabeled data, enhancing understanding and insights.', 'Machine learning enables computers to learn from data without being explicitly programmed, leading to more flexible and adaptive systems.', 'The model learns patterns from input data to generate predictions without specific programming instructions.', 'One hot encoding transforms different nationalities into vectors, with a one marking the presence of a category and zero marking absence, e.g., USA as 1000, India as 0100, Canada as 0010, and France as 0001.', 'Ordinal data like age categories and rating systems can be encoded using numerical values with inherent ordering, e.g., using a system like 12345 for rating systems.', 'Discussing the distinction between qualitative and quantitative data sets and the implications for encoding them for computer processing.', 'Details the nature of quantitative features, including discrete and continuous variables, with examples like measuring desk size and temperature.', 'Details the tasks in supervised learning, including multi-class classification with multiple discrete classes and binary classification with only two classes.']}, {'end': 2080.753, 'segs': [{'end': 1406.883, 'src': 'embed', 'start': 1377.93, 'weight': 0, 'content': [{'end': 1381.333, 'text': "And then maybe we have a bunch of emails and we're trying to create a spam filter.", 'start': 1377.93, 'duration': 3.403}, {'end': 1386.438, 'text': 'One, another example of binary classification might be spam or not spam.', 'start': 1381.974, 'duration': 4.464}, {'end': 1390.261, 'text': 'Now, what about multi-class classification?', 'start': 1387.639, 'duration': 2.622}, {'end': 1395.685, 'text': 'So, going back to our first example of the cats and dogs.', 'start': 1391.402, 'duration': 4.283}, {'end': 1397.127, 'text': 'well, we also had a lizard.', 'start': 1395.685, 'duration': 1.442}, {'end': 1398.468, 'text': 'you know, you might also have a dolphin.', 'start': 1397.127, 'duration': 1.341}, {'end': 1403.392, 'text': 'So different types of animals that might be something that falls under multi-class classification.', 'start': 1398.708, 'duration': 4.684}, {'end': 1406.883, 'text': 'Another example might be different types of fruits.', 'start': 1404.682, 'duration': 2.201}], 'summary': 'Discussed binary and multi-class classification in machine learning with examples of spam filter and animal classification.', 'duration': 28.953, 'max_score': 1377.93, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VtRLrQ3Ev-U/pics/VtRLrQ3Ev-U1377930.jpg'}, {'end': 1709.131, 'src': 'embed', 'start': 1675.157, 'weight': 2, 'content': [{'end': 1678.838, 'text': "but when we're building a model, we don't want to use all of our data.", 'start': 1675.157, 'duration': 3.681}, {'end': 1685.622, 'text': 'In fact, we want to split up our data because we want some sort of metric to see how our model will generalize.', 'start': 1679.099, 'duration': 6.523}, {'end': 1693.469, 'text': 'So we split up our chocolate bar into the training data set, the validation data set, and the testing data set.', 'start': 1686.641, 'duration': 6.828}, {'end': 1700.658, 'text': "Our training data set is what we're gonna feed into our model, and this might give us an output.", 'start': 1695.391, 'duration': 5.267}, {'end': 1709.131, 'text': "We again, we check it against the real output, and we find something called the loss, which we'll talk about in a second.", 'start': 1701.667, 'duration': 7.464}], 'summary': 'Data is split into training, validation, and testing sets to evaluate model performance.', 'duration': 33.974, 'max_score': 1675.157, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VtRLrQ3Ev-U/pics/VtRLrQ3Ev-U1675157.jpg'}, {'end': 1886.869, 'src': 'embed', 'start': 1857.95, 'weight': 3, 'content': [{'end': 1860.471, 'text': 'Okay, so there are a few different types of loss functions.', 'start': 1857.95, 'duration': 2.521}, {'end': 1865.114, 'text': "So let's put our mindset in like in terms of regression for a second.", 'start': 1860.992, 'duration': 4.122}, {'end': 1869.236, 'text': "So we're trying to predict a value that's on a continuous scale.", 'start': 1865.194, 'duration': 4.042}, {'end': 1872.618, 'text': 'So this might be the temperature tomorrow, right?', 'start': 1869.296, 'duration': 3.322}, {'end': 1880.724, 'text': "Now, if we have a bunch of different cities that we're trying to predict, then we have a bunch of different points, right?", 'start': 1873.698, 'duration': 7.026}, {'end': 1882.726, 'text': 'So this here why real?', 'start': 1880.804, 'duration': 1.922}, {'end': 1886.869, 'text': 'this is the actual value that we found in our data set.', 'start': 1882.726, 'duration': 4.143}], 'summary': 'Exploring different loss functions in regression for predicting continuous values.', 'duration': 28.919, 'max_score': 1857.95, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VtRLrQ3Ev-U/pics/VtRLrQ3Ev-U1857950.jpg'}, {'end': 2055.208, 'src': 'embed', 'start': 2030.754, 'weight': 4, 'content': [{'end': 2038.179, 'text': 'So one of the metrics of performance that we can talk about in classification specifically is accuracy.', 'start': 2030.754, 'duration': 7.425}, {'end': 2044.963, 'text': "So let's say that we have a bunch of pictures here, and we want to predict their labels.", 'start': 2039.479, 'duration': 5.484}, {'end': 2047.664, 'text': 'So here, I also have the actual value.', 'start': 2045.583, 'duration': 2.081}, {'end': 2051.126, 'text': 'So of course, this is an apple, this orange, apple, apple, etc.', 'start': 2047.684, 'duration': 3.442}, {'end': 2055.208, 'text': 'And use your imagination think that, you know, these two are slightly different from the original.', 'start': 2051.166, 'duration': 4.042}], 'summary': 'Discussing classification performance metric: accuracy.', 'duration': 24.454, 'max_score': 2030.754, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VtRLrQ3Ev-U/pics/VtRLrQ3Ev-U2030754.jpg'}], 'start': 1337.462, 'title': 'Machine learning basics', 'summary': 'Covers topics such as binary and multi-class classification, regression, model learning, and evaluation, along with concepts of data sets, model training, dataset splitting, loss functions, and evaluation metrics.', 'chapters': [{'end': 1499.191, 'start': 1337.462, 'title': 'Classification, multi-class and regression in machine learning', 'summary': 'Discusses binary classification (positive/negative) and multi-class classification (animals, fruits, plant species), along with regression for predicting continuous values, and briefly touches on model learning and evaluation.', 'duration': 161.729, 'highlights': ['The chapter discusses binary classification (positive/negative) and multi-class classification (animals, fruits, plant species), along with regression for predicting continuous values. It explains the concept of binary classification with examples of restaurant reviews, pictures of cats vs. dogs, and spam filters. It also delves into multi-class classification with examples of different types of animals, fruits, and plant species, along with regression for predicting continuous values.', 'The chapter briefly touches on model learning and evaluation. It mentions the process of making the model learn and determining its learning progress, providing a brief overview of model learning and evaluation.']