🎯 Key Takeaways for quick navigation: 00:05 📚 Self-supervised learning is the focus of the lecture, involving recent works in the last few years. 00:32 🔍 An emergent paradigm of AI involves large-scale unsupervised or self-supervised learning based on deep neural networks. 01:00 💡 Self-supervised learning is a new type of unsupervised learning that leverages neural networks with technical and conceptual differences. 01:54 📑 "Foundation model" is a term for the collection of ideas involving pre-training on unsupervised data and adapting for downstream tasks. 02:20 🧠 Pre-training involves training neural networks on a large-scale unlabeled dataset to learn general features or representations. 03:17 💡 The shift towards self-supervised learning allows leveraging unlabeled data, enabling the use of much larger datasets for training. 04:14 🎯 Pre-training involves two steps: large-scale pre-training on unlabeled data and adaptation to downstream tasks with labeled data. 06:00 🚀 Adaptation involves fine-tuning a pre-trained model for specific tasks, often with few labeled examples. 06:59 🔁 The distinction between pre-training and adaptation is that pre-training focuses on intrinsic data structure, while adaptation focuses on specific tasks. 08:20 📊 Pre-training aims to create a foundation model with generic representations, enabling better performance on downstream tasks. 10:12 🏛️ "Foundation model" implies a general-purpose model with widespread applicability, serving as a basis for adaptation. 11:10 📝 Pre-training is done with a loss function optimized on unlabeled data, resulting in a foundation model. 15:18 🎓 Adaptation involves using labeled downstream task examples to fine-tune the model for specific tasks. 19:59 🎯 Linear probing is one adaptation approach involving training a linear classifier on the pre-trained feature representations. 24:43 ⚙️ Fine-tuning entails adapting both the model's parameters and linear classifier to the downstream task, initializing with pre-trained parameters. 27:26 🧠 Self-supervised learning methods 28:21 🌐 Pretraining approaches vary for different domains (vision, language) 29:17 🖼️ Supervised pretraining in vision using labeled data (ImageNet) 32:05 🔬 Applying pretrained models to new tasks and fine-tuning 34:46 💡 Unsupervised contrastive learning for pretraining 35:13 🔄 Data augmentation techniques for unsupervised learning 38:08 ➕ Designing loss functions to encourage similar representations for positive pairs 40:00 ➖ Designing loss functions to encourage dissimilar representations for random pairs 42:48 🎓 SIMCLR as an example of a contrastive learning framework 54:16 📚 Applying similar self-supervised pretraining to language models 55:31 🔤 Encoding text data for large language models 56:27 🧩 Self-supervised learning involves encoding data using binary vectors and more realistic language models. 57:22 📜 Language examples are sequences of words or documents; often extracted from large texts like Wikipedia. 58:49 🗃️ Tokens represent words, where common words are single tokens, while less frequent or longer words can be split into multiple tokens. 01:00:16 📊 Language models involve predicting probabilities of sequences; using chain rule to simplify probability calculations. 01:01:05 🔤 Neural networks, like transformers, are used to predict conditional probabilities of words. 01:05:12 📡 Transformers encode input sequences into output vectors; used to compute conditional probabilities. 01:06:52 🧭 Conditional probability models predict next word probabilities; softmax and linear transformations are often used. 01:08:18 📚 Cross-entropy loss measures the difference between predicted and actual probabilities. 01:19:57 📝 Adaptation methods include zero-shot learning (generation-based) and in-context learning (few-shot learning). Made with HARPA AI
Thank you very much for those very interesting videos - Would be great to have a link to the playlist for the course and the details in the description.
🎯 Key Takeaways for quick navigation:
00:05 📚 Self-supervised learning is the focus of the lecture, involving recent works in the last few years.
00:32 🔍 An emergent paradigm of AI involves large-scale unsupervised or self-supervised learning based on deep neural networks.
01:00 💡 Self-supervised learning is a new type of unsupervised learning that leverages neural networks with technical and conceptual differences.
01:54 📑 "Foundation model" is a term for the collection of ideas involving pre-training on unsupervised data and adapting for downstream tasks.
02:20 🧠 Pre-training involves training neural networks on a large-scale unlabeled dataset to learn general features or representations.
03:17 💡 The shift towards self-supervised learning allows leveraging unlabeled data, enabling the use of much larger datasets for training.
04:14 🎯 Pre-training involves two steps: large-scale pre-training on unlabeled data and adaptation to downstream tasks with labeled data.
06:00 🚀 Adaptation involves fine-tuning a pre-trained model for specific tasks, often with few labeled examples.
06:59 🔁 The distinction between pre-training and adaptation is that pre-training focuses on intrinsic data structure, while adaptation focuses on specific tasks.
08:20 📊 Pre-training aims to create a foundation model with generic representations, enabling better performance on downstream tasks.
10:12 🏛️ "Foundation model" implies a general-purpose model with widespread applicability, serving as a basis for adaptation.
11:10 📝 Pre-training is done with a loss function optimized on unlabeled data, resulting in a foundation model.
15:18 🎓 Adaptation involves using labeled downstream task examples to fine-tune the model for specific tasks.
19:59 🎯 Linear probing is one adaptation approach involving training a linear classifier on the pre-trained feature representations.
24:43 ⚙️ Fine-tuning entails adapting both the model's parameters and linear classifier to the downstream task, initializing with pre-trained parameters.
27:26 🧠 Self-supervised learning methods
28:21 🌐 Pretraining approaches vary for different domains (vision, language)
29:17 🖼️ Supervised pretraining in vision using labeled data (ImageNet)
32:05 🔬 Applying pretrained models to new tasks and fine-tuning
34:46 💡 Unsupervised contrastive learning for pretraining
35:13 🔄 Data augmentation techniques for unsupervised learning
38:08 ➕ Designing loss functions to encourage similar representations for positive pairs
40:00 ➖ Designing loss functions to encourage dissimilar representations for random pairs
42:48 🎓 SIMCLR as an example of a contrastive learning framework
54:16 📚 Applying similar self-supervised pretraining to language models
55:31 🔤 Encoding text data for large language models
56:27 🧩 Self-supervised learning involves encoding data using binary vectors and more realistic language models.
57:22 📜 Language examples are sequences of words or documents; often extracted from large texts like Wikipedia.
58:49 🗃️ Tokens represent words, where common words are single tokens, while less frequent or longer words can be split into multiple tokens.
01:00:16 📊 Language models involve predicting probabilities of sequences; using chain rule to simplify probability calculations.
01:01:05 🔤 Neural networks, like transformers, are used to predict conditional probabilities of words.
01:05:12 📡 Transformers encode input sequences into output vectors; used to compute conditional probabilities.
01:06:52 🧭 Conditional probability models predict next word probabilities; softmax and linear transformations are often used.
01:08:18 📚 Cross-entropy loss measures the difference between predicted and actual probabilities.
01:19:57 📝 Adaptation methods include zero-shot learning (generation-based) and in-context learning (few-shot learning).
Made with HARPA AI
Wow having access to those classes is precious luck! Thank you Stanford!
Thanks for your comment, glad your enjoying these lectures!
Thank you very much for those very interesting videos - Would be great to have a link to the playlist for the course and the details in the description.
Thanks for sharing Stanford, would love to do PhD in ML at Stanford!!
Me too
Good luck! make it happen
I wish they made slides in order to save time spent writing stuff on the boards
分かりました、何が行われているか
私はデータモデルとしてＡＩの学習に協力をしていたのですね
何かのテストケースとは思っていました
私の感情の動きがなにかのためになればそれで良かったです