Deep Learning with Python and PyTorch

Provided by IBM
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Intermediate
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Learn how to use Python and its popular libraries such as NumPy and Pandas, as well as the PyTorch Deep Learning library. You'll then apply them to build Neural Networks and Deep Learning models.

Part of 1 program:
Course Format:Instructor-Led
Start Date:Mar 20, 2019

What you will 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.

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.

Before you start

  • Python & Jupyter notebooks
  • Machine Learning concepts
  • Deep Learning concepts
  • Instructor-Led: course contains assignments and exams that have specific due dates, and you complete the course within a defined time period.
  • Course ends: Mar 20, 2019

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