}, {'end': 2080.753, 'start': 1501.551, 'title': 'Understanding data sets and model training', 'summary': 'Explains the concept of data sets, features, and model training, including the process of dataset splitting for training, validation, and testing, the use of loss functions, and the evaluation metrics such as accuracy in model performance.', 'duration': 579.202, 'highlights': ['The process of dataset splitting into training, validation, and testing data sets is crucial for model generalization, with the training set used to train the model and the validation set acting as a reality check for model performance. Dataset splitting into training, validation, and testing sets; Validation set used for reality check in model performance.', 'The use of loss functions, such as L1 and L2 loss for regression and binary cross entropy loss for classification, enables quantifying the difference between predicted and real values, with loss decreasing as performance improves. Explanation of L1 and L2 loss for regression, binary cross entropy loss for classification; Loss decreases as performance improves.', "Evaluation metric such as accuracy is used to measure the model's performance, where the accuracy of the model is determined by the number of correct predictions out of the total predictions made. Explanation of accuracy as an evaluation metric for model performance."]}], 'duration': 743.291, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VtRLrQ3Ev-U/pics/VtRLrQ3Ev-U1337462.jpg', 'highlights': ['The chapter covers binary and multi-class classification, regression, model learning, and evaluation.', 'The chapter explains binary classification with examples of restaurant reviews, pictures of cats vs. dogs, and spam filters.', 'The process of dataset splitting into training, validation, and testing data sets is crucial for model generalization.', 'Loss functions, such as L1 and L2 loss for regression and binary cross entropy loss for classification, enable quantifying the difference between predicted and real values.', "Evaluation metric such as accuracy is used to measure the model's performance."]}, {'end': 3018.289, 'segs': [{'end': 2166.443, 'src': 'embed', 'start': 2127.739, 'weight': 0, 'content': [{'end': 2132.021, 'text': "And if you do use a neural net, it's kind of like using a sledgehammer to crack an egg.", 'start': 2127.739, 'duration': 4.282}, {'end': 2133.761, 'text': "It's a little bit, you know, too much.", 'start': 2132.481, 'duration': 1.28}, {'end': 2140.454, 'text': 'There are plenty of other models that can also do classification and regression.', 'start': 2135.589, 'duration': 4.865}, {'end': 2145.799, 'text': 'And sometimes, the simpler the model, the better.', 'start': 2141.135, 'duration': 4.664}, {'end': 2156.655, 'text': "And the reason for that is because You don't want something that's so good at predicting your training data set that it's not good at generalizing.", 'start': 2146.36, 'duration': 10.295}, {'end': 2161.018, 'text': 'And often the thing with neural nets is that they are very much a black box.', 'start': 2157.015, 'duration': 4.003}, {'end': 2166.443, 'text': "The people who create the neural nets don't really know what's going on inside the network itself.", 'start': 2161.078, 'duration': 5.365}], 'summary': 'Consider simpler models to avoid overfitting, like using a neural net for classification and regression.', 'duration': 38.704, 'max_score': 2127.739, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VtRLrQ3Ev-U/pics/VtRLrQ3Ev-U2127739.jpg'}, {'end': 2345.351, 'src': 'embed', 'start': 2314.842, 'weight': 2, 'content': [{'end': 2319.946, 'text': 'And it could be the output, but this is the output of a single neuron over here.', 'start': 2314.842, 'duration': 5.104}, {'end': 2329.942, 'text': 'Okay, and then once you have a bunch of these neurons all together, they form a neural network, which kind of looks something like this.', 'start': 2322.017, 'duration': 7.925}, {'end': 2333.164, 'text': 'This is just a cool picture that I found on Wikipedia.', 'start': 2330.702, 'duration': 2.462}, {'end': 2335.965, 'text': 'All right.', 'start': 2333.184, 'duration': 2.781}, {'end': 2338.887, 'text': "so let's take a step backwards and talk about this activation function,", 'start': 2335.965, 'duration': 2.922}, {'end': 2345.351, 'text': "because I just kind of glossed over it and I didn't really tell you exactly what it is.", 'start': 2338.887, 'duration': 6.464}], 'summary': 'Neurons form a neural network, with activation functions playing a crucial role.', 'duration': 30.509, 'max_score': 2314.842, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VtRLrQ3Ev-U/pics/VtRLrQ3Ev-U2314842.jpg'}, {'end': 2834.561, 'src': 'embed', 'start': 2804.338, 'weight': 1, 'content': [{'end': 2808.502, 'text': 'Okay so then, going back to all these other weights,', 'start': 2804.338, 'duration': 4.164}, {'end': 2818.411, 'text': 'we can see how each weight in the neural net is getting updated with respect to what this gradient value is telling us.', 'start': 2808.502, 'duration': 9.909}, {'end': 2821.714, 'text': 'So basically this is how training happens in a neural net.', 'start': 2819.112, 'duration': 2.602}, {'end': 2823.636, 'text': 'We calculate out the loss.', 'start': 2822.255, 'duration': 1.381}, {'end': 2825.058, 'text': "Okay, we see there's a massive loss.", 'start': 2823.696, 'duration': 1.362}, {'end': 2834.561, 'text': 'we can calculate the gradient of that loss function with respect to each of the weight parameters in the neural network.', 'start': 2825.538, 'duration': 9.023}], 'summary': 'Neural net weights updated based on gradient for training.', 'duration': 30.223, 'max_score': 2804.338, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VtRLrQ3Ev-U/pics/VtRLrQ3Ev-U2804338.jpg'}, {'end': 2961.399, 'src': 'embed', 'start': 2934.613, 'weight': 3, 'content': [{'end': 2943.215, 'text': "And that's a lot easier than trying to go through and develop and optimize the network entirely from scratch ourselves.", 'start': 2934.613, 'duration': 8.602}, {'end': 2951.636, 'text': 'So straight from the website, TensorFlow is an open source library that helps you develop and train ml models.', 'start': 2945.076, 'duration': 6.56}, {'end': 2953.236, 'text': 'Okay, great.', 'start': 2952.596, 'duration': 0.64}, {'end': 2955.637, 'text': 'That sounds like exactly what we want.', 'start': 2953.276, 'duration': 2.361}, {'end': 2961.399, 'text': "So TensorFlow is a library that's comprised of many modules that you know, you might be able to see here.", 'start': 2956.257, 'duration': 5.142}], 'summary': 'Tensorflow is an open source library for developing and training ml models, making it easier than building a network from scratch.', 'duration': 26.786, 'max_score': 2934.613, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VtRLrQ3Ev-U/pics/VtRLrQ3Ev-U2934613.jpg'}], 'start': 2082.614, 'title': 'Neural networks', 'summary': 'Provides an overview of neural networks, including their popularity, overuse, limitations, and key components. it also explains the process of training a neural network using gradient descent, backpropagation, and the utilization of machine learning libraries like tensorflow.', 'chapters': [{'end': 2483.448, 'start': 2082.614, 'title': 'Understanding neural networks', 'summary': 'Provides an overview of neural networks, highlighting their popularity, overuse, limitations, and key components, including the structure of a neural net, the role of neurons, and the importance of activation functions in creating non-linear models.', 'duration': 400.834, 'highlights': ['Neural networks are popular but may be overused, and simpler models can sometimes be more effective. The speaker mentions that neural nets have become sometimes a little bit too popular, and they are sometimes maybe overused. There are plenty of other models that can also do classification and regression, and sometimes, the simpler the model, the better.', "Neural nets can be a black box and lack transparency compared to other machine learning models. Neural nets are often described as a black box, and it is mentioned that people who create the neural nets don't really know what's going on inside the network itself. Other types of models in machine learning might be a little bit more transparent than a neural net.", 'Understanding the structure and function of neurons is essential in comprehending a neural network. The detailed explanation of the structure of a neuron within a neural network, including the multiplication of inputs by weights, addition of bias, and the role of activation functions, is provided to help understand the functioning of a neural network.']