title
What is Word2Vec? A Simple Explanation | Deep Learning Tutorial 41 (Tensorflow, Keras & Python)

description
A very simple explanation of word2vec. This video gives an intuitive understanding of how word2vec algorithm works and how it can generate accurate word embeddings for words such that you can do math with words (a famous example is king - man + woman = queen) Part 2 (Coding): https://youtu.be/Q2NtCcqmIww Deep learning playlist: https://www.youtube.com/playlist?list=PLeo1K3hjS3uu7CxAacxVndI4bE_o3BDtO Machine learning playlist : https://www.youtube.com/playlist?list=PLeo1K3hjS3uvCeTYTeyfe0-rN5r8zn9rw   Do you want to learn technology from me? Check https://codebasics.io/?utm_source=description&utm_medium=yt&utm_campaign=description&utm_id=description for my affordable video courses. 🔖Hashtags🔖 #word2vecexplained #word2vec #nlpword2vec #nlpword2vectutorial #word2vecdeeplearning #word2vecpython #wordembeddings #wordembedding #pythonword2vec #deeplearning #word2vec #deeplearningtensorflow #deeplearningWord2Vec 🌎 Website: https://codebasics.io/?utm_source=description&utm_medium=yt&utm_campaign=description&utm_id=description 🎥 Codebasics Hindi channel: https://www.youtube.com/channel/UCTmFBhuhMibVoSfYom1uXEg #️⃣ Social Media #️⃣ 🔗 Discord: https://discord.gg/r42Kbuk 📸 Dhaval's Personal Instagram: https://www.instagram.com/dhavalsays/ 📸 Instagram: https://www.instagram.com/codebasicshub/ 🔊 Facebook: https://www.facebook.com/codebasicshub 📱 Twitter: https://twitter.com/codebasicshub 📝 Linkedin (Personal): https://www.linkedin.com/in/dhavalsays/ 📝 Linkedin (Codebasics): https://www.linkedin.com/company/codebasics/ ❗❗ DISCLAIMER: All opinions expressed in this video are of my own and not that of my employers'.

detail
{'title': 'What is Word2Vec? A Simple Explanation | Deep Learning Tutorial 41 (Tensorflow, Keras & Python)', 'heatmap': [{'end': 623.502, 'start': 585.472, 'weight': 0.703}, {'end': 684.221, 'start': 630.763, 'weight': 0.727}, {'end': 866.363, 'start': 794.584, 'weight': 0.749}, {'end': 992.461, 'start': 963.348, 'weight': 0.862}], 'summary': 'Discusses word2vec, explaining its ability to perform mathematical operations with words and representation of word relationships through neural networks, covering topics like word vector representation, application of vector mathematics, and learning word embeddings and neural networks.', 'chapters': [{'end': 193.658, 'segs': [{'end': 84.9, 'src': 'embed', 'start': 0.009, 'weight': 0, 'content': [{'end': 5.475, 'text': 'Word2Vec is a technique in computer science that allows you to do mathematics with the word.', 'start': 0.009, 'duration': 5.466}, {'end': 13.483, 'text': 'For example, you can give this equation to a computer, which will be something like king minus man plus woman,', 'start': 5.535, 'duration': 7.948}, {'end': 16.626, 'text': 'and computer will tell you the answer is queen.', 'start': 13.483, 'duration': 3.143}, {'end': 23.658, 'text': "What? Isn't that mind boggling? This is super cool and it works really well.", 'start': 17.547, 'duration': 6.111}, {'end': 25.139, 'text': "And I'm not making this up.", 'start': 23.958, 'duration': 1.181}, {'end': 28.881, 'text': 'So how can computer do this? Well, think about this.', 'start': 25.619, 'duration': 3.262}, {'end': 32.523, 'text': "Computers don't understand text, so they understand numbers.", 'start': 29.261, 'duration': 3.262}, {'end': 43.114, 'text': 'So if there is a way to represent a word king, in a number such that it can accurately represent the meaning of the word King.', 'start': 32.863, 'duration': 10.251}, {'end': 45.535, 'text': 'Now that number cannot be one number.', 'start': 43.694, 'duration': 1.841}, {'end': 51.038, 'text': 'So you need to have set of numbers and in mathematics set of numbers are called vectors.', 'start': 45.575, 'duration': 5.463}, {'end': 52.459, 'text': "So let's think about this.", 'start': 51.599, 'duration': 0.86}, {'end': 63.406, 'text': 'How about we represent word King into a vector, which is just a bunch of numbers such that it can represent the meaning of word King accurately.', 'start': 53.02, 'duration': 10.386}, {'end': 65.22, 'text': 'Now think about king.', 'start': 64.42, 'duration': 