Explain and apply knowledge of Deep Neural Networks and related machine learning methods;
Know how to use Python, and Python libraries such as Numpy and Pandas along with the PyTorch library for Deep Learning applications;
Build Deep Neural Networks using PyTorch.
The course will teach you how to develop Deep Learning models using Pytorch while providing the necessary deep-learning background.
We’ll start off with PyTorch’s tensors and its Automatic Differentiation package. Then we’ll cover different Deep Learning models in each section, beginning with fundamentals such as Linear Regression and logistic/softmax regression.
We’ll then move on to Feedforward deep neural networks, the role of different activation functions, normalization and dropout layers.
In the final part of the course, we’ll focus on Convolutional Neural Networks and Transfer Learning (pre-trained models). Several other Deep Learning methods will also be covered.
Module 1 – Introduction to Pytorch
What’s Deep Learning and why Pytorch
1-D Tensors and useful Pytoch Functions
2-D Tensors and useful functions
Derivatives and Graphs in Pytorch
Module 2 – Linear Regression
Prediction 1D regression
Training 1D regression
Stochastic gradient descent, mini-batch gradient descent
Train, test, split and early stopping
Multiple Linear Regression
Module 3 - Classification
Training Logistic Regressions Part 1
Training Logistic Regressions Part 2
Module 4 - Neural Networks
Introduction to Networks
Network Shape Depth vs Width
Module 5 - Deep Networks
Other optimization methods
Module 6 - Computer Vision Networks
Python & Jupyter notebooks
Machine Learning concepts
Deep Learning concepts