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0:13:33

Understanding PyTorch Buffers

0:58:46

Developing an LLM: Building, Training, Finetuning

0:15:25

Scaling PyTorch Model Training With Minimal Code Changes

0:10:38

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

0:28:33

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

0:56:58

The Three Elements of PyTorch

0:14:59

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

0:23:36

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

0:30:00

13.4.4 Sequential Feature Selection (L13: Feature Selection)

0:27:38

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

0:16:56

13.4.2 Feature Permutation Importance (L13: Feature Selection)

0:28:52

13.4.1 Recursive Feature Elimination (L13: Feature Selection)

0:39:43

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

0:23:33

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

0:19:53

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

0:11:39

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

0:16:10

13.0 Introduction to Feature Selection (L13: Feature Selection)

1:28:26

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

0:34:39

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

0:09:54

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

0:09:03

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

0:22:36

L19.5.1 The Transformer Architecture

0:08:41

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

0:17:58

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