IBM
Introduction to Neural Networks and PyTorch
IBM

Introduction to Neural Networks and PyTorch

73,844 already enrolled

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Gain insight into a topic and learn the fundamentals.
4.4

(1,719 reviews)

Intermediate level

Recommended experience

Flexible schedule
Approx. 17 hours
Learn at your own pace
91%
Most learners liked this course
Gain insight into a topic and learn the fundamentals.
4.4

(1,719 reviews)

Intermediate level

Recommended experience

Flexible schedule
Approx. 17 hours
Learn at your own pace
91%
Most learners liked this course

What you'll learn

  • Job-ready PyTorch skills employers need in just 6 weeks

  • How to implement and train linear regression models from scratch using PyTorch’s functionalities

  • Key concepts of logistic regression and how to apply them to classification problems

  • How to handle data and train models using gradient descent for optimization 

Details to know

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Assessments

5 assignments

Taught in English

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This course is part of the IBM AI Engineering Professional Certificate
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There are 6 modules in this course

This module provides an overview of tensors and datasets. It will cover the appropriate methods to classify the type of data in a tensor and the type of tensor. You will learn the basics of 1D and 2-D tensors and the Numel method. Then you will learn to differentiate simple and partial derivatives. The module lists the different attributes that PyTorch uses in order to calculate a derivative. You will build a simple dataset class and object and a dataset for images. You will apply your learnings in labs and test your concepts in quizzes.

What's included

7 videos3 readings1 assignment6 app items3 plugins

This module describes linear regression. You will learn about classes, and how to build custom modules using nn.Modules to make predictions. Then you will explore the state_dict() method that returns a python dictionary. Then you will learn how to train the model, define a dataset and the noise assumption. You will further see how to minimize the cost and how to calculate loss using PyTorch. You will understand the Gradient Descent method and how to apply it on the cost function. You will learn to determine the bias and slope using the Gradient Descent method and define the cost surface. You will apply your learnings in labs and test your concepts in quizzes.

What's included

7 videos1 assignment3 app items2 plugins

This module covers implementing stochastic gradient descent using PyTorch’s data loader. Then you will explore batch processing techniques for efficient model training. You will compare Mini-Batch Gradient Descent and Stochastic Gradient Descent. Next, you will learn about Convergence Rate and using PyTorch’s optimization modules. Finally, you will learn the best practices for splitting data to ensure robust model evaluation and how hyperparameters are applied to train data. You will apply your learnings in labs and test your concepts in quizzes.

What's included

5 videos1 assignment4 app items1 plugin

In this module, you will learn to use the class linear to perform linear regression in multiple dimensions. In addition, you will learn about model parameters and how to calculate cost and perform gradient descent in PyTorch. You will learn to extend linear regression for multiple outputs. You will apply your learnings in labs and test your concepts in quizzes.

What's included

4 videos1 assignment4 app items

In this module, you will learn the fundamentals of linear classifiers and logistic regression. You will learn to use the nn.sequential model to build neural networks in PyTorch. You will implement logistic regression for prediction. The module also covers statistical concepts like Bernoulli Distribution and Maximum Likelihood Estimation underpinning logistic regression. In addition, you will understand and implement the cross entropy loss function. You will apply your learnings in labs and test your concepts in quizzes.

What's included

4 videos1 assignment3 app items

In this module, you will implement the final project applying all concepts learned. You will build a logistic regression model aimed at predicting the outcomes of League of Legends matches. Leveraging various in-game statistics, this project will utilize your knowledge of PyTorch, logistic regression, and data handling to create a robust predictive model.

What's included

2 readings1 peer review2 app items1 plugin

Instructor

Instructor ratings
4.5 (373 ratings)
Joseph Santarcangelo
IBM
33 Courses1,646,531 learners

Offered by

IBM

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