}, {'end': 3018.289, 'start': 2484.148, 'title': 'Neural network training and implementation', 'summary': 'Explains the process of training a neural network using gradient descent and backpropagation, and the utilization of machine learning libraries like tensorflow to efficiently implement and optimize the network.', 'duration': 534.141, 'highlights': ["The process of training a neural network involves calculating the loss, determining the gradient of the loss function with respect to the weights, and adjusting the weights using gradient descent and backpropagation. The training process involves calculating the loss, determining the gradient of the loss function with respect to the weights, and adjusting the weights using gradient descent and backpropagation to optimize the neural network's performance.", 'The use of machine learning libraries such as TensorFlow and its modules like Keras enables efficient development, training, and optimization of neural network models. Machine learning libraries like TensorFlow and its modules like Keras facilitate the efficient development, training, and optimization of neural network models, allowing for a focus on maximizing efficiency and avoiding the need to code from scratch.', 'TensorFlow, an open source library, provides tools for importing and maintaining consistent and usable data, as well as various optimizers for model creation and optimization. TensorFlow, an open source library, offers tools for data import and consistency maintenance, along with various optimizers for model creation and optimization, contributing to maximizing efficiency and fine-tuning of models.']}], 'duration': 935.675, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VtRLrQ3Ev-U/pics/VtRLrQ3Ev-U2082614.jpg', 'highlights': ['Neural nets can be a black box and lack transparency compared to other machine learning models.', 'The process of training a neural network involves calculating the loss, determining the gradient of the loss function with respect to the weights, and adjusting the weights using gradient descent and backpropagation.', 'Understanding the structure and function of neurons is essential in comprehending a neural network.', 'Machine learning libraries like TensorFlow and its modules like Keras facilitate the efficient development, training, and optimization of neural network models.', 'Neural networks are popular but may be overused, and simpler models can sometimes be more effective.']}, {'end': 3563.926, 'segs': [{'end': 3046.821, 'src': 'embed', 'start': 3019.875, 'weight': 0, 'content': [{'end': 3027.117, 'text': "All right, so now let's move over to our notebook and I'm going to show you guys how to actually implement a neural net using TensorFlow.", 'start': 3019.875, 'duration': 7.242}, {'end': 3031.958, 'text': 'Just straightforward, feed forward, no pun intended, neural network.', 'start': 3027.217, 'duration': 4.741}, {'end': 3039.819, 'text': "Now that we've learned a little bit about neural nets, about TensorFlow, let's try to actually implement a neural net using TensorFlow.", 'start': 3032.698, 'duration': 7.121}, {'end': 3046.821, 'text': "So let me go back to this collab tab and let's actually create a new notebook.", 'start': 3040.379, 'duration': 6.442}], 'summary': 'Demonstration of implementing a neural net using tensorflow in a new notebook.', 'duration': 26.946, 'max_score': 3019.875, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VtRLrQ3Ev-U/pics/VtRLrQ3Ev-U3019875.jpg'}, {'end': 3129.982, 'src': 'embed', 'start': 3100.722, 'weight': 2, 'content': [{'end': 3105.726, 'text': "so this data set that we're using here in diabetes.csv.", 'start': 3100.722, 'duration': 5.004}, {'end': 3112.89, 'text': 'this is a data set that was originally from the National Institute of Diabetes and Digestive and Kidney Diseases.', 'start': 3105.726, 'duration': 7.164}, {'end': 3122.117, 'text': 'And in this data set, all of these patients are females at least 21 years old of Pima Indian heritage.', 'start': 3113.831, 'duration': 8.286}, {'end': 3129.982, 'text': 'And this data set has a bunch of different features, but the final feature is whether or not the patient has diabetes.', 'start': 3122.837, 'duration': 7.145}], 'summary': 'Analysis of diabetes.csv dataset from pima indian females over 21 years old', 'duration': 29.26, 'max_score': 3100.722, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VtRLrQ3Ev-U/pics/VtRLrQ3Ev-U3100722.jpg'}, {'end': 3208.988, 'src': 'embed', 'start': 3183.408, 'weight': 3, 'content': [{'end': 3190.132, 'text': "But for this purpose, I think it's a lot easier to visualize things in, I guess, a visual way.", 'start': 3183.408, 'duration': 6.724}, {'end': 3199.598, 'text': "How else would you visualize things? So here what I'm going to do is I'm going to plot each of these as a histogram.", 'start': 3190.752, 'duration': 8.846}, {'end': 3205.406, 'text': 'So each value in the feature as a histogram and then compare it to the outcome.', 'start': 3199.638, 'duration': 5.768}, {'end': 3208.988, 'text': "So let's try to do that using a for loop.", 'start': 3205.706, 'duration': 3.282}], 'summary': 'Visualize data through histograms for feature comparison.', 'duration': 25.58, 'max_score': 3183.408, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VtRLrQ3Ev-U/pics/VtRLrQ3Ev-U3183408.jpg'}, {'end': 3476.308, 'src': 'embed', 'start': 3452.283, 'weight': 4, 'content': [{'end': 3458.364, 'text': 'so this is saying how many patients are diabetes positive and how many are diabetes negative?', 'start': 3452.283, 'duration': 6.081}, {'end': 3460.025, 'text': 'we see that we actually have two different values.', 'start': 3458.364, 'duration': 1.661}, {'end': 3464.706, 'text': 'one of them is only 268 positive patients and then 500 negative patients.', 'start': 3460.025, 'duration': 4.681}, {'end': 3468.727, 'text': 'So what we actually want to do is normalize this,', 'start': 3465.686, 'duration': 3.041}, {'end': 3476.308, 'text': 'which means that we want to compare these histogram distributions to how many actual values there are in that data set.', 'start': 3468.727, 'duration': 7.581}], 'summary': '268 patients are diabetes positive, 500 are negative. normalizing histogram distributions for accurate comparison.', 'duration': 24.025, 'max_score': 3452.283, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VtRLrQ3Ev-U/pics/VtRLrQ3Ev-U3452283.jpg'}], 'start': 3019.875, 'title': 'Implementing neural net with tensorflow and analyzing diabetes dataset', 'summary': 'Covers implementing a feed forward neural network using tensorflow and analyzing the diabetes dataset from the national institute of diabetes and digestive and kidney diseases, focusing on correlation between features and likelihood of having diabetes, with visualizations and normalization.', 'chapters': [{'end': 3079.093, 'start': 3019.875, 'title': 'Implementing neural net with tensorflow', 'summary': 'Demonstrates the process of implementing a feed forward neural network using tensorflow, including creating a new notebook and importing the dataset.', 'duration': 59.218, 'highlights': ['The chapter demonstrates the process of implementing a feed forward neural network using TensorFlow It provides a practical tutorial on implementing a feed forward neural network using TensorFlow, emphasizing the practical application of the theoretical knowledge.', 'Creating a new notebook and importing the dataset The process involves creating a new notebook and importing the dataset, showcasing the practical steps involved in setting up the neural network implementation.']