Introduction and reminders about Machine Learning

• Machine Learning and its Applications
• Deep learning and its applications

The basics of TensorFlow

• Tensors
• Variables vs. Placeholders
• Runtime graph
• Session (session, interactiveSession)
• First program in TensorFlow
• Manipulation of data   * Visualization of data with Tensorboard
• TensorFlow API: Tf.contrib.learn
• Running on CPUs vs. GPUs
• Running on Cluster
• Going into production with TensorServing
• Lab 1: Manipulation of TensorFlow Basics

Machine Learning with TensorFlow

Regression with TensorFlow

• Use case: Prediction of sale prices of houses
• Linear regression, multiple
• Optimization
• Comparison of models
• Lab 2: Regression

Classification with TensorFlow

• Use case: Image classification - MNIST dataset
• Logistic Regression, Random Forests, ...
• Compare models   * Lab 3: Classification

Deep learning

Perceptron and multilayer neural networks

• Motivation
• Use case: Image classification - MNIST dataset
• Principle and operation
• Lab 4: Classification with multilayer networks

Convolutional Neural Networks (CNN)

• Motivation
• Use case: Image classification - MNIST dataset
• Principle and operation
• Lab 5: Image recognition with convolutional networks

Recurrent Neural Networks (RNN)

• Motivation
• Use case: Natural language processing
• Long Short-Term Memory (LSTM)
• Recurrent Neural Networks (RNN)
• Lab 6: Natural Language Processing (NLP) with Recurring Networks

Restricted Boltzmann Machine and Neural Networks Autoencoders

• Motivation
• Use case: Dimension reduction
• Restricted Boltzmann Machine (RBM)
• Deep Belief Network (DBN)
• Lab 7: Dimension reduction with autoencoders