Deep Learning using Python.txt

1. Machine Learning – An Introduction
What is machine learning?
Different machine learning approaches
Supervised learning
Unsupervised learning
Reinforcement learning
Steps Involved in machine learning systems
Brief description of popular techniques/algorithms
Linear regression
Decision trees
K-means
Naïve Bayes
Support vector machines
The cross-entropy method
Neural networks
Deep learning
Applications in real life
A popular open source package

2. Neural Networks
Why neural networks?
Fundamentals
Neurons and layers
Different types of activation function
The back-propagation algorithm
Linear regression
Logistic regression
Back-propagation
Applications in industry
Signal processing
Medical
Autonomous car driving
Business
Pattern recognition
Speech production
Code example of a neural network for the function xor

3. Deep Learning Fundamentals
What is deep learning?
Fundamental concepts
Feature learning
Deep learning algorithms
Deep learning applications
Speech recognition
Object recognition and classification
GPU versus CPU
Popular open source libraries – an introduction
Theano
TensorFlow
Keras
Sample deep neural net code using Keras

4. Unsupervised Feature Learning
Autoencoders
Network design
Regularization techniques for autoencoders
Denoising autoencoders
Contractive autoencoders
Sparse autoencoders
Summary of autoencoders
Restricted Boltzmann machines
Hopfield networks and Boltzmann machines
Boltzmann machine
Restricted Boltzmann machine
Implementation in TensorFlow
Deep belief networks

5. Image Recognition
Similarities between artificial and biological models
Intuition and justification
Convolutional layers
Stride and padding in convolutional layers
Pooling layers
Dropout
Convolutional layers in deep learning
Convolutional layers in Theano
A convolutional layer example with Keras to recognize digits
A convolutional layer example with Keras for cifar10
Pre-training

6. Recurrent Neural Networks and Language Models
Recurrent neural networks
RNN — how to implement and train
Backpropagation through time
Vanishing and exploding gradients
Long short term memory
Language modeling
Word-based models
N-grams
Neural language models
Character-based model
Preprocessing and reading data
LSTM network
Training
Sampling
Example training
Speech recognition
Speech recognition pipeline
Speech as input data
Preprocessing
Acoustic model
Deep belief networks
Recurrent neural networks
CTC
Attention-based models
Decoding
End-to-end models

7. Deep Learning for Board Games
Early game playing AI
Using the min-max algorithm to value game states
Implementing a Python Tic-Tac-Toe game
Learning a value function
Training AI to master Go
Upper confidence bounds applied to trees
Deep learning in Monte Carlo Tree Search
Quick recap on reinforcement learning
Policy gradients for learning policy functions
Policy gradients in AlphaGo

8. Deep Learning for Computer Games
A supervised learning approach to games
Applying genetic algorithms to playing games
Q-Learning
Q-function
Q-learning in action
Dynamic games
Experience replay
Epsilon greedy
Atari Breakout
Atari Breakout random benchmark
Preprocessing the screen
Creating a deep convolutional network
Convergence issues in Q-learning
Policy gradients versus Q-learning
Actor-critic methods
Baseline for variance reduction
Generalized advantage estimator
Asynchronous methods
Model-based approaches

9. Anomaly Detection
What is anomaly and outlier detection?
Real-world applications of anomaly detection
Popular shallow machine learning techniques
Data modeling
Detection modeling
Anomaly detection using deep auto-encoders
H2O
Getting started with H2O
Examples
MNIST digit anomaly recognition
Electrocardiogram pulse detection

10. Building a Production-Ready Intrusion Detection System
What is a data product?
Training
Weights initialization
Parallel SGD using HOGWILD!
Adaptive learning
Rate annealing
Momentum
Nesterov's acceleration
Newton's method
Adagrad
Adadelta
Distributed learning via Map/Reduce
Sparkling Water
Testing
Model validation
Labeled Data
Unlabeled Data
Summary of validation
Hyper-parameters tuning
End-to-end evaluation
A/B Testing
A summary of testing
Deployment
POJO model export
Anomaly score APIs
A summary of deployment