Stanford CS224N: NLP with Deep Learning | Winter 2021 | Lecture 1 - Intro & Word Vectors

This lecture covers:
1. The course (10min)
2. Human language and word meaning (15 min)
3. Word2vec algorithm introduction (15 min)
4. Word2vec objective function gradients (25 min)
5. Optimization basics (5min)
6. Looking at word vectors (10 min or less)

Key learning: The (really surprising!) result that word meaning can be representing rather well by a large vector of real numbers.

This course will teach:
1. The foundations of the effective modern methods for deep learning applied to NLP. Basics first, then key methods used in NLP: recurrent networks, attention, transformers, etc.
2. A big picture understanding of human languages and the difficulties in understanding and producing them
3. An understanding of an ability to build systems (in Pytorch) for some of the major problems in NLP. Word meaning, dependency parsing, machine translation, question answering.

Professor Christopher Manning
Thomas M. Siebel Professor in Machine Learning, Professor of Linguistics and of Computer Science
Director, Stanford Artificial Intelligence Laboratory (SAIL)

0:00 Introduction
1:43 Goals
3:10 Human Language
10:07 Google Translate
10:43 GPT
14:13 Meaning
16:19 Wordnet
19:11 Word Relationships
20:27 Distributional Semantics
23:33 Word Embeddings
27:31 Word tovec
37:55 How to minimize loss
39:55 Interactive whiteboard
41:10 Gradient
48:50 Chain Rule

[rahullak]

Thank you to Stanford and to Prof. Manning for making these lectures available to everyone.

[itaylavi]

So many full courses in great quality, great lecturers AND with normal subtitles... Can someone PLEASE give Stanford University some kind of international prize for knowledge sharing?

[gefallenesobst]

I am so grateful that Stanford has given us all this great gift. Thanks to their great machine learning and AI video series, I am able to build a solid foundation of knowledge and have started my PhD based on that.

[nabilisham]

🎯 Key Takeaways for quick navigation:

00:05 🎓 This lecture introduces Stanford's CS224N course on NLP with deep learning, covering topics like word vectors, word2vec algorithm, optimization, and system building.
01:32 🤯 The surprising discovery that word meanings can be well represented by large vectors of real numbers challenges centuries of linguistic tradition.
02:29 📚 The course aims to teach deep understanding of modern NLP methods, provide insights into human language complexity, and impart PyTorch-based skills for solving NLP problems.
07:15 🗓️ Human language's evolution is relatively recent (100, 000 - 1 million years ago), but it has led to significant communication power and adaptability.
10:59 🧠 GPT-3 is a powerful language model capable of diverse tasks due to its ability to predict and generate text based on context and examples.
14:52 🧩 Distributional semantics uses context words to represent word meaning as dense vectors, enabling similarity and relationships between words to be captured.
18:37 🏛️ Traditional NLP represented words as discrete symbols, lacking a natural notion of similarity; distributional semantics overcomes this by capturing meaning through context.
25:19 🔍 Word embeddings, or distributed representations, place words in high-dimensional vector spaces; they group similar words, forming clusters that capture meaning relationships.
27:15 🧠 Word2Vec is an algorithm introduced by Tomas Mikolov and colleagues in 2013 for learning word vectors from text corpus.
28:11 📚 Word2Vec creates vector representations for words by predicting words' context in a text corpus using distributional similarity.
29:07 🔄 Word vectors are adjusted to maximize the probability of context words occurring around center words in the training text.
31:02 🎯 Word2Vec aims to predict context words within a fixed window size given a center word, optimizing for predictive accuracy.
32:56 📈 The optimization process involves calculating gradients using calculus to adjust word vectors for better context word predictions.
36:33 💡 Word2Vec employs the softmax function to convert dot products of word vectors into probability distributions for context word prediction.
38:51 ⚙️ The optimization process aims to minimize the loss function, maximizing the accuracy of context word predictions.
45:53 📝 The derivative of the log probability of context words involves using the chain rule and results in a formula similar to the softmax probability formula.
49:28 🔢 The gradient calculation involves adjusting word vectors to minimize the difference between observed and expected context word probabilities.
53:34 🔀 The derivative of the log probability formula simplifies into a form where the observed context word probability is subtracted from the expected probability.
58:57 📊 Word vectors for "bread" and "croissant" show similarity in dimensions, indicating they are related.
59:26 🌐 Word vectors reveal similar words to "croissant" (e.g., brioche, baguette), and analogies like "USA" to "Canada" can be inferred.
59:55 ➗ Word vector arithmetic allows analogy tasks, like "king - male + female = queen, " and similar analogies can be formed for various words.
01:00:22 🤖 The analogy task shows the ability to perform vector arithmetic and retrieve similar words based on relationships.
01:01:23 🤔 Negative similarity and positive similarity together enable analogies and meaningful relationships among words.
01:03:17 💬 The model's knowledge is limited to the time it was built (2014), but it can still perform various linguistic analogies.
01:04:39 🧠 Word vectors capture multiple meanings and contexts for a single word, like "star" having astronomical or fame-related connotations.
01:05:36 🔄 Different vectors are used for a word as the center and as part of the context, contributing to the overall representation.
01:07:02 🧐 Using separate vectors for center and context words simplifies derivatives calculations and results in similar word representations.
01:11:26 ⚖️ The model struggles with capturing antonyms and sentiment-related relationships due to common contexts.
01:12:44 🎙️ The class primarily focuses on text analysis, with a separate speech class covering speech recognition and dialogue systems.
01:18:06 🗣️ Function words like "so" and "not" pose challenges due to occurring in diverse contexts, but advanced models consider structural information.
01:20:25 🧠 Word2Vec offers different algorithms within the framework; optimization details like negative sampling can significantly improve efficiency.
01:23:18 🔁 The process of constructing word vectors involves iterative updates using gradients, moving towards minimizing the loss function.

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[teogiannilias]

Thanks for everything Stanford University. As an AI master's student I have to state that having these lectures for free enables me to compare and broaden my ideas for NLP, resulting in deeper intuitive understanding of the subject.

[tusharrohilla]

Amazing lecture it was, thanks to Stanford to make these lectures public.

[ansekao]

What a great lecturer, he feels students, puts himself in our place and explains material very nicely. This is literally my first piece of material about NLP I have ever seen, and I understood most of it. Thanks a lot

[hewas]

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[vanongle]

hello Stanford online i started to self-study machine learning my university program does not teach in depth about AI, i feel i have not reached my full potential and i have taught myself about AI for 6 months recently .And i have learned, learned all areas in AI, machine learning, deep learning or reinforcement learning, thank you for this free lecture, i really appreciate it.

[wenqianzhao]

Moved from Coursera NLP Specialization to here. Definately amazing to receive such detailed math explanations of all these concepts

[MenTaLLyMenTaL]

Math is not magic, but is as beautiful as magic.

[commonsense]

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[SrivathsanM-wv]

For some reason I am reminded of Grant from 3Blue1Brown. The way he speaks and the way he's excited about the subject, it's so intoxicating.

[dazhijiang-fxhe]

Can't expect more from a lesson! Thank you all for sharing the class towards all the people🤩

[kunalnarang]

The result at 55:45 is just beautiful!

[progamer]

Really liked the energy and simplicity of the presentation !

[niyousha]

It is great to watch this and don't have to do the homework.

[ThaoPham-pevj]

Thank you Stanford and Professor for the excellent lecture!

[NelluriPavithra]

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[goanshubansal]

hopefully I will be proud after it's completion