Resources

• a-sections
• labs
• lectures

Topics

AE

• Lab 10: VAEs and GANs
• Lab 10: VAEs and GANs [Notebook]

Agglomerative

• Lab 3: Clustering [Notebook]
• Lab 3: Clustering

Anaconda

• Lab 1: Getting Started
• Lab 1: Getting Started [Notebook]

autoencoders

• Lab 10: VAEs and GANs
• Lab 10: VAEs and GANs [Notebook]
• Lab 7: AE
• Lab 7: AE [Notebook]
• Lecture 12: Autoencoders + Unet

Bayesian

• Lecture 6: Bayesian 1/3

Bayesian Analysis

• Lab 4: Bayesian Analysis (Extended) [Notebook]
• Lab 4: Bayesian Analysis
• Lab 4: Bayesian Analysis [Notebook]

BDRNN

• Lecture 14: RNNs 2 + Text 1

Bert

• Lecture 16: Language Models

Clustering

• Lab 3: Clustering [Notebook]
• Lab 3: Clustering
• Lecture 4: Unsupervised Learning - Clustering 1

CNNs

• Lab 6: CNNs-2
• Lab 6: CNNs-2 [Notebook]
• Lecture 11: CNNs 2/2
• Lab 5: CNNs-1
• Lab 5: CNNs-1 [Notebook]
• Lecture 10: CNNs 1/2

Convolution

• Lecture 11: CNNs 2/2

Echo State

• Advanced Section 3: RNN, Echo State

ELMo

• Lecture 16: Language Models

GAMs

• Lab 2: Smoothers & GAMs
• Lab 2: Smoothers & GAMs [Notebook]

GANs

• Lab 10: VAEs and GANs
• Lab 10: VAEs and GANs [Notebook]
• Lecture 19: GANs 2
• Lecture 18: VAE + Generative Models + GANs 1

generative adversarial networks

• Lab 10: VAEs and GANs
• Lab 10: VAEs and GANs [Notebook]

Generative Models

• Lecture 19: GANs 2
• Lecture 18: VAE + Generative Models + GANs 1

GRU

• Lab 9: RNNs 2
• Lab 9: Recurrent Neural Networks Part II [Notebook]

Hierarchical

• Lab 3: Clustering [Notebook]
• Lab 3: Clustering

JupyterHub

• Lab 1: Getting Started
• Lab 1: Getting Started [Notebook]

K-Means

• Lab 3: Clustering [Notebook]
• Lab 3: Clustering

Keras

• Lab 9: RNNs 2
• Lab 9: Recurrent Neural Networks Part II [Notebook]
• Lab 8: Recurrent Neural Networks [Notebook]
• Lab 8: Recurrent Neural Networks

KMeans

• Lab 3: Clustering [Notebook]
• Lab 3: Clustering

LSTM

• Lab 9: RNNs 2
• Lab 9: Recurrent Neural Networks Part II [Notebook]
• Lab 8: Recurrent Neural Networks [Notebook]
• Lecture 14: RNNs 2 + Text 1
• Lab 8: Recurrent Neural Networks

MCMC

• Lab 4: Bayesian Analysis (Extended) [Notebook]
• Lab 4: Bayesian Analysis
• Lab 4: Bayesian Analysis [Notebook]

model-based

• Lab 11: Reinforcement Learning
• Lab 11: Reinforcement Learning [Notebook]

MPL

• Lecture 10: CNNs 1/2

N-Gram

• Lecture 16: Language Models

Neural Networks

• Lecture 11: CNNs 2/2
• Lecture 10: CNNs 1/2

OpenAIgym

• Lab 11: Reinforcement Learning
• Lab 8: Recurrent Neural Networks

planning

• Lab 11: Reinforcement Learning
• Lab 11: Reinforcement Learning [Notebook]

policy estimation

• Lab 11: Reinforcement Learning
• Lab 11: Reinforcement Learning [Notebook]

reinforcement learning

• Lab 11: Reinforcement Learning
• Lab 11: Reinforcement Learning [Notebook]
• Advanced Section 5: RL
• Lecture 21: Reinforcement Learning 2
• Lecture 20: Reinforcement Learning 1

RL

• Lab 11: Reinforcement Learning
• Lab 11: Reinforcement Learning [Notebook]

RNN

• Lab 9: RNNs 2
• Lab 9: Recurrent Neural Networks Part II [Notebook]
• Lab 8: Recurrent Neural Networks [Notebook]
• Lab 8: Recurrent Neural Networks

RNNs

• Advanced Section 3: RNN, Echo State
• Lecture 14: RNNs 2 + Text 1
• Lecture 13: RNNs 1

Self-Supervised Learning

• Lecture 12: Autoencoders + Unet

Semantic Segmentation

• Advanced Section 2: Segmentation Techniques, YOLO, Unet and M-RCNN

Silhouette

• Lab 3: Clustering [Notebook]
• Lab 3: Clustering

Smoothers

• Lab 2: Smoothers & GAMs
• Lab 2: Smoothers & GAMs [Notebook]
• Lecture 1: Introduction + Smoothers and Additive 1/3

Splines

• Lecture 1: Introduction + Smoothers and Additive 1/3

Transfer Learning

• Advanced Section 1: ResNet, Dense-Net, res-Next and Inception and transfer learning

Transformers

• Lecture 16: Language Models

Unsupervised

• Lab 3: Clustering [Notebook]
• Lab 3: Clustering

VAE

• Lab 10: VAEs and GANs
• Lab 10: VAEs and GANs [Notebook]

variational autoencoders

• Lab 10: VAEs and GANs
• Lab 10: VAEs and GANs [Notebook]
• Lecture 18: VAE + Generative Models + GANs 1

Variational Inference

• Advanced Section 4: Variational Inference

Virtual Environments

• Lab 1: Getting Started
• Lab 1: Getting Started [Notebook]