0.8}, {'end': 71.604, 'text': 'King has a different property or there are different ways of representing the word king.', 'start': 65.721, 'duration': 5.883}, {'end': 73.085, 'text': 'For example, king has authority.', 'start': 71.704, 'duration': 1.381}, {'end': 76.147, 'text': 'King is rich usually.', 'start': 74.586, 'duration': 1.561}, {'end': 79.488, 'text': 'King has a gender of male.', 'start': 77.327, 'duration': 2.161}, {'end': 84.9, 'text': 'Okay, does king have a tail?', 'start': 79.508, 'duration': 5.392}], 'summary': "Word2vec enables computers to perform mathematical operations on words, such as 'king - man + woman = queen', by representing words as vectors of numbers.", 'duration': 84.891, 'max_score': 0.009, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/hQwFeIupNP0/pics/hQwFeIupNP09.jpg'}, {'end': 169.48, 'src': 'embed', 'start': 140.71, 'weight': 4, 'content': [{'end': 143.192, 'text': 'So let me just show you a very simple example here.', 'start': 140.71, 'duration': 2.482}, {'end': 152.01, 'text': "Let's say I have a story of king and queen, and I want to represent all the words in that story with word vectors.", 'start': 144.366, 'duration': 7.644}, {'end': 156.793, 'text': 'Here I have different properties such as authority, event, has tail, and so on.', 'start': 152.791, 'duration': 4.002}, {'end': 158.674, 'text': "And let's say there is a word called battle.", 'start': 157.213, 'duration': 1.461}, {'end': 164.097, 'text': 'For battle, battle is an event, so that value is one, remaining values are zero.', 'start': 159.254, 'duration': 4.843}, {'end': 167.259, 'text': "Horse has a tail, that's why it's one.", 'start': 165.077, 'duration': 2.182}, {'end': 169.48, 'text': 'Horse might have little authority.', 'start': 167.819, 'duration': 1.661}], 'summary': 'Using word vectors to represent story words with specific properties and values.', 'duration': 28.77, 'max_score': 140.71, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/hQwFeIupNP0/pics/hQwFeIupNP0140710.jpg'}], 'start': 0.009, 'title': 'Word2vec and word vector representation', 'summary': 'Explains word2vec, a technique enabling mathematical operations with words and discusses word vector representation based on properties like authority, tail, richness, and gender, facilitating mathematical operations and representation of different words in a story.', 'chapters': [{'end': 84.9, 'start': 0.009, 'title': 'Word2vec: mathematics with words', 'summary': 'Explains the concept of word2vec, a technique in computer science that allows mathematical operations with words, such as king minus man plus woman equals queen, by representing words as vectors of numbers to accurately capture their meanings and properties.', 'duration': 84.891, 'highlights': ['Word2Vec allows mathematical operations with words, such as king minus man plus woman equals queen, by representing words as vectors of numbers to accurately capture their meanings and properties.', 'Computers understand text as numbers and represent words as vectors to capture their meanings and properties.', 'Word vectors capture different properties of words, such as authority, wealth, and gender, to accurately represent their meanings.']}, {'end': 193.658, 'start': 84.9, 'title': 'Word vector representation', 'summary': 'Discusses representing words using word vectors based on properties like authority, tail, richness, and gender, and demonstrates a simple example of representing words in a story with word vectors, enabling mathematical operations. word vectors are created from properties like authority, tail, richness, and gender, allowing mathematical operations and representation of different words in a story.', 'duration': 108.758, 'highlights': ['Word vectors are created from properties like authority, tail, richness, and gender, enabling mathematical operations. (relevance score: 5)', 'A simple example of representing words in a story with word vectors is demonstrated, showcasing the representation of different properties like authority, event, and tail. (relevance score: 4)', "The vector representation of the word 'king' is one, zero, one, minus one, representing properties like authority, tail, richness, and