Python; Deep Learning with neural networks

Deepen your knowledge of Python to familiarize yourself with its subdomains and common algorithms.
Private session

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  • Duration: 3 days
  • Regular price: On request

Course outline

Reference : @Python Deep Learning with neural networks

Duration : 3 days

Prerequisites

Pré-requis pour suivre ce cours

Objectives

Objectifs du cours

Contents

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)
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
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
Representation learning
  • Autoencoders
  • Mutual information neural estimator
  • Deep Info Max
  • Contrastive Predictive Coding
Generative Models
  • Variational Autoencoders
  • GANs
  • Generation by reinforcement
  • Generation under condition
  • Style transfer
  • Evaluation of generative models
Advanced topics
  • Multi-task learning
  • Semi-supervised learning
  • Transfer learning
  • Debugging neural networks

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Pierre-Edouard Brondel
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