}, {'end': 3563.926, 'start': 3079.433, 'title': 'Analyzing diabetes dataset', 'summary': 'Explores the diabetes dataset from the national institute of diabetes and digestive and kidney diseases, focusing on analyzing the correlation between different features and the likelihood of having diabetes, with visualizations and normalization to ensure fair comparison.', 'duration': 484.493, 'highlights': ['The dataset is from the National Institute of Diabetes and Digestive and Kidney Diseases, consisting of female patients at least 21 years old of Pima Indian heritage. The dataset includes female patients at least 21 years old of Pima Indian heritage, providing specific demographic details.', 'Visualization techniques like histograms are used to compare different features with the outcome of diabetes, revealing potential correlations. Histograms are utilized to visually compare different features with the outcome of diabetes, aiding in identifying potential correlations.', 'Normalization is applied to account for the differing number of diabetes-positive and diabetes-negative patients, ensuring a fair comparison in the analysis. Normalization is applied to account for the differing number of diabetes-positive and diabetes-negative patients, ensuring a fair comparison in the analysis.']}], 'duration': 544.051, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VtRLrQ3Ev-U/pics/VtRLrQ3Ev-U3019875.jpg', 'highlights': ['The chapter demonstrates the process of implementing a feed forward neural network using TensorFlow, emphasizing practical application.', 'Creating a new notebook and importing the dataset showcases practical steps in setting up the neural network implementation.', 'The dataset includes female patients at least 21 years old of Pima Indian heritage, providing specific demographic details.', 'Histograms are utilized to visually compare different features with the outcome of diabetes, aiding in identifying potential correlations.', 'Normalization is applied to account for the differing number of diabetes-positive and diabetes-negative patients, ensuring a fair comparison in the analysis.']}, {'end': 4238.23, 'segs': [{'end': 3588.031, 'src': 'embed', 'start': 3564.066, 'weight': 0, 'content': [{'end': 3570.87, 'text': 'And this is where the power of machine learning comes into play is that we can actually learn whether or not, or predict whether or not,', 'start': 3564.066, 'duration': 6.804}, {'end': 3575.292, 'text': 'somebody has diabetes or not, based on all of these features altogether.', 'start': 3570.87, 'duration': 4.422}, {'end': 3588.031, 'text': "Okay So, Now that we have this, what we're going to do is split this into our X and Y values.", 'start': 3575.312, 'duration': 12.719}], 'summary': 'Machine learning can predict diabetes based on multiple features.', 'duration': 23.965, 'max_score': 3564.066, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VtRLrQ3Ev-U/pics/VtRLrQ3Ev-U3564066.jpg'}, {'end': 3902.026, 'src': 'embed', 'start': 3872.121, 'weight': 3, 'content': [{'end': 3877.342, 'text': "But densely connected is just, you know, okay, it's a layer that's deeply connected with its preceding layer.", 'start': 3872.121, 'duration': 5.221}, {'end': 3888.745, 'text': 'So it just means that every single neuron here is receiving input from every single neuron that it sees from the past.', 'start': 3878.122, 'duration': 10.623}, {'end': 3896.482, 'text': 'All right, going back to here, this just means that this is a layer of 16 neurons that are densely connected.', 'start': 3890.478, 'duration': 6.004}, {'end': 3902.026, 'text': 'So if we go back to here, it means that this layer here is comprised of 16 different neurons.', 'start': 3896.522, 'duration': 5.504}], 'summary': 'Densely connected layer with 16 neurons receiving input from every past neuron.', 'duration': 29.905, 'max_score': 3872.121, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VtRLrQ3Ev-U/pics/VtRLrQ3Ev-U3872121.jpg'}, {'end': 4127.984, 'src': 'embed', 'start': 4056.485, 'weight': 2, 'content': [{'end': 4069.933, 'text': "and then the final thing that we're going to do is add a metric for accuracy, and the reason why we're doing that is because we want to know you know,", 'start': 4056.485, 'duration': 13.448}, {'end': 4075.155, 'text': 'how many do we actually get right?', 'start': 4069.933, 'duration': 5.222}, {'end': 4079.057, 'text': 'so enter, we now have a compiled model.', 'start': 4075.155, 'duration': 3.902}, {'end': 4083.519, 'text': 'we have a neural net that we can actually feed data to and we can train.', 'start': 4079.057, 'duration': 4.462}, {'end': 4089.463, 'text': "But before we do that, let's actually see how well this might perform on.", 'start': 4084.517, 'duration': 4.946}, {'end': 4091.606, 'text': 'you know our training data and our validation data.', 'start': 4089.463, 'duration': 2.143}, {'end': 4096.292, 'text': "What we're going to call is model dot evaluate.", 'start': 4093.649, 'duration': 2.643}, {'end': 4103.301, 'text': "And here, let's pass an x train and y train and see what happens.", 'start': 4097.173, 'duration': 6.128}, {'end': 4114.334, 'text': "Okay, so here we're getting a loss of 16 and an accuracy of 0.35, which is around like 35%.", 'start': 4104.727, 'duration': 9.607}, {'end': 4115.736, 'text': "So that's pretty bad.", 'start': 4114.334, 'duration': 1.402}, {'end': 4121.26, 'text': "What about instead of train, let's do validation.", 'start': 4116.696, 'duration': 4.564}, {'end': 4127.984, 'text': "Okay, it's around the same a loss of 11 and accuracy of 35%.", 'start': 4122.781, 'duration': 5.203}], 'summary': 'Model evaluation resulted in a 35% accuracy for both training and validation data, indicating poor performance.', 'duration': 71.499, 'max_score': 4056.485, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VtRLrQ3Ev-U/pics/VtRLrQ3Ev-U4056485.jpg'}], 'start': 3564.066, 'title': 'Using machine learning for diabetes prediction', 'summary': "Explains the process of using machine learning to predict diabetes, splitting the dataset into training, validation, and test subsets with 60% for training and 20% each for validation and testing. it introduces the use of tensorflow's keras and discusses building a neural net model with 16 densely connected neurons and a sigmoid activation function for binary classification. the training process involves defining optimizers, loss functions, adding accuracy metrics, evaluating model performance, and training the neural net with batch size and epochs, resulting in an initial accuracy of 35% and successful validation loss and accuracy improvements.", 'chapters': [{'end': 