gender. (relevance score: 3)"]}], 'duration': 193.649, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/hQwFeIupNP0/pics/hQwFeIupNP09.jpg', 'highlights': ['Word2Vec enables mathematical operations with words, e.g., king minus man plus woman equals queen.', 'Word vectors represent words as numbers to capture their meanings and properties.', 'Word vectors capture different properties of words, such as authority, wealth, and gender, to accurately represent their meanings.', 'Word vectors are created from properties like authority, tail, richness, and gender, enabling mathematical operations.', 'A simple example demonstrates the representation of different properties like authority, event, and tail.', "The vector representation of the word 'king' represents properties like authority, tail, richness, and gender."]}, {'end': 435.995, 'segs': [{'end': 281.82, 'src': 'embed', 'start': 252.619, 'weight': 1, 'content': [{'end': 254.56, 'text': 'There are so many thousands of words.', 'start': 252.619, 'duration': 1.941}, {'end': 260.403, 'text': 'And to come up with these kind of properties for each of these words will be very, very difficult.', 'start': 255.819, 'duration': 4.584}, {'end': 266.027, 'text': "So you don't want to handcraft it in computer programming.", 'start': 262.064, 'duration': 3.963}, {'end': 272.733, 'text': 'You can use basically neural networks to learn these feature vectors.', 'start': 266.608, 'duration': 6.125}, {'end': 276.215, 'text': 'So these these numbers are called feature vectors.', 'start': 272.773, 'duration': 3.442}, {'end': 281.82, 'text': 'OK, so authority event has still are called features in the language of machine learning.', 'start': 276.336, 'duration': 5.484}], 'summary': 'Using neural networks to learn feature vectors for thousands of words in machine learning.', 'duration': 29.201, 'max_score': 252.619, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/hQwFeIupNP0/pics/hQwFeIupNP0252619.jpg'}, {'end': 418.976, 'src': 'embed', 'start': 384.04, 'weight': 0, 'content': [{'end': 385.542, 'text': 'These are those feature vectors.', 'start': 384.04, 'duration': 1.502}, {'end': 387.925, 'text': 'And once you have vectors, you can do math.', 'start': 386.243, 'duration': 1.682}, {'end': 391.37, 'text': 'You can say king is almost equal to emperor.', 'start': 388.586, 'duration': 2.784}, {'end': 395.936, 'text': 'So see, you will be able to derive the synonyms, the antonyms.', 'start': 391.811, 'duration': 4.125}, {'end': 400.082, 'text': 'You can do math such as king minus man plus woman is equal to queen and so on.', 'start': 395.956, 'duration': 4.126}, {'end': 405.449, 'text': "So now let's look into this problem a little further.", 'start': 401.567, 'duration': 3.882}, {'end': 407.35, 'text': 'For example, you have this sentence.', 'start': 405.769, 'duration': 1.581}, {'end': 410.912, 'text': 'Eating something is very healthy.', 'start': 408.19, 'duration': 2.722}, {'end': 418.976, 'text': "And if I ask you to fill in the missing word, well, most likely you will say apple and walnut because that's food and that's healthy.", 'start': 411.732, 'duration': 7.244}], 'summary': 'Feature vectors enable deriving synonyms and antonyms, and solving word analogies through mathematics.', 'duration': 34.936, 'max_score': 384.04, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/hQwFeIupNP0/pics/hQwFeIupNP0384040.jpg'}], 'start': 193.658, 'title': 'Word representation in mathematics', 'summary': 'Covers the application of vector mathematics to represent word relationships and the use of neural networks for word embedding. it discusses mathematical equations yielding vectors and the demonstration of word vector computation through neural networks.', 'chapters': [{'end': 252.319, 'start': 193.658, 'title': 'Vector mathematics for word relationships', 'summary': "Discusses the application of vector mathematics to represent word relationships, demonstrating a mathematical equation that yields a vector result similar to the word 'queen' through the computation of 'king minus men plus woman'.", 'duration': 58.661, 'highlights': ["The mathematical equation 'king minus men plus woman' yields a result vector similar to that of 'queen', with only a 0.1 difference, showcasing the power of computer-enabled word relationship representation.", 'The process involves forming vectors for words and performing mathematical operations on them, demonstrating the potential for computer-enabled word relationship identification and representation.', 'The application of this mathematical approach eliminates the need for manual coding of word properties, particularly beneficial for large-scale natural language processing tasks such as analyzing Wikipedia text.']