• Length:
    6 Weeks
  • Effort:
    2–4 hours per week
  • Price:

    FREE
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  • Institution
  • Subject:
  • Level:
    Intermediate
  • Language:
    English
  • Video Transcript:
    English

Associated Programs:

Prerequisites

  • Python & Jupyter notebooks
  • Machine Learning concepts
  • Deep Learning concepts

About this course

Skip About this course
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.

What you'll learn

Skip What you'll learn
  • 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.
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

Meet your instructors

Joseph Santarcangelo
PhD., Data Scientist
IBM

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Who can take this course?

Unfortunately, learners from one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. EdX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.