Deep Learning with python

Deepen your knowledge of Machine learning with deep learning modeling techniques.

Duration: 3 days

Prerequisites: Basics in python programming, Familiarity with ML concepts

Objectives: Hands-on deep learning techniques

Content

  1. Introduction
    • Basic concepts: (Fully connected neural networks, layers, forward propagation)
    • Nonlinear activation functions
    • Loss functions for supervised learning
    • Initialization and regularization
    • Backward propagation
    • Optimization and learning algorithms
    • Introduction to Pytorch
    • Use case: Optical Character Recognition (OCR)
  2. Convolutional Neural Networks (CNN) for image recognition
    • Motivation and key-concepts (local connectivity, weight sharing)
    • Convolution: kernels, filters and feature maps
    • Aggregation, downsampling and pooling
    • Popular architectures: VGG, ResNet, GoogleNet
    • Image pre-processing
    • Using a pre trained neural network
  3. Recurrent Neural Networks (RNN)
    • Motivation and key-concepts (windows size, memory, etc)
    • RNN most used architectures (LSTM, GRU), their losses and gradients
    • Discrete sequence: One hot encoding and embeddings
    • Application to classification
    • Application to sequence prediction
  4. Representation learning
    • Autoencoders
    • Mutual information neural estimator
    • Deep Info Max
    • Contrastive Predictive Coding
  5. Generative Models
    • Variational Autoencoders
    • GANs
    • Generation by reinforcement
    • Generation under condition
    • Style transfer
    • Evaluation of generative models
  6. Advanced topics
    • Multi-task learning
    • Semi-supervised learning
    • Transfer learning
    • Debugging neural networks
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