}, {'end': 435.995, 'start': 252.619, 'title': 'Neural network for word embedding', 'summary': 'Discusses using neural networks to learn feature vectors for word embedding, demonstrating how a fake problem can be used to derive word vectors and perform mathematical operations on words.', 'duration': 183.376, 'highlights': ['Neural networks can be used to learn feature vectors for words, eliminating the need for handcrafting properties for each word. Using neural networks to learn feature vectors for words removes the need for manual programming and handcrafting properties for each word.', 'A fake problem, such as finding a missing word in a sentence, can be used to derive word embedding as a side effect of solving the problem. By using a fake problem, like finding a missing word in a sentence, as a side effect of solving it, word embedding can be derived, enabling the learning of word vectors.', "Word vectors allow for mathematical operations on words, such as deriving synonyms, antonyms, and performing arithmetic operations like 'king - man + woman = queen'. The obtained word vectors enable mathematical operations on words, allowing for the derivation of synonyms, antonyms, and arithmetic operations like 'king - man + woman = queen'."]}], 'duration': 242.337, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/hQwFeIupNP0/pics/hQwFeIupNP0193658.jpg', 'highlights': ["The mathematical equation 'king minus men plus woman' yields a result vector similar to that of 'queen', showcasing the power of word relationship representation.", 'Neural networks can learn feature vectors for words, eliminating the need for handcrafting properties for each word.', 'Word vectors allow for mathematical operations on words, such as deriving synonyms, antonyms, and performing arithmetic operations.']}, {'end': 1106.605, 'segs': [{'end': 490.161, 'src': 'embed', 'start': 461.181, 'weight': 3, 'content': [{'end': 470.557, 'text': "So now let's take this paragraph and We will try to auto complete those missing words.", 'start': 461.181, 'duration': 9.376}, {'end': 475.658, 'text': 'And auto completing missing words is really not the area of our interest.', 'start': 471.557, 'duration': 4.101}, {'end': 476.838, 'text': 'It is our fake problem.', 'start': 475.778, 'duration': 1.06}, {'end': 482.659, 'text': 'Our area of interest is to learn the word embeddings, the vectors which can represent those words.', 'start': 477.338, 'duration': 5.321}, {'end': 490.161, 'text': 'So I will parse this paragraph and I will take a window of three words.', 'start': 484.38, 'duration': 5.781}], 'summary': 'Learning word embeddings to represent words in a window of three words.', 'duration': 28.98, 'max_score': 461.181, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/hQwFeIupNP0/pics/hQwFeIupNP0461181.jpg'}, {'end': 564.289, 'src': 'embed', 'start': 538.896, 'weight': 0, 'content': [{'end': 546.001, 'text': "You need to have some understanding of neural network in order to understand things which I'm going to explain in this video.", 'start': 538.896, 'duration': 7.105}, {'end': 549.083, 'text': "So if you don't know already what is neural network, pause the video right now.", 'start': 546.301, 'duration': 2.782}, {'end': 554.006, 'text': "I'm going to provide my neural network video link in a video description below.", 'start': 549.903, 'duration': 4.103}, {'end': 556.248, 'text': 'So just get some basic understanding.', 'start': 554.086, 'duration': 2.162}, {'end': 564.289, 'text': "Assuming you have the basic understanding now, let's go back to our problem, which is you have training samples.", 'start': 559.187, 'duration': 5.102}], 'summary': 'Video assumes basic