Introduction to Neural Networks and PyTorch

Introduction to Neural Networks and PyTorch

This course is part of multiple programs.

Instructor: Joseph Santarcangelo

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

Skills you'll gain

Details to know

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Assessments

5 assignments

Taught in English

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There are 5 modules in this course

PyTorch is one of the top 10 highest paid skills in tech (Indeed). As the use of PyTorch for neural networks rockets, professionals with PyTorch skills are in high demand. This course is ideal for AI engineers looking to gain job-ready skills in PyTorch that will catch the eye of an employer.

AI developers use PyTorch to design, train, and optimize neural networks to enable computers to perform tasks such as image recognition, natural language processing, and predictive analytics. During this course, you’ll learn about 2-D Tensors and derivatives in PyTorch. You’ll look at linear regression prediction and training and calculate loss using PyTorch. You’ll explore batch processing techniques for efficient model training, model parameters, calculating cost, and performing gradient descent in PyTorch. Plus, you’ll look at linear classifiers and logistic regression. Throughout, you’ll apply your new skills in hands-on labs, and at the end, you’ll complete a project you can talk about in interviews. If you’re an aspiring AI engineer with basic knowledge of Python and mathematical concepts, who wants to get hands-on with PyTorch, enroll today and get set to power your AI career forward!

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

7 videosTotal 49 minutes

Course Introduction5 minutesPreview module
Overview of Tensors3 minutes
Tensors 1D13 minutes
Two-Dimensional Tensors9 minutes
Differentiation in PyTorch5 minutes
Simple Dataset7 minutes
Dataset4 minutes

3 readingsTotal 24 minutes

Course Introduction4 minutes
Course Overview10 minutes
Helpful Tips for Course Completion10 minutes

1 assignmentTotal 30 minutes

Module-level Graded Quiz: Tensor and Dataset30 minutes

6 app itemsTotal 165 minutes

Lab: Tensors 1D20 minutes
Lab: Two-Dimensional Tensors20 minutes
Lab: Differentiation in PyTorch25 minutes
Lab: Simple Dataset30 minutes
Lab: Dataset60 minutes
Lab: Torch Vision Datasets10 minutes

3 pluginsTotal 19 minutes

Errata: Tensors 1D2 minutes
Reading: Tensors 1D15 minutes
Errata: Two-Dimensional Tensors2 minutes

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

7 videosTotal 34 minutes

Linear Regression Prediction8 minutesPreview module
Linear Regression Training3 minutes
Loss3 minutes
Gradient Descent and Cost 4 minutes
Cost3 minutes
Linear Regression PyToch5 minutes
PyTorch Linear Regression Training Slope and Bias5 minutes

1 assignmentTotal 30 minutes

Module-level Graded Quiz: Linear Regression30 minutes

3 app itemsTotal 80 minutes

Lab: Linear Regression 1D: Prediction30 minutes
Lab Linear Regression 1D: Training One Parameter20 minutes
Lab Linear Regression: Prediction30 minutes

2 pluginsTotal 4 minutes

Errata: Linear Regression Training2 minutes
Errata: PyTorch Linear Regression Training Slope and Bias2 minutes

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

5 videosTotal 20 minutes

Stochastic Gradient Descent6 minutesPreview module
Mini-Batch Gradient Descent3 minutes
Optimization in PyTorch3 minutes
Training, Validation, and Test Split4 minutes
Training, Validation, and Test Split PyTorch3 minutes

1 assignmentTotal 30 minutes

Module-level Graded Quiz: Linear Regression Pytorch Way30 minutes

4 app itemsTotal 120 minutes

Lab: Stochastic Gradient Descent and Data Loader 30 minutes
Mini-Batch Gradient Descent30 minutes
Lab: Optimization in PyTorch30 minutes
Lab: Training, Validation, and Test Split in PyTorch30 minutes

1 pluginTotal 2 minutes

Errata: Training, Validation, and Test Split PyTorch2 minutes

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

4 videosTotal 17 minutes

Multiple Linear Regression Prediction8 minutesPreview module
Multiple Linear Regression Training2 minutes
Linear Regression Multiple Outputs5 minutes
Multiple Output Linear Regression Training1 minute

1 assignmentTotal 30 minutes

Module-level Graded Quiz: Multiple Input Output Linear Regression30 minutes

4 app itemsTotal 65 minutes

Lab: Multiple Linear Regression Prediction15 minutes
Lab: Multiple Linear Regression Training15 minutes
Lab: Linear Regression Multiple Output15 minutes
Lab: Multiple Output Linear Regression Training20 minutes

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.