Resources
Topics
Adam
Adveserial
ae
AlexNet
AMSGrad
autoencoder
Autoencoders
- Lab 10: Autoencoders and Variational Autoencoders [Notebook]
- Lab 10: Autoencoders and Variational Autoencoders [Notebook]
- Lecture 18 Notebook [Notebook]
Autoncoders
AWS
batch normalization
batch size
- Lab 3: Optimization of Neural Networks
- Lab 3: Optimization of Neural Networks [Notebook]
- Lab 3: Optimization of Neural Networks [Notebook]
Bayes
bayesian
- Lab 8: Bayesian Analysis using pyjags (+ Reinforcement Learning with gym)
- Lab 9: Latent Dirichlet Allocation (LDA)
Bellman Equation
Biderectional RNN
callbacks
- Lab 3: Optimization of Neural Networks
- Lab 3: Optimization of Neural Networks [Notebook]
- Lab 3: Optimization of Neural Networks [Notebook]
cgan
- Advanced Section: Generative Adversarial Networks [Notebook]
- Advanced Section 8: Generative Adversarial Networks
Clustering
CNN
- Lab 5: CNNs [Notebook]
- Lecture 9: CNN-2
- Lecture 9 Notebook [Notebook]
- Lecture 8: CNN-1
CNNs
Conda
Content image
convolutional neural net
- Lab 5: CNNs [Notebook]
Convolutional Neural Network
- Lecture 9: CNN-2
- Lecture 9 Notebook [Notebook]
- Lecture 8: CNN-1
Cross-entropy
cross validation
- Lab 3: Optimization of Neural Networks
- Lab 3: Optimization of Neural Networks [Notebook]
- Lab 3: Optimization of Neural Networks [Notebook]
decoder
DeconvNet
Deep Reinforcement Learning
Deep RNN
DeepDream
DenseNets
Domain adaptation
dropout
Earth Mover's distance
embedding
- Lecture 18: Autoencoders
- Lecture 9 Notebook [Notebook]
encoding
environments
Explodding Gradients
exponential weights
Face recognition
Fast R-CNN
Faster R-CNN
FPN
GAMs
- Lab 2: Smooths and GAMs
- Lab 2: Smooths and GAMs [Notebook]
- Lab 2: Smooths and GAMs [Notebook]
gan
- Advanced Section: Generative Adversarial Networks [Notebook]
- Lab 11: Generative Adversarial Networks
- Lab 11: Generative Adversarial Networks [Notebook]
- Lab 11: Generative Adversarial Networks [Notebook]
- Advanced Section 8: Generative Adversarial Networks
- Lecture 20: GANS
- Lecture 21: GANS
GANS
Gap Statistic
Generated image
generative adversarial networks
- Advanced Section: Generative Adversarial Networks [Notebook]
- Advanced Section 8: Generative Adversarial Networks
Generative Adveserial Network
Generative models
gradient descent
GRU
gym
- Lab 8: Bayesian Analysis using pyjags (+ Reinforcement Learning with gym)
- Lab 8: Bayesian Analysis using pyjags (+Reinforcement Learning using gym) [Notebook]
- Lab 9: Latent Dirichlet Allocation (LDA)
image preprocessing
- Lab 5: CNNs [Notebook]
Introduction
jags
keras
- Advanced Section: Generative Adversarial Networks [Notebook]
- Lab 11: Generative Adversarial Networks
- Lab 11: Generative Adversarial Networks [Notebook]
- Lab 11: Generative Adversarial Networks [Notebook]
- Advanced Section 8: Generative Adversarial Networks
- Lab 6: Recurrent Neural Networks
- Lab 6: Recurrent Neural Networks [Notebook]
- Lab 6: Recurrent Neural Networks [Notebook]
- Lab 5: Convolutional Neural Networks