understanding of neural networks for discussing training samples.', 'duration': 25.393, 'max_score': 538.896, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/hQwFeIupNP0/pics/hQwFeIupNP0538896.jpg'}, {'end': 623.502, 'src': 'heatmap', 'start': 585.472, 'weight': 0.703, 'content': [{'end': 588.414, 'text': "So let's have my first sample is order his.", 'start': 585.472, 'duration': 2.942}, {'end': 594.097, 'text': 'So order his is an input based on that you want to predict working, which is an output.', 'start': 588.894, 'duration': 5.203}, {'end': 598.18, 'text': 'Now you can build a neural network that looks something like this.', 'start': 595.558, 'duration': 2.622}, {'end': 605.144, 'text': 'The input layer will have a one hot encoded vector.', 'start': 599.48, 'duration': 5.664}, {'end': 609.106, 'text': "So let's say there are 5, 000 words in my vocabulary.", 'start': 605.444, 'duration': 3.662}, {'end': 616.04, 'text': 'then there will be a vector of size 5000 and only one of them will be one.', 'start': 610.999, 'duration': 5.041}, {'end': 623.502, 'text': 'So if the word is ordered, the value of order will be one and remaining numbers will be zero.', 'start': 616.22, 'duration': 7.282}], 'summary': 'Using a one hot encoded vector, a neural network can predict working based on order frequency.', 'duration': 38.03, 'max_score': 585.472, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/hQwFeIupNP0/pics/hQwFeIupNP0585472.jpg'}, {'end': 684.221, 'src': 'heatmap', 'start': 630.763, 'weight': 0.727, 'content': [{'end': 631.803, 'text': "5000 is, let's say, vocabulary.", 'start': 630.763, 'duration': 1.04}, {'end': 640.045, 'text': "Vocabulary means unique words in your text corpus or in the, you know, text problem that you're trying to solve.", 'start': 632.083, 'duration': 7.962}, {'end': 647.026, 'text': 'And in the hidden layer, here I have put four neurons.', 'start': 642.245, 'duration': 4.781}, {'end': 651.767, 'text': 'And these four neurons are the size of my embedding vector.', 'start': 648.227, 'duration': 3.54}, {'end': 655.228, 'text': 'Now, size of embedding vector could be anything.', 'start': 653.348, 'duration': 1.88}, {'end': 656.509, 'text': 'Like, there is no golden rule.', 'start': 655.428, 'duration': 1.081}, {'end': 660.43, 'text': "I just selected four, but it's a hyperparameter to your neural network.", 'start': 656.609, 'duration': 3.821}, {'end': 663.09, 'text': 'It could be 5, 10, 200, anything.', 'start': 660.47, 'duration': 2.62}, {'end': 665.551, 'text': 'This is something you learn using trial and error.', 'start': 663.53, 'duration': 2.021}, {'end': 673.957, 'text': 'In the output layer, I will have 5, 000 size vector.', 'start': 667.014, 'duration': 6.943}, {'end': 684.221, 'text': 'And when I feed this training sample into my neural network, what happens is these weights or the edges will have random weights.', 'start': 675.137, 'duration': 9.084}], 'summary': 'Neural network has 5000 vocabulary, 4 neurons, and 5000 size vector in output layer.', 'duration': 53.458, 'max_score': 630.763, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/hQwFeIupNP0/pics/hQwFeIupNP0630763.jpg'}, {'end': 866.363, 'src': 'heatmap', 'start': 794.584, 'weight': 0.749, 'content': [{'end': 799.568, 'text': "and let's say, you run 10 or 15 or 50 epochs and your neural network is trained.", 'start': 794.584, 'duration': 4.984}, {'end': 800.309, 'text': 'at that point.', 'start': 799.568, 'duration': 0.741}, {'end': 809.212, 'text': 'the word vector for king would be these weights w1, w2, w3, w4.', 'start': 801.826, 'duration': 7.386}, {'end': 819.74, 'text': 'so those weights are nothing but a trained word vector, and this vector will be very similar to a vector of emperor.', 'start': 809.212, 'duration': 10.528}, {'end': 821.882, 'text': 'so the vector for the emperor will be w5, w6, w7, w8.', 'start': 819.74, 'duration': 2.142}, {'end': 825.812, 'text': 'Just think about it.', 'start': 825.151, 'duration': 0.661}, {'end': 828.334, 'text': 'It will be similar because the input is same.', 'start': 826.112, 'duration': 2.222}, {'end': 833.278, 'text': 'So here order and his, both for king and emperor, the input is same.', 'start': 