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

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