Deep Learning with Python and PyTorch, Certificate | Part time online | edX - online learning platform | United States
42 days
Duration
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About

EdX is an online learning platform trusted by over 12 million users offering the Deep Learning with Python and PyTorch Certificate in collaboration with IBMx. This course is the second part of a two-part course on how to develop Deep Learning models using Pytorch.

Visit the official programme website for more information

Overview

In order to be successful in completing this course, please ensure you are familiar with PyTorch Basics and have practical knowledge to apply it to Machine Learning. If you do not have this pre-requiste knowledge, it is highly recommended you complete the PyTorch Basics for Machine Learning course prior to starting this course.

This Deep Learning with Python and PyTorch course offered by EdX in partnership with IBMx 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.

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

Programme Structure

Courses include:

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

Key information

Duration

  • Part-time
    • 42 days
    • 2 hrs/week

Start dates & application deadlines

You can apply for and start this programme anytime.

Language

English

Delivered

Online
  • Self-paced

Academic requirements

We are not aware of any specific GRE, GMAT or GPA grading score requirements for this programme.

English requirements

We are not aware of any English requirements for this programme.

Other requirements

General requirements

Prerequisites:

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

Tuition Fee

To always see correct tuition fees
  • International

    Free
    Tuition Fee
    Based on the tuition of 0 USD for the full programme during 42 days.
  • National

    Free
    Tuition Fee
    Based on the tuition of 0 USD for the full programme during 42 days.
  • Add a Verified Certificate for $99 USD

  • Limited access:free

Funding

Studyportals Tip: Students can search online for independent or external scholarships that can help fund their studies. Check the scholarships to see whether you are eligible to apply. Many scholarships are either merit-based or needs-based.

Our partners

Deep Learning with Python and PyTorch
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edX - online learning platform

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