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

    FREE
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  • Institution
  • Subject:
  • Level:
    Intermediate
  • Language:
    English
  • Video Transcript:
    English
  • Course Type:
    Self-paced on your time

Associated Programs:

Prerequisites

  • Python & Jupyter notebooks
  • Machine Learning concepts
  • Deep Learning concepts
  • https://www.edx.org/course/pytorch-basics-for-machine-learning

About this course

Skip About this course

This course is the second part of a two-part course on how to develop Deep Learning models using Pytorch.

In the first course, you learned the basics of PyTorch; in this course, you will learn how to build deep neural networks in PyTorch. Also, you will learn how to train these models using state of the art methods. You will first review multiclass classification, learning how to build and train a multiclass linear classifier in PyTorch. This will be followed by an in-depth introduction on how to construct Feed-forward neural networks in PyTorch, learning how to train these models, how to adjust hyperparameters such as activation functions and the number of neurons.

You will then learn how to build and train deep neural networks—learning how to apply methods such as dropout, initialization, different types of optimizers and batch normalization. We will then focus on Convolutional Neural Networks, training your model on a GPU and Transfer Learning (pre-trained models). You will finally learn about dimensionality reduction and autoencoders. Including principal component analysis, data whitening, shallow autoencoders, deep autoencoders, transfer learning with autoencoders, and autoencoder applications.

Finally, you will test your skills in a final project.

What you'll learn

Skip What you'll learn
  • Apply knowledge of Deep Neural Networks and related machine learning methods
  • Build and Train Deep Neural Networks using PyTorch
  • Build Deep learning pipelines

Module 1 - Classification

  • Softmax Regression
  • Softmax in PyTorch Regression
  • Training Softmax in PyTorch Regression

Module 2 - Neural Networks

  • Introduction to Networks
  • Network Shape Depth vs Width
  • Back Propagation
  • Activation functions

Module 3 - Deep Networks

  • Dropout
  • Initialization
  • Batch normalization
  • Other optimization methods

Module 4 - Computer Vision Networks

  • Convolution
  • Max Polling
  • Convolutional Networks
  • Pre-trained Networks

Module 5 - Computer Vision Networks

  • Convolution
  • Max Pooling
  • Convolutional Networks
  • Training your model with a GPU
  • Pre-trained Networks

Module 6 Dimensionality reduction and autoencoders

  • Principle component analysis
  • Linear autoencoders
  • Autoencoders
  • Transfer learning
  • Deep Autoencoders

Module 7 -Independent Project

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.