- Lab 3: Optimization of Neural Networks
- Lab 3: Optimization of Neural Networks [Notebook]
- Lab 3: Optimization of Neural Networks [Notebook]
keras-viz
- Lab 5: CNNs [Notebook]
Kmeans
learning rate
- Lab 3: Optimization of Neural Networks
- Lab 3: Optimization of Neural Networks [Notebook]
- Lab 3: Optimization of Neural Networks [Notebook]
LeNet
logistics
LSTM
- Lab 6: Recurrent Neural Networks
- Lab 6: Recurrent Neural Networks [Notebook]
- Lab 6: Recurrent Neural Networks [Notebook]
- Lecture 11: RNN-2
Markov Process
Mask R-CNN
MLP
MNIST
- Lab 11: Generative Adversarial Networks
- Lab 11: Generative Adversarial Networks [Notebook]
- Lab 11: Generative Adversarial Networks [Notebook]
momentum
- Lab 3: Optimization of Neural Networks
- Lab 3: Optimization of Neural Networks [Notebook]
- Lab 3: Optimization of Neural Networks [Notebook]
- Advanced Section 1: Optimization/Dropout
moving average
Neural style transfer
NLP
- Lecture 9 Notebook [Notebook]
OpenAIgym
Optimal transport
optimization
optimizers
- Lab 3: Optimization of Neural Networks
- Lab 3: Optimization of Neural Networks [Notebook]
- Lab 3: Optimization of Neural Networks [Notebook]
pyjags
- Lab 8: Bayesian Analysis using pyjags (+ Reinforcement Learning with gym)
- Lab 9: Latent Dirichlet Allocation (LDA)
Python
Q-learning
R
- Lab 8: Bayesian Analysis using pyjags (+ Reinforcement Learning with gym)
- Lab 8: Bayesian Analysis using pyjags (+Reinforcement Learning using gym) [Notebook]
- Lab 9: Latent Dirichlet Allocation (LDA)
- Lab 2: Smooths and GAMs
- Lab 2: Smooths and GAMs [Notebook]
- Lab 2: Smooths and GAMs [Notebook]
- Lab 1: Setting up environment
- Lab 1: R set up [Notebook]
R-CNN
Receptive Field
Reinforcement learning
- Advanced Section 6: Deep Reinforcement Learning
- Lab 8: Bayesian Analysis using pyjags (+ Reinforcement Learning with gym)
- Lab 9: Latent Dirichlet Allocation (LDA)
- Lecture 14: Reinforcement Learning
representation learning
Reservoir Computing
ResNets
RL
RMSprop
RNN
- Lab 6: Recurrent Neural Networks
- Lab 6: Recurrent Neural Networks [Notebook]
- Lab 6: Recurrent Neural Networks [Notebook]
- Lecture 11: RNN-2
- Lecture 10: RNN-1
RNNs
RPN
saliency
- Lab 5: CNNs [Notebook]
Saliency maps
sgd
smooths
- Lab 2: Smooths and GAMs
- Lab 2: Smooths and GAMs [Notebook]
- Lab 2: Smooths and GAMs [Notebook]
splines
- Lab 2: Smooths and GAMs
- Lab 2: Smooths and GAMs [Notebook]
- Lab 2: Smooths and GAMs [Notebook]
SSD
Style image
tokenize
- Lecture 9 Notebook [Notebook]
transfer learning
vae
- Lab 10: Variational Autoencoders
- Lab 10: Autoencoders and Variational Autoencoders [Notebook]
- Lab 10: Autoencoders and Variational Autoencoders [Notebook]
Vanishing Gradients
Variational Autoencoders
- Lab 10: Autoencoders and Variational Autoencoders [Notebook]
- Lab 10: Autoencoders and Variational Autoencoders [Notebook]
- Lecture 19: Variational Autoencoders
Wasserstein
wgan
- Advanced Section: Generative Adversarial Networks [Notebook]
- Advanced Section 8: Generative Adversarial Networks