Sebastian Raschka

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

L5.1 Online, Batch, and Minibatch Mode

L13.9.3 AlexNet in PyTorch

6.5 Gini & Entropy versus misclassification error (L06: Decision Trees)

L3.4 Perceptron in Python using NumPy and PyTorch

L13.7 CNN Architectures & AlexNet

L19.5.1 The Transformer Architecture

L1.1.1 Course Overview Part 1: Motivation and Topics

11.4 Statistical Tests for Algorithm Comparison (L11 Model Eval. Part 4)

7.1 Intro to ensemble methods (L07: Ensemble Methods)

4.4 NumPy Broadcasting (L04: Scientific Computing in Python)

Managing Sources of Randomness When Training Deep Neural Networks

L1.3.2 Broad Categories of ML Part 2: Unsupervised Learning

L11.2 How BatchNorm Works

L13.1 Common Applications of CNNs

6.4 Splitting criteria (L06: Decision Trees)

4.9 NumPy Linear Algebra Basics (L04: Scientific Computing in Python)

L14.4.2 All-Convolutional Network in PyTorch -- Code Example

L11.3 BatchNorm in PyTorch -- Code Example

L12.1 Learning Rate Decay

L14.3.1.1 VGG16 Overview

L13.9.2 Saving and Loading Models in PyTorch

L4.1 Tensors in Deep Learning

L8.7.1 OneHot Encoding and Multi-category Cross Entropy

L4.3 Vectors, Matrices, and Broadcasting

13.4.2 Feature Permutation Importance (L13: Feature Selection)

L7.0 GPU resources & Google Colab

L5.6 Understanding Gradient Descent

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

L9.4 Overfitting and Underfitting

L13.8 What a CNN Can See

L14.6.2 Transfer Learning in PyTorch -- Code Example

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

L6.2 Understanding Automatic Differentiation via Computation Graphs

L19.1 Sequence Generation with Word and Character RNNs

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

L4.5 A Fully Connected (Linear) Layer in PyTorch

4.7 Reshaping NumPy Arrays (L04: Scientific Computing in Python)

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

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)