Build an LLM from Scratch 7: Instruction Finetuning

Build an LLM from Scratch 6: Finetuning for Classification

Build an LLM from Scratch 5: Pretraining on Unlabeled Data

Build an LLM from Scratch 4: Implementing a GPT model from Scratch To Generate Text

Build an LLM from Scratch 3: Coding attention mechanisms

Build an LLM from Scratch 2: Working with text data

Build an LLM from Scratch 1: Set up your code environment

Reinforcement Learning with Human Feedback (RLHF) in 4 minutes

LLMs: A Journey Through Time and Architecture

Building LLMs from the Ground Up: A 3-hour Coding Workshop

Understanding PyTorch Buffers

Developing an LLM: Building, Training, Finetuning

Managing Sources of Randomness When Training Deep Neural Networks

Insights from Finetuning LLMs with Low-Rank Adaptation

Finetuning Open-Source LLMs

Scaling PyTorch Model Training With Minimal Code Changes

L13.5 What's The Difference Between Cross-Correlation And Convolution?

Conditional Ordinal Regression for Neural Networks (CORN) With Examples in PyTorch

The Three Elements of PyTorch

Ratings and Rankings -- Using Deep Learning When Class Labels Have A Natural Order

13.4.5 Sequential Feature Selection -- Code Examples (L13: Feature Selection)

13.4.4 Sequential Feature Selection (L13: Feature Selection)

13.4.3 Feature Permutation Importance Code Examples (L13: Feature Selection)

13.4.2 Feature Permutation Importance (L13: Feature Selection)

13.4.1 Recursive Feature Elimination (L13: Feature Selection)

13.3.2 Decision Trees & Random Forest Feature Importance (L13: Feature Selection)

13.3.1 L1-regularized Logistic Regression as Embedded Feature Selection (L13: Feature Selection)

13.2 Filter Methods for Feature Selection -- Variance Threshold (L13: Feature Selection)

13.1 The Different Categories of Feature Selection (L13: Feature Selection)

13.0 Introduction to Feature Selection (L13: Feature Selection)

Introduction to Generative Adversarial Networks (Tutorial Recording at ISSDL 2021)

Designing Generative Adversarial Networks for Privacy-enhanced Face Recognition (Conference rec.)

L19.5.2.2 GPT-v1: Generative Pre-Trained Transformer

L19.5.2.4 GPT-v2: Language Models are Unsupervised Multitask Learners

L19.5.2.7: Closing Words -- The Recent Growth of Language Transformers

L19.5.2.6 BART: Combining Bidirectional and Auto-Regressive Transformers

L19.5.2.5 GPT-v3: Language Models are Few-Shot Learners

L19.6 DistilBert Movie Review Classifier in PyTorch -- Code Example

L19.5.2.3 BERT: Bidirectional Encoder Representations from Transformers

L19.5.2.1 Some Popular Transformer Models: BERT, GPT, and BART -- Overview

L19.5.1 The Transformer Architecture

L19.4.3 Multi-Head Attention

L19.4.2 Self-Attention and Scaled Dot-Product Attention

L19.4.1 Using Attention Without the RNN -- A Basic Form of Self-Attention

L19.3 RNNs with an Attention Mechanism

L19.2.1 Implementing a Character RNN in PyTorch (Concepts)

L19.2.2 Implementing a Character RNN in PyTorch --Code Example

L19.1 Sequence Generation with Word and Character RNNs

L19.0 RNNs & Transformers for Sequence-to-Sequence Modeling -- Lecture Overview

L18.6: A DCGAN for Generating Face Images in PyTorch -- Code Example

L18.5: Tips and Tricks to Make GANs Work

L18.4: A GAN for Generating Handwritten Digits in PyTorch -- Code Example

L18.3: Modifying the GAN Loss Function for Practical Use

L18.2: The GAN Objective

L18.1: The Main Idea Behind GANs

L18.0: Introduction to Generative Adversarial Networks -- Lecture Overview

L17.7 VAE Latent Space Arithmetic in PyTorch -- Making People Smile (Code Example)

L17.6 A Variational Autoencoder for Face Images in PyTorch -- Code Example

L17.5 A Variational Autoencoder for Handwritten Digits in PyTorch -- Code Example

L17.4 Variational Autoencoder Loss Function