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Deep Learning with Python and PyTorch

IBM via edX

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Overview

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.

Syllabus

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
  • Data Loader
 
Module 2 – Linear Regression
  • Prediction 1D regression
  • Training 1D regression
  • Stochastic gradient descent, mini-batch gradient descent
  • Train, test, split and early stopping
  • Pytorch way
  • Multiple Linear Regression

Module 3 - Classification
  • Logistic Regression
  • Training Logistic Regressions Part 1
  • Training Logistic Regressions Part 2
  • Softmax Regression
 
Module 4 - Neural Networks
  • Introduction to Networks
  • Network Shape Depth vs Width
  • Back Propagation
  • Activation functions

Module 5 - Deep Networks
  • Dropout
  • Initialization
  • Batch normalization
  • Other optimization methods

Module 6 - Computer Vision Networks
  • Convolution
  • Max Polling
  • Convolutional Networks
  • Pre-trained Networks

Taught by

Joseph Santarcangelo

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