Interactive Course

Introduction to Deep Learning with PyTorch

Learn to create deep learning models with the PyTorch library.

4 hours
17 Videos
53 Exercises
7,071 Participants
4,300 XP

Create Your Free Account

By pressing Continue you accept the Terms of Use and Privacy Policy. You also accept that you are aware that your data will be stored outside of the EU and that you are above the age of 16.

Loved by learners at thousands of top companies:

Course Description

Neural networks have been at the forefront of Artificial Intelligence research during the last few years, and have provided solutions to many difficult problems like image classification, language translation or Alpha Go. PyTorch is one of the leading deep learning frameworks, being at the same time both powerful and easy to use. In this course you will use PyTorch to first learn about the basic concepts of neural networks, before building your first neural network to predict digits from MNIST dataset. You will then learn about convolutional neural networks, and use them to build much more powerful models which give more accurate results. You will evaluate the results and use different techniques to improve them. Following the course, you will be able to delve deeper into neural networks and start your career in this fascinating field.

Introduction to Deep Learning with PyTorch

In this first chapter, we introduce basic concepts of neural networks and deep learning using PyTorch library.

In this third chapter, we introduce convolutional neural networks, learning how to train them and how to use them to make predictions.

In this second chapter, we delve deeper into Artificial Neural Networks, learning how to train them with real datasets.

In this last chapter, we learn how to make neural networks work well in practice, using concepts like regularization, batch-normalization and transfer learning.

1
Introduction to PyTorch
Free

In this first chapter, we introduce basic concepts of neural networks and deep learning using PyTorch library.
View Chapter Details Play Chapter Now
Artificial Neural Networks

In this second chapter, we delve deeper into Artificial Neural Networks, learning how to train them with real datasets.
View Chapter Details Play Chapter Now
Convolutional Neural Networks (CNNs)

In this third chapter, we introduce convolutional neural networks, learning how to train them and how to use them to make predictions.
View Chapter Details Play Chapter Now
Using Convolutional Neural Networks

In this last chapter, we learn how to make neural networks work well in practice, using concepts like regularization, batch-normalization and transfer learning.
View Chapter Details Play Chapter Now

What do other learners have to say?

“I've used other sites, but DataCamp's been the one that I've stuck with.”

Devon Edwards Joseph

Lloyd's Banking Group

“DataCamp is the top resource I recommend for learning data science.”

Louis Maiden

Harvard Business School

“DataCamp is by far my favorite website to learn from.”

Ronald Bowers

Decision Science Analytics @ USAA

Ismail Elezi

Researcher PHD Student at Ca' Foscari University of Venice

I am a third year PhD Student of Deep Learning, supervised by professor Marcello Pelillo at Ca' Foscari, University of Venice. During my PhD, I did an exchange period at ZHAW Datalab (Switzerland) working with professor Thilo Stadelmann. From January on, I am visiting professor's Laura Leal-Taixe lab in Technical University of Munich.

Tracks

Deep Learning in Python

Collaborators

Hadrien Lacroix
Hillary Green-Lerman

If you type... We will search for...

Press enter to see all search results

Courses

Tracks

Instructors

Learning Experience

Business Features

Academic Features

Introduction to Deep Learning with PyTorch

Create Your Free Account

Loved by learners at thousands of top companies:

Course Description

Introduction to PyTorch

Introduction to PyTorch

Creating tensors in PyTorch

Matrix multiplication

Forward propagation

Forward pass

Backpropagation by auto-differentiation

Backpropagation by hand

Backpropagation using PyTorch

Calculating gradients in PyTorch

Introduction to Neural Networks

Your first neural network

Your first PyTorch neural network

Convolutional Neural Networks (CNNs)

Convolution operator

Convolution operator - OOP way

Convolution operator - Functional way

Pooling operators

Max-pooling operator

Average-pooling operator

Convolutional Neural Networks

Your first CNN - __init__ method

Your first CNN - forward() method

Training Convolutional Neural Networks

Training CNNs

Using CNNs to make predictions

Artificial Neural Networks

Activation functions

Neural networks

ReLU activation

ReLU activation again

Loss functions

Calculating loss function by hand

Calculating loss function in PyTorch

Loss function of random scores

Preparing a dataset in PyTorch

Preparing MNIST dataset

Inspecting the dataloaders

Training neural networks

Building a neural network - again

Training a neural network

Using the network to make predictions

Using Convolutional Neural Networks

The sequential module

Sequential module - init method

Sequential module - forward() method

The problem of overfitting

Validation set

Detecting overfitting

Regularization techniques

L2-regularization

Dropout

Batch-normalization

Transfer learning

Finetuning a CNN

Torchvision module

Congratulations!

Introduction to PyTorch

Introduction to PyTorch

Creating tensors in PyTorch

Matrix multiplication

Forward propagation

Forward pass

Backpropagation by auto-differentiation

Backpropagation by hand

Backpropagation using PyTorch

Calculating gradients in PyTorch

Introduction to Neural Networks

Your first CNN - init method

Your first CNN - init method