Deep Learning with

Author: Valerio Maggio

Senior Research Associate

Contacts:

@leriomaggio	ValerioMaggio	valerio.maggio@bristol.ac.uk

Clone the repository

git clone https://github.com/leriomaggio/deep-learning-pytorch.git

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Table of Contents

• Part I: Introduction

  • Intro to Artificial Neural Networks

    • Perceptron and MLP (numpy)
    • naive pure-Python implementation

  • Introduction to PyTorch

    • AutoGrad and Automatic Differentiation
    • Perceptron (torch)
    • Towards torch.nn: micrograd
    • Neural Network with Pytorch (torch.nn)

  • Brief overview of Deep Learning Frameworks

    • What's there around and why PyTorch

• Part II: Data and Dataset

  • Data for Machine and Deep Learning
    • torch.utils.data: DataSet and DataLoader
  • Preparing Data for Experiments
    • Training, Test & the torch way
    • Validation and Cross Validation

• Part III: Supervised Learning

• Part IV: Unsupervised Learning

• ... (more to come)

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Requirements

This tutorial requires the following packages:

• Python version 3.7

  • Python 3.4+ should be fine as well

• numpy

• scikit-learn

• torch

• torchvision

• matplotlib

Detailed (step-by-step) instructions on how to setup the Python virtual environment on your local machine are available here