Senior Research Associate
@leriomaggio | ValerioMaggio | [email protected] |
git clone https://github.com/leriomaggio/deep-learning-pytorch.git
-
Part I: Introduction
-
Intro to Artificial Neural Networks
- Perceptron and MLP (
numpy
) - naive pure-Python implementation
- Perceptron and MLP (
-
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
andDataLoader
- Preparing Data for Experiments
- Training, Test & the torch way
- Validation and Cross Validation
- Data for Machine and Deep Learning
-
Part III: Supervised Learning
-
Part IV: Unsupervised Learning
-
... (more to come)
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