Big Data & Data Science training

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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