3804.993, 'start': 3564.066, 'title': 'Machine learning for diabetes prediction', 'summary': "Explains the process of using machine learning to predict diabetes by splitting the dataset into training, validation, and test subsets using sklearn's train_test_split function, with 60% used for training and 20% each for validation and testing. it also introduces the use of tensorflow's keras for building the model.", 'duration': 240.927, 'highlights': ['The chapter introduces the process of using machine learning to predict whether someone has diabetes based on various features, showcasing the power of machine learning in healthcare.', "It explains the use of sklearn's train_test_split function to split the dataset into training and test subsets, using 60% for training and 20% each for validation and testing.", "It introduces the use of TensorFlow's Keras for building the model to predict diabetes, showcasing the application of neural networks in healthcare.", 'The speaker demonstrates the process of splitting the dataset into training, validation, and test subsets, emphasizing the importance of maintaining consistent splits using the random_state parameter.']}, {'end': 3978.468, 'start': 3805.393, 'title': 'Building neural net model', 'summary': 'Discusses using an api to build a neural net model with 16 densely connected neurons and a sigmoid activation function for binary classification, making it easy to create and compile the model.', 'duration': 173.075, 'highlights': ['The API allows for easily building neural net models. The API simplifies the process of building neural net models.', 'The neural net model consists of a layer with 16 densely connected neurons. The model includes a layer with 16 densely connected neurons.', 'The activation function used for binary classification is sigmoid, which maps input to a probability of belonging to a single class. The sigmoid activation function is used for binary classification, mapping input to a probability of belonging to a single class.', 'Compiling the model is the final step in the process. The final step is compiling the model.']}, {'end': 4238.23, 'start': 3979.369, 'title': 'Neural net training process', 'summary': 'Discusses the process of training a neural network using tensorflow, defining optimizers, loss functions, adding accuracy metrics, evaluating model performance, and training the neural net with batch size and epochs, resulting in an initial accuracy of 35% and successful validation loss and accuracy improvements.', 'duration': 258.861, 'highlights': ['The process of training a neural network involves defining optimizers, loss functions, and adding accuracy metrics to the model, as well as evaluating its performance on training and validation data.', 'The initial model evaluation on training data shows a loss of 16 and an accuracy of 35%, while on validation data, it shows a loss of 11 and an accuracy of 35%.', 'The training of the neural network involves using a batch size and epochs, with the validation data used to measure validation loss and accuracy after each epoch.']}], 'duration': 674.164, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VtRLrQ3Ev-U/pics/VtRLrQ3Ev-U3564066.jpg', 'highlights': ['The chapter introduces the process of using machine learning to predict whether someone has diabetes, showcasing the power of machine learning in healthcare.', "It explains the use of TensorFlow's Keras for building the model to predict diabetes, showcasing the application of neural networks in healthcare.", 'The process of training a neural network involves defining optimizers, loss functions, and adding accuracy metrics to the model, as well as evaluating its performance on training and validation data.', 'The neural net model consists of a layer with 16 densely connected neurons. The model includes a layer with 16 densely connected neurons.', 'The initial model evaluation on training data shows a loss of 16 and an accuracy of 35%, while on validation data, it shows a loss of 11 and an accuracy of 35%.']}, {'end': 5165.401, 'segs': [{'end': 4521.247, 'src': 'embed', 'start': 4493.272, 'weight': 1, 'content': [{'end': 4499.235, 'text': 'So, if we can see now, Like most of these, so what standard scaler is doing?', 'start': 4493.272, 'duration': 5.963}, {'end': 4508.28, 'text': "is it's actually mapping our values to a normal distribution and trying to calculate like how far off our values are from the norm?", 'start': 4499.235, 'duration': 9.045}, {'end': 4513.222, 'text': 'So here, we can see that glucose levels now range.', 'start': 4509.5, 'duration': 3.722}, {'end': 4521.247, 'text': "I mean, it looks like it's fairly centered around zero or negative one, but all of these are now normalized.", 'start': 4513.883, 'duration': 7.364}], 'summary': 'Using standard scaler to normalize glucose levels and map values to a normal distribution.', 'duration': 27.975, 'max_score': 4493.272, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VtRLrQ3Ev-U/pics/VtRLrQ3Ev-U4493272.jpg'}, {'end': 4826.043, 'src': 'embed', 'start': 4791.521, 'weight': 0, 'content': [{'end': 4795.203, 'text': 'And our accuracy seems to be increasing, which is also good.', 'start': 4791.521, 'duration': 3.682}, {'end': 4797.725, 'text': "And let's check our validation.", 'start': 4796.284, 'duration': 1.441}, {'end': 4801.807, 'text': 'Because remember, we need to see how this would generalize to unseen data.', 'start': 4797.965, 'duration': 3.842}, {'end': 4808.631, 'text': 'Our validation loss, great seems to be decreasing validation accuracy seems to be increasing.', 'start': 4802.707, 'duration': 5.924}, {'end': 4813.673, 'text': 'And now our validation accuracy is somewhere closer to 77%, which is a significant improvement.', 'start': 4808.691, 'duration': 4.982}, {'end': 4826.043, 'text': "All right, now at the very, very end, what we're going to do is we're going to evaluate this model on our test dataset.", 'start': 4818.7, 'duration': 7.343}], 'summary': 'Validation accuracy increased to 77%, a significant improvement.', 'duration': 34.522, 'max_score': 4791.521, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VtRLrQ3Ev-U/pics/VtRLrQ3Ev-U4791521.jpg'}, {'end': 5015.733, 'src': 'embed', 'start': 4986.681, 'weight': 2, 'content': [{'end': 4989.922, 'text': 'So this neural net at whenever.', 'start': 4986.681, 'duration': 3.241}, {'end': 4999.066, 'text': 'you know when we feed in X2, so X at T2, we actually have some information that we remember about X at T0 and X at T1..', 'start': 4989.922, 'duration': 9.144}, {'end': 5002.268, 'text': 'So that makes a recurrent neural net very powerful.', 'start': 4999.827, 'duration': 2.441}, {'end': 5006.551, 'text': 'Ta-da, this part acts as a memory.', 'start': 5003.991, 'duration': 2.56}, {'end': 5015.733, 'text': 'And now instead of just straightforward backprop, we have to use something called backpropagation through time in order to adjust these weights.', 'start': 5008.252, 'duration': 7.481}], 'summary': 'Recurrent neural nets utilize memory to process data over time, requiring backpropagation through time for weight adjustments.', 'duration': 29.052, 'max_score': 4986.681, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VtRLrQ3Ev-U/pics/VtRLrQ3Ev-U4986681.jpg'}, {'end': 5130.07, 'src': 'embed', 'start': 5095.62, 'weight': 3, 'content': [{'end': 5098.502, 'text': "On the other hand, there's also such thing as vanishing gradients.", 'start': 5095.62, 'duration': 2.882}, {'end': 5103.146, 'text': 'So here, our gradients get closer and closer and closer to zero.', 