01:46:04
Build an LLM from Scratch 7: Instruction Finetuning
02:15:29
Build an LLM from Scratch 6: Finetuning for Classification
02:36:44
Build an LLM from Scratch 5: Pretraining on Unlabeled Data
01:45:37
Build an LLM from Scratch 4: Implementing a GPT model from Scratch To Generate Text
02:15:41
Build an LLM from Scratch 3: Coding attention mechanisms
01:28:01
Build an LLM from Scratch 2: Working with text data
21:02
Build an LLM from Scratch 1: Set up your code environment
04:06
Reinforcement Learning with Human Feedback (RLHF) in 4 minutes
19:44
LLMs: A Journey Through Time and Architecture
02:45:10
Building LLMs from the Ground Up: A 3-hour Coding Workshop
13:33
Understanding PyTorch Buffers
58:46
Developing an LLM: Building, Training, Finetuning
23:11
Managing Sources of Randomness When Training Deep Neural Networks
13:49
Insights from Finetuning LLMs with Low-Rank Adaptation
20:05
Finetuning Open-Source LLMs
15:25
Scaling PyTorch Model Training With Minimal Code Changes
10:38
L13.5 What's The Difference Between Cross-Correlation And Convolution?
28:33
Conditional Ordinal Regression for Neural Networks (CORN) With Examples in PyTorch
56:58
The Three Elements of PyTorch
14:59
Ratings and Rankings -- Using Deep Learning When Class Labels Have A Natural Order
23:36
13.4.5 Sequential Feature Selection -- Code Examples (L13: Feature Selection)
30:00
13.4.4 Sequential Feature Selection (L13: Feature Selection)
27:38
13.4.3 Feature Permutation Importance Code Examples (L13: Feature Selection)
16:56
13.4.2 Feature Permutation Importance (L13: Feature Selection)
28:52
13.4.1 Recursive Feature Elimination (L13: Feature Selection)
39:43
13.3.2 Decision Trees & Random Forest Feature Importance (L13: Feature Selection)
23:33
13.3.1 L1-regularized Logistic Regression as Embedded Feature Selection (L13: Feature Selection)
19:53
13.2 Filter Methods for Feature Selection -- Variance Threshold (L13: Feature Selection)
11:39
13.1 The Different Categories of Feature Selection (L13: Feature Selection)
16:10
13.0 Introduction to Feature Selection (L13: Feature Selection)
01:28:26
Introduction to Generative Adversarial Networks (Tutorial Recording at ISSDL 2021)
34:39
Designing Generative Adversarial Networks for Privacy-enhanced Face Recognition (Conference rec.)
09:54
L19.5.2.2 GPT-v1: Generative Pre-Trained Transformer
09:03
L19.5.2.4 GPT-v2: Language Models are Unsupervised Multitask Learners
06:10
L19.5.2.7: Closing Words -- The Recent Growth of Language Transformers
10:15
L19.5.2.6 BART: Combining Bidirectional and Auto-Regressive Transformers
06:41
L19.5.2.5 GPT-v3: Language Models are Few-Shot Learners
17:58
L19.6 DistilBert Movie Review Classifier in PyTorch -- Code Example
18:31
L19.5.2.3 BERT: Bidirectional Encoder Representations from Transformers
08:41
L19.5.2.1 Some Popular Transformer Models: BERT, GPT, and BART -- Overview
22:36
L19.5.1 The Transformer Architecture
07:37
L19.4.3 Multi-Head Attention
16:09
L19.4.2 Self-Attention and Scaled Dot-Product Attention
16:11
L19.4.1 Using Attention Without the RNN -- A Basic Form of Self-Attention
22:19
L19.3 RNNs with an Attention Mechanism
09:20
L19.2.1 Implementing a Character RNN in PyTorch (Concepts)
25:57
L19.2.2 Implementing a Character RNN in PyTorch --Code Example
17:44
L19.1 Sequence Generation with Word and Character RNNs
03:05
L19.0 RNNs & Transformers for Sequence-to-Sequence Modeling -- Lecture Overview
12:43
L18.6: A DCGAN for Generating Face Images in PyTorch -- Code Example
17:14
L18.5: Tips and Tricks to Make GANs Work
22:46
L18.4: A GAN for Generating Handwritten Digits in PyTorch -- Code Example
18:50
L18.3: Modifying the GAN Loss Function for Practical Use
26:26
L18.2: The GAN Objective
10:43
L18.1: The Main Idea Behind GANs
05:15
L18.0: Introduction to Generative Adversarial Networks -- Lecture Overview
11:54
L17.7 VAE Latent Space Arithmetic in PyTorch -- Making People Smile (Code Example)
10:06
L17.6 A Variational Autoencoder for Face Images in PyTorch -- Code Example
23:13
L17.5 A Variational Autoencoder for Handwritten Digits in PyTorch -- Code Example
12:16
L17.4 Variational Autoencoder Loss Function
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I'm also the author of "Build a Large Language Model From Scratch" (mng.bz/amjo), Machine Learning Q and AI (nostarch.com/machine-learning-q-and-ai), and other books (sebastianraschka.com/books/).
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