Sebastian Raschka

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

L10.4 L2 Regularization for Neural Nets

L16.4 A Convolutional Autoencoder in PyTorch -- Code Example

7.7 Stacking (L07: Ensemble Methods)

L6.4 Training ADALINE with PyTorch -- Code Example

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

4.10 Matplotlib (L04: Scientific Computing in Python)

L5.7 Training an Adaptive Linear Neuron (Adaline)

7.4 Boosting and AdaBoost (L07: Ensemble Methods)

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

L6.3 Automatic Differentiation in PyTorch -- Code Example

4.2 NumPy Array Construction and Indexing (L04: Scientific Computing in Python)

7.5 Gradient Boosting (L07: Ensemble Methods)

Scaling PyTorch Model Training With Minimal Code Changes

2.6 K-nearest neighbors in Python (L02: Nearest Neighbor Methods)

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

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

Understanding PyTorch Buffers

4.5 NumPy Advanced Indexing -- Memory Views and Copies (L04: Scientific Computing in Python)

L19.3 RNNs with an Attention Mechanism

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

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

L3.3 Vectorization in Python

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

5.3 Object Oriented Programming & Python Classes (L05: Machine Learning with Scikit-Learn)

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

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

L16.1 Dimensionality Reduction

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

Finetuning Open-Source LLMs

13.4.1 Recursive Feature Elimination (L13: Feature Selection)

13.0 Introduction to Feature Selection (L13: Feature Selection)

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

13.4.4 Sequential Feature Selection (L13: Feature Selection)

Insights from Finetuning LLMs with Low-Rank Adaptation

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

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

4.3 NumPy Array Math and Universal Functions (L04: Scientific Computing in Python)

L15.4 Backpropagation Through Time Overview

The Three Elements of PyTorch

L9.3.2 Multilayer Perceptron in PyTorch -- Code Example Part 2/3 (Jupyter Notebook)

L13.3 Convolutional Neural Network Basics

4.6 NumPy Random Number Generators (L04: Scientific Computing in Python)

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

L15.1: Different Methods for Working With Text Data

6.7 Code Example Implementing Decision Trees in Scikit-Learn (L06: Decision Trees)

L2.2 Multilayer Networks

L1.4 The Supervised Learning Workflow

L18.2: The GAN Objective

L17.4 Variational Autoencoder Loss Function