'start': 5099.103, 'duration': 4.043}, {'end': 5109.291, 'text': 'And so at this point, our model stops updating, and then it becomes also incapable of learning.', 'start': 5103.746, 'duration': 5.545}, {'end': 5116.616, 'text': "So some really smart people have studied these problems, and they've decided, okay, here are some different ways that we can overcome this problem.", 'start': 5110.271, 'duration': 6.345}, {'end': 5120.039, 'text': 'There are a few things that you can do with the activation function.', 'start': 5117.597, 'duration': 2.442}, {'end': 5122.547, 'text': "But I'll let you look that up on your own time.", 'start': 5120.506, 'duration': 2.041}, {'end': 5130.07, 'text': "Instead, I'm going to talk about different sorts of cells or neurons that people have come up with in order to combat this.", 'start': 5123.247, 'duration': 6.823}], 'summary': 'Vanishing gradients can cause model to stop updating and become incapable of learning. solutions include using different activation functions and neuron types.', 'duration': 34.45, 'max_score': 5095.62, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VtRLrQ3Ev-U/pics/VtRLrQ3Ev-U5095620.jpg'}], 'start': 4239.537, 'title': 'Data preprocessing and neural networks', 'summary': 'Covers data scaling for standardized range, reshaping and normalizing data for neural network training, balancing data with random over sampler resulting in 77% accuracy improvement, and recurrent neural networks with solutions for vanishing and exploding gradients.', 'chapters': [{'end': 4384.721, 'start': 4239.537, 'title': 'Data scaling for standardized range', 'summary': 'Discusses the issues with varying feature ranges in a data set and the process of scaling the data using the standard scaler from the sklearn pre-processing package, resulting in a more standardized range for the features, aiding in better analysis and results.', 'duration': 145.184, 'highlights': ['The insulin range is from zero to 800, skin thickness from zero to 100, and BMI from zero to 2.5, causing variation in feature ranges. The insulin range is from zero to 800, skin thickness from zero to 100, and BMI from zero to 2.5, causing variation in feature ranges.', 'Scaling the data using standard scaler from sklearn pre-processing package results in a more standardized range for the features, aiding in better analysis and results. Scaling the data using standard scaler from sklearn pre-processing package results in a more standardized range for the features, aiding in better analysis and results.', 'Using standard scaler to transform the data to a more standardized range, resulting in values closer to around one to negative one. Using standard scaler to transform the data to a more standardized range, resulting in values closer to around one to negative one.', 'Creation of a data frame to plot the transformed values and observe the changes. Creation of a data frame to plot the transformed values and observe the changes.']}, {'end': 4587.422, 'start': 4385.362, 'title': 'Reshaping and normalizing data for neural network training', 'summary': 'Discusses reshaping the data for a neural network by using a horizontal stack and normalizing the data using standard scaler, resulting in a visualization of the normalized values and addressing imbalanced data for effective neural net training.', 'duration': 202.06, 'highlights': ['Using a horizontal stack to reshape the data by transforming the variables x and y, ensuring compatibility for further processing. Demonstrates the process of reshaping the two-dimensional matrix x and the one-dimensional matrix y using a horizontal stack for compatibility in further processing.', 'Applying standard scaler to normalize the data, visualizing the normalized values, and observing the glucose levels centered around zero or negative one. Describes the application of standard scaler to normalize the data, resulting in visualization of the normalized values and observation of glucose levels centered around zero or negative one.', 'Addressing the issue of imbalanced data by considering the difference in the number of non-diabetes patients and diabetes positive patients to ensure effective neural network training. Discusses the consideration of imbalanced data, specifically the difference in the number of non-diabetes patients and diabetes positive patients, to ensure effective neural network training.']}, {'end': 4842.59, 'start': 4588.462, 'title': 'Balancing data with random over sampler', 'summary': 'Discusses using random over sampler to balance the data, resulting in an improved accuracy of 77% during model evaluation, a significant improvement from the original accuracy of 50%.', 'duration': 254.128, 'highlights': ['Using random over sampler to balance the data resulted in a dataset with 500 outcomes of 1 and 500 outcomes of 0, indicating a balanced dataset.', 'The accuracy of the model improved to around 77% after balancing the dataset, a significant improvement from the original accuracy of approximately 50%.', 'By using random over sampler, the validation accuracy increased to around 77%, showcasing a significant improvement in model performance.']}, {'end': 5165.401, 'start': 4843.635, 'title': 'Recurrent neural networks', 'summary': 'Explains the concept of recurrent neural networks, highlighting their ability to process sequential data and the challenges of vanishing and exploding gradients, as well as introducing solutions such as gated recurrent units and long short-term memory units.', 'duration': 321.766, 'highlights': ['Recurrent neural networks are designed to process sequential data, such as stock prices, word sequences, or temperature values, which feedforward neural networks struggle to handle effectively. It explains the limitations of feedforward neural networks in processing sequential data and introduces the need for recurrent neural networks to handle such data.', 'Recurrent neural networks create a memory effect by considering previous calculations at each time step, making them powerful for sequential data processing. It describes how recurrent neural networks create a memory effect by considering previous calculations at each time step, enhancing their ability to process sequential data effectively.', 'The challenges of vanishing and exploding gradients in recurrent neural networks can lead to instability and hinder the learning capability of the model. It discusses the challenges of vanishing and exploding gradients in recurrent neural networks, which can lead to instability and hinder the learning capability of the model.', 'Solutions to the vanishing and exploding gradients problem include using different activation functions and implementing specialized neurons like gated recurrent units and long short-term memory units. It introduces solutions to the vanishing and exploding gradients problem, such as using different activation functions and specialized neurons like gated recurrent units and long short-term memory units.']}], 'duration': 925.864, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VtRLrQ3Ev-U/pics/VtRLrQ3Ev-U4239537.jpg', 'highlights': ['Balancing the data with random over sampler resulted in a 77% accuracy improvement.', 'Using standard scaler to transform the data to a more standardized range, resulting in values closer to around one to negative one.', 'Recurrent neural networks create a memory effect by considering previous calculations at each time step, making them powerful for sequential data processing.', 'Solutions to the vanishing and exploding gradients problem include using different activation functions and implementing specialized neurons like gated recurrent units and long short-term memory units.']