828.394, 'duration': 4.884}, {'end': 840.383, 'text': 'So when the input is same, you expect that these weights will also be similar.', 'start': 833.718, 'duration': 6.665}, {'end': 846.128, 'text': 'And hence the vector for king and emperor will be very similar using this approach.', 'start': 841.224, 'duration': 4.904}, {'end': 849.15, 'text': 'This approach is called continuous bag of words.', 'start': 846.568, 'duration': 2.582}, {'end': 860.538, 'text': 'So here you have a context which is order his, and based on that context, you are trying to predict Target, which is King.', 'start': 850.371, 'duration': 10.167}, {'end': 866.363, 'text': 'There is a second methodology Called skip gram in script gram.', 'start': 860.538, 'duration': 5.825}], 'summary': 'Train neural network with 10-50 epochs, word vectors for king and emperor will be similar using continuous bag of words approach.', 'duration': 71.779, 'max_score': 794.584, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/hQwFeIupNP0/pics/hQwFeIupNP0794584.jpg'}, {'end': 936.517, 'src': 'embed', 'start': 879.714, 'weight': 1, 'content': [{'end': 880.856, 'text': 'these are fake problems.', 'start': 879.714, 'duration': 1.142}, {'end': 888.265, 'text': 'you know, we are not interested in solving this problem, but while we solve those problems as a side effect, we get word embedding,', 'start': 880.856, 'duration': 7.409}, {'end': 891.329, 'text': 'so we are more interested in learning word embeddings.', 'start': 888.265, 'duration': 3.064}, {'end': 899.894, 'text': 'just to summarize, word2vec is not a single method, but it could be using one of the two techniques,', 'start': 891.329, 'duration': 8.565}, {'end': 905.056, 'text': 'which is either continuous bag of words or skip gram, to learn word embeddings.', 'start': 899.894, 'duration': 5.162}, {'end': 909.578, 'text': 'See the word word2vec means convert word to a vector.', 'start': 905.336, 'duration': 4.242}, {'end': 923.467, 'text': 'So Word2Vect is a revolutionary invention in the field of computer science, which allows you to represent words in a vector in a very accurate way,', 'start': 910.558, 'duration': 12.909}, {'end': 925.149, 'text': 'so that you can do mathematics with it.', 'start': 923.467, 'duration': 1.682}, {'end': 927.911, 'text': "Let's talk about SkipGram.", 'start': 926.69, 'duration': 1.221}, {'end': 933.334, 'text': 'So in SkipGram, I have inverted my neural network diagram.', 'start': 928.631, 'duration': 4.703}, {'end': 936.517, 'text': "So here you can see it's exactly reversed than the C bar.", 'start': 933.395, 'duration': 3.122}], 'summary': 'Word2vec enables accurate word representation in vectors for mathematical operations.', 'duration': 56.803, 'max_score': 879.714, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/hQwFeIupNP0/pics/hQwFeIupNP0879714.jpg'}, {'end': 992.461, 'src': 'heatmap', 'start': 963.348, 'weight': 0.862, 'content': [{'end': 967.591, 'text': 'So the embedding for Ashoka will be w1 to w4.', 'start': 963.348, 'duration': 4.243}, {'end': 969.973, 'text': 'The embedding for emperor will be w6 to w9.', 'start': 967.671, 'duration': 2.302}, {'end': 979.397, 'text': "So when you're using SkipGram, the word embedding is a layer between the input layer and the hidden layer.", 'start': 971.954, 'duration': 7.443}, {'end': 985.379, 'text': 'In the CBOW, it was the weights between hidden layer and the output layer.', 'start': 979.957, 'duration': 5.422}, {'end': 992.461, 'text': 'You can do wonderful things with Word2Vec, such as USA-Washington DC plus Delhi.', 'start': 987.059, 'duration': 5.402}], 'summary': 'Word2vec uses word embeddings for skipgram and cbow methods.', 'duration': 29.113, 'max_score': 963.348, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/hQwFeIupNP0/pics/hQwFeIupNP0963348.jpg'}], 'start': 438.402, 'title': 'Learning word embeddings and neural networks', 'summary': 'Covers the use of context to infer word meaning, learning word embeddings through training samples, understanding neural networks, word2vec training, continuous bag of words (cbow), skip gram, mathematical operations with word vectors, and overall understanding of word2vec.', 'chapters': [{'end': 556.248, 