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Offered By: IBMSkillsNetwork

Build a Neural Network with PyTorch

In this course, you will be focusing on how PyTorch creates and Neural Network optimizes models. We will quickly iterate through different aspects of PyTorch Neural Networks, giving you strong foundations and all the prerequisites you need to build deep learning models. Designed for students and professionals interested in machine learning and deep learning, this course offers a comprehensive understanding of the theory and practical applications of building and deploying neural networks. Note, this course is a part of a PyTorch Learning Path, check Prerequisites section.

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AI0114EN

Course

Artificial Intelligence

129 Enrolled
(6)

At a Glance

In this course, you will be focusing on how PyTorch creates and Neural Network optimizes models. We will quickly iterate through different aspects of PyTorch Neural Networks, giving you strong foundations and all the prerequisites you need to build deep learning models. Designed for students and professionals interested in machine learning and deep learning, this course offers a comprehensive understanding of the theory and practical applications of building and deploying neural networks. Note, this course is a part of a PyTorch Learning Path, check Prerequisites section.

In this course, participants will dive into the fundamentals of neural networks and gain hands-on experience with PyTorch, one of the most popular frameworks for deep learning. Through a series of interactive lectures, coding exercises, and projects, students will develop a solid foundation in building and training neural networks. Throughout the course, participants will have the opportunity to apply their knowledge through hands-on coding exercises. By the end of the course, students will have the skills and confidence to build, train, and deploy neural networks using PyTorch, enabling them to tackle real-world machine learning challenges and contribute to the advancement of AI applications.

Syllabus

Module 1 - Neural Networks
  • Introduction to Networks
  • Network Shape: Depth vs Width
  • Back Propagation
  • Activation Functions
Module 2- Deep Networks
  • Dropout
  • Initialization
  • Batch Normalization
  • Other Optimization Methods

Prerequisites

Note: this course is a part of the PyTorch Learning Path requires the completion of the following courses:
  1. PyTorch: Tensor, Dataset and Data Augmentation
  2. Linear Regression with PyTorch
  3. Classification with PyTorch
Alternatively, the student must have a good understanding of PyTorch Tensors and DataSets, linear regression, and classification.

Skills Prior to Taking this Course

  • Basic knowledge of the Python programming language

Estimated Effort

7 Hours

Level

Beginner

Skills You Will Learn

Artificial Intelligence, PyTorch, Python

Language

English

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Instructors

Artem Arutyunov

Data Scientist

Hey, Artem here! I am excited about answering new challenges with data science, machine learning and especially Reinforcement Learning. Love helping people to learn, and learn myself. Studying Math and Stats at University of Toronto, hit me up if you are from there as well.

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Joseph Santarcangelo

Senior Data Scientist at IBM

Joseph has a Ph.D. in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

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This Course is part of the following Learning Paths