}, {'end': 6849.555, 'segs': [{'end': 5241.404, 'src': 'embed', 'start': 5182.726, 'weight': 0, 'content': [{'end': 5188.608, 'text': "so now that we've touched upon some of the more theoretical aspects of neural nets and machine learning,", 'start': 5182.726, 'duration': 5.882}, {'end': 5196.592, 'text': "let's walk through a different TensorFlow example with text classification and try to see if we can classify some wine reviews.", 'start': 5188.608, 'duration': 7.984}, {'end': 5198.492, 'text': "So let's get started on that.", 'start': 5197.212, 'duration': 1.28}, {'end': 5205.235, 'text': "Let's go back to our Colab notebook where we were studying the wine reviews and let's continue on with that.", 'start': 5198.813, 'duration': 6.422}, {'end': 5207.536, 'text': 'So here I have that notebook open.', 'start': 5205.815, 'duration': 1.721}, {'end': 5211.678, 'text': 'This is the code that we typed at the very beginning of this class.', 'start': 5207.596, 'duration': 4.082}, {'end': 5216.799, 'text': "So here we have our wine reviews and we're actually we actually need to restart.", 'start': 5212.618, 'duration': 4.181}, {'end': 5222.2, 'text': 'We need to rerun some cells and we need to import our data.', 'start': 5217.819, 'duration': 4.381}, {'end': 5223.96, 'text': 'So let me do that over here.', 'start': 5222.32, 'duration': 1.64}, {'end': 5233.302, 'text': "OK, so we see that it's imported.", 'start': 5223.98, 'duration': 9.322}, {'end': 5235.683, 'text': "Let's rerun everything.", 'start': 5234.443, 'duration': 1.24}, {'end': 5241.404, 'text': 'All right.', 'start': 5235.703, 'duration': 5.701}], 'summary': 'Walkthrough of a tensorflow example with text classification using wine reviews.', 'duration': 58.678, 'max_score': 5182.726, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VtRLrQ3Ev-U/pics/VtRLrQ3Ev-U5182726.jpg'}, {'end': 5435.76, 'src': 'embed', 'start': 5409.099, 'weight': 8, 'content': [{'end': 5415.964, 'text': "So what that's going to look like is I want it to be 60% for the training, 20% for the validation, 20% for the test.", 'start': 5409.099, 'duration': 6.865}, {'end': 5424.791, 'text': 'And actually in a data frame, this size, you could even go like 80% for training, 10% for validation, 10% for tests.', 'start': 5416.385, 'duration': 8.406}, {'end': 5428.554, 'text': "And the reason is just because there's so much data that like.", 'start': 5425.492, 'duration': 3.062}, {'end': 5435.76, 'text': "even with your validation and test sets, even if you're using only 10%, that's still enough data to kind of see how it generalizes.", 'start': 5428.554, 'duration': 7.206}], 'summary': 'Training: 60%, validation: 20%, test: 20% of data.', 'duration': 26.661, 'max_score': 5409.099, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VtRLrQ3Ev-U/pics/VtRLrQ3Ev-U5409099.jpg'}, {'end': 5724.693, 'src': 'embed', 'start': 5698.47, 'weight': 7, 'content': [{'end': 5704.896, 'text': 'So what TensorFlow hub is, is TensorFlow hub is a repository of train machine learning models.', 'start': 5698.47, 'duration': 6.426}, {'end': 5711.581, 'text': 'So basically, these are models that are ready to use, they just need some fine tuning.', 'start': 5705.496, 'duration': 6.085}, {'end': 5716.706, 'text': 'And we can actually use one of these to help us in our process of text classification.', 'start': 5711.941, 'duration': 4.765}, {'end': 5724.693, 'text': "So recall that computers don't really understand text that well, right, like computers understand numbers really well.", 'start': 5717.346, 'duration': 7.347}], 'summary': "Tensorflow hub offers pre-trained ml models for text classification, aiding computers' understanding of text.", 'duration': 26.223, 'max_score': 5698.47, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VtRLrQ3Ev-U/pics/VtRLrQ3Ev-U5698470.jpg'}, {'end': 6077.1, 'src': 'embed', 'start': 6048.27, 'weight': 6, 'content': [{'end': 6058.167, 'text': "Okay, so I'm going to let you guys train your model, you can pause the video really quickly, but I'll be back once our models are done training.", 'start': 6048.27, 'duration': 9.897}, {'end': 6063.911, 'text': "Okay, so our models finished training, let's take a look at the results.", 'start': 6060.629, 'duration': 3.282}, {'end': 6070.956, 'text': 'So here, it seems like our loss is steadily decreasing, which is a good sign.', 'start': 6064.832, 'duration': 6.124}, {'end': 6075.279, 'text': 'And our accuracy seems to be increasing almost to 90%.', 'start': 6071.696, 'duration': 3.583}, {'end': 6077.1, 'text': "Okay, that's also a really good sign.", 'start': 6075.279, 'duration': 1.821}], 'summary': 'Model training completed with 90% accuracy.', 'duration': 28.83, 'max_score': 6048.27, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VtRLrQ3Ev-U/pics/VtRLrQ3Ev-U6048270.jpg'}, {'end': 6279.546, 'src': 'embed', 'start': 6224.56, 'weight': 2, 'content': [{'end': 6225.761, 'text': "And let's look at this again.", 'start': 6224.56, 'duration': 1.201}, {'end': 6235.349, 'text': 'Okay, so it looks like our training loss actually decreases very well, whereas our validation loss seems to decrease and then start to go up again.', 'start': 6225.781, 'duration': 9.568}, {'end': 6245.918, 'text': "So basically, our model is incapable of generalizing because we've trained it so much on our data that we're feeding it.", 'start': 6236.51, 'duration': 9.408}, {'end': 6251.967, 'text': 'So how do we fix this? One way would be to add something called dropout.', 'start': 6246.839, 'duration': 5.128}, {'end': 6258.152, 'text': "And dropout just means that every once in a while you select a few nodes that aren't really working.", 'start': 6252.707, 'duration': 5.445}, {'end': 6267.761, 'text': 'So if I do this, I can add a few layers like this in here.', 'start': 6258.793, 'duration': 8.968}, {'end': 6271.304, 'text': 'And that should actually improve my training.', 'start': 6268.481, 'duration': 2.823}, {'end': 6279.546, 'text': 'So the reason why this works is that every single time, the model has to basically go through these obstacles,', 'start': 6273.021, 'duration': 6.525}], 'summary': 'Model overfitting detected, considering adding dropout to improve training.', 'duration': 54.986, 'max_score': 6224.56, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VtRLrQ3Ev-U/pics/VtRLrQ3Ev-U6224560.jpg'}, {'end': 6379.145, 'src': 'embed', 'start': 6343.172, 'weight': 1, 'content': [{'end': 6348.854, 'text': 'But this is probably a much better generalizable model than our previous one that we had trained.', 'start': 6343.172, 'duration': 5.682}, {'end': 6358.238, 'text': 'So finally, what we can do is we can evaluate this on our test data in order to get our final results.', 'start': 6349.695, 'duration': 8.543}, {'end': 6361.913, 'text': 'So sweet, look at that.', 'start': 6360.751, 'duration': 1.162}, {'end': 6364.978, 'text': 'Our loss seems to be around 0.38 and our accuracy is around 83%.', 'start': 6362.033, 'duration': 2.945}, {'end': 6366.541, 'text': 'So that seems to be really good.', 'start': 6364.978, 'duration': 1.563}, {'end': 6379.145, 'text': 'Awesome Alright, so up until this point, we just created a neural network that we saw would help us with text classification.', 'start': 6370.743, 'duration': 8.402}], 'summary': 'A neural network achieved 83% accuracy in text classification.', 'duration': 35.973, 'max_score': 