'start': 438.402, 'title': 'Learning word embeddings and neural networks', 'summary': 'Discusses the use of context to infer word meaning, learning word embeddings through training samples, and the importance of understanding neural networks for the topic.', 'duration': 117.846, 'highlights': ['The meaning of a word can be inferred by surrounding words, also known as context.', 'Learning word embeddings involves parsing a paragraph and creating training samples by taking a window of three words.', 'The training set for a neural network consists of word pairs, where the left-hand words are input (x) and the right-hand words are output (y).', 'Understanding neural networks is crucial for grasping the concepts discussed in this chapter.']}, {'end': 1106.605, 'start': 559.187, 'title': 'Word2vec: learning word embeddings', 'summary': 'Discusses the self-supervised problem of training a neural network for word2vec, using techniques like continuous bag of words (cbow) and skip gram. it explores the process of learning word embeddings and the ability to perform mathematical operations with word vectors, ultimately leading to a better understanding of word2vec.', 'duration': 547.418, 'highlights': ['The chapter explains the self-supervised problem of training a neural network for word2vec, using techniques like continuous bag of words (CBOW) and skip gram, to learn word embeddings with vocabulary size of 5,000 words and an embedding vector size of 4.', 'It details the process of back propagation in training the neural network with 10,000 to 1 million samples and 10 to 50 epochs, aiming to accurately predict words based on context for word embeddings.', "The chapter highlights the ability of word2vec to represent words in a vector in an accurate way, enabling mathematical operations like 'USA-Washington DC plus Delhi equals India,' and the visualization of word relationships in vector space.", 'The chapter explains the next steps, including exploring the coding part using Python to demonstrate the functionality of Word2Vec and running code to understand its practical application.']}], 'duration': 668.203, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/hQwFeIupNP0/pics/hQwFeIupNP0438402.jpg', 'highlights': ['Understanding neural networks is crucial for grasping the concepts discussed in this chapter.', 'The chapter explains the self-supervised problem of training a neural network for word2vec, using techniques like continuous bag of words (CBOW) and skip gram, to learn word embeddings with vocabulary size of 5,000 words and an embedding vector size of 4.', "The chapter highlights the ability of word2vec to represent words in a vector in an accurate way, enabling mathematical operations like 'USA-Washington DC plus Delhi equals India,' and the visualization of word relationships in vector space.", 'Learning word embeddings involves parsing a paragraph and creating training samples by taking a window of three words.']}], 'highlights': ['Word2Vec enables mathematical operations with words, e.g., king minus man plus woman equals queen.', "The mathematical equation 'king minus men plus woman' yields a result vector similar to that of 'queen', showcasing the power of word relationship representation.", 'Word vectors represent words as numbers to capture their meanings and properties.', 'Word vectors capture different properties of words, such as authority, wealth, and gender, to accurately represent their meanings.', 'Neural networks can learn feature vectors for words, eliminating the need for handcrafting properties for each word.', 'Word vectors allow for mathematical operations on words, such as deriving synonyms, antonyms, and performing arithmetic operations.', 'The chapter explains the self-supervised problem of training a neural network for word2vec, using techniques like continuous bag of words (CBOW) and skip gram, to learn word embeddings with vocabulary size of 5,000 words and an embedding vector size of 4.', "The chapter highlights the ability of word2vec to represent words in a vector in an accurate way, enabling mathematical operations like 'USA-Washington DC plus Delhi equals India,' and the visualization of word relationships in vector space."]}