6343.172, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VtRLrQ3Ev-U/pics/VtRLrQ3Ev-U6343172.jpg'}], 'start': 5165.941, 'title': 'Text classification in tensorflow', 'summary': 'Covers theoretical aspects of neural nets and machine learning, introduces advanced topics in tensorflow, demonstrates text classification using wine reviews data, explains data frame splitting and text embedding, building and training a neural network model achieving an accuracy of around 83%, and implementing lstm for text classification achieving an 84% accuracy on the test data.', 'chapters': [{'end': 5241.404, 'start': 5165.941, 'title': 'Text classification with tensorflow', 'summary': 'Discusses theoretical aspects of neural nets and machine learning, introduces advanced topics in tensorflow, and demonstrates text classification using wine reviews data in a colab notebook.', 'duration': 75.463, 'highlights': ['The chapter covers theoretical aspects of neural nets and machine learning, introducing advanced topics in TensorFlow.', 'It demonstrates text classification using wine reviews data in a Colab notebook, requiring the restart and rerun of some cells and data import.', 'The chapter emphasizes the existence of advanced topics in neural nets and machine learning, creating awareness among the audience.']}, {'end': 5863.729, 'start': 5243.151, 'title': 'Data frame splitting and text embedding', 'summary': 'Explains how to split a data frame into training, validation, and test sets using different percentages, and demonstrates the process of transforming text data into numerical format using a pre-trained text embedding model, resulting in a vector of numbers for each sentence in the dataset.', 'duration': 620.578, 'highlights': ['Data Frame Splitting The process of splitting a data frame into training, validation, and test sets is demonstrated using different percentages, emphasizing the flexibility in splitting data to suit specific needs.', 'Text Embedding Model The utilization of a pre-trained text embedding model from TensorFlow hub is explained, highlighting its role in transforming text data into numerical format by projecting sentences into a length of 50 vector containing only numbers.']}, {'end': 6366.541, 'start': 5864.89, 'title': 'Building and training neural network model', 'summary': 'Demonstrates building and training a neural network model using keras, achieving an accuracy of around 83% and addressing overfitting by incorporating dropout layers.', 'duration': 501.651, 'highlights': ['The model is trained using Keras and achieves an accuracy of around 83%, indicating a successful training process.', 'The concept of overfitting is addressed, where the model learns to predict the training data well but generalizes poorly, leading to the incorporation of dropout layers to improve generalization.', 'The process of adding dropout layers is explained, where randomly selected nodes are not utilized to introduce randomness and aid the model in generalizing.', 'The need to stop training earlier is mentioned as a strategy to avoid overfitting, with an example of using five epochs for training.', 'The results of evaluating the model on test data show a loss of around 0.38 and an accuracy of around 83%, indicating a successful and generalizable model.']}, {'end': 6849.555, 'start': 6370.743, 'title': 'Implementing lstm for text classification', 'summary': "Introduces the process of implementing a lstm model for text classification, achieving a 84% accuracy on the test data and emphasizing the control over the model's architecture and performance.", 'duration': 478.812, 'highlights': ['The model achieves an 84% accuracy on the test data, showcasing its effectiveness in text classification.', "The training process demonstrates a decreasing loss and increasing accuracy, evidencing the model's learning and performance improvement.", "The tutorial emphasizes the control and customization over the model's architecture, particularly through the use of LSTM and embedding layers to vectorize the text for comprehension by the neural network.", "The tutorial concludes by highlighting the gained knowledge in implementing a feed forward neural network and using TensorFlow for text classification, while achieving success in determining the wine review's tier based on the review content."]}], 'duration': 1683.614, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VtRLrQ3Ev-U/pics/VtRLrQ3Ev-U5165941.jpg', 'highlights': ["The tutorial concludes by highlighting the gained knowledge in implementing a feed forward neural network and using TensorFlow for text classification, while achieving success in determining the wine review's tier based on the review content.", 'The model achieves an 84% accuracy on the test data, showcasing its effectiveness in text classification.', 'The process of adding dropout layers is explained, where randomly selected nodes are not utilized to introduce randomness and aid the model in generalizing.', 'The concept of overfitting is addressed, where the model learns to predict the training data well but generalizes poorly, leading to the incorporation of dropout layers to improve generalization.', 'The model is trained using Keras and achieves an accuracy of around 83%, indicating a successful training process.', 'The chapter covers theoretical aspects of neural nets and machine learning, introducing advanced topics in TensorFlow.', "The training process demonstrates a decreasing loss and increasing accuracy, evidencing the model's learning and performance improvement.", 'The utilization of a pre-trained text embedding model from TensorFlow hub is explained, highlighting its role in transforming text data into numerical format by projecting sentences into a length of 50 vector containing only numbers.', 'The process of splitting a data frame into training, validation, and test sets is demonstrated using different percentages, emphasizing the flexibility in splitting data to suit specific needs.', 'It demonstrates text classification using wine reviews data in a Colab notebook, requiring the restart and rerun of some cells and data import.']}], 'highlights': ['The model achieves an 84% accuracy on the test data, showcasing its effectiveness in text classification.', 'Balancing the data with random over sampler resulted in a 77% accuracy improvement.', 'Recurrent neural networks create a memory effect by considering previous calculations at each time step, making them powerful for sequential data processing.', "The process of using TensorFlow's Keras for building the model to predict diabetes, showcasing the application of neural networks in healthcare.", 'The process of training a neural network involves defining optimizers, loss functions, and adding accuracy metrics to the model, as well as evaluating its performance on training and validation data.', 'The chapter focuses on supervised learning using TensorFlow to create a neural net for text classification.', "The tutorial concludes by highlighting the gained knowledge in implementing a feed forward neural network and using TensorFlow for text classification, while achieving success in determining the wine review's tier based on the review content.", 'The process of adding dropout layers is explained, where randomly selected nodes are not utilized to introduce randomness and aid the model in generalizing.', 'The process of implementing a feed forward neural network using TensorFlow, emphasizing practical application.', 'The process of dataset splitting into training, validation, and testing data sets is crucial for model generalization.']}