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Introduction to Deep Learning with PyTorch

Intermediate

4.1+

Updated 01/2025

Learn how to build your first neural network, adjust hyperparameters, and tackle classification and regression problems in PyTorch.

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PyTorchArtificial Intelligence4 hours16 videos48 exercises3,850 XP36,802Statement of Accomplishment

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Course Description

Understanding the power of Deep Learning

Deep learning is everywhere: in smartphone cameras, voice assistants, and self-driving cars. It has even helped discover protein structures and beat humans at the game of Go. Discover this powerful technology and learn how to leverage it using PyTorch, one of the most popular deep learning libraries.

Train your first neural network

First, tackle the difference between deep learning and "classic" machine learning. You will learn about the training process of a neural network and how to write a training loop. To do so, you will create loss functions for regression and classification problems and leverage PyTorch to calculate their derivatives.

Evaluate and improve your model

In the second half, learn the different hyperparameters you can adjust to improve your model. After learning about the different components of a neural network, you will be able to create larger and more complex architectures. To measure your model performances, you will leverage TorchMetrics, a PyTorch library for model evaluation.

Upon completion, you will be able to leverage PyTorch to solve classification and regression problems on both tabular and image data using deep learning. A vital capability for experienced data professionals looking to advance their careers.

Prerequisites

Supervised Learning with scikit-learn Introduction to NumPy Python Toolbox

1

Introduction to PyTorch, a Deep Learning Library

Introduction to deep learning with PyTorch

Getting started with PyTorch tensors

Checking and adding tensors

Creating our first neural network

Your first neural network

Stacking linear layers

Discovering activation functions

Activate your understanding!

The sigmoid and softmax functions

2

Training Our First Neural Network with PyTorch

Running a forward pass

Building a binary classifier in PyTorch

From regression to multi-class classification

Using loss functions to assess model predictions

Creating one-hot encoded labels

Calculating cross entropy loss

Using derivatives to update model parameters

Accessing the model parameters

Updating the weights manually

Using the PyTorch optimizer

Writing our first training loop

Using the MSELoss

Writing a training loop

3

Neural Network Architecture and Hyperparameters

Discovering activation functions between layers

Implementing ReLU

Implementing leaky ReLU

Understanding activation functions

A deeper dive into neural network architecture

Counting the number of parameters

Manipulating the capacity of a network

Learning rate and momentum

Experimenting with learning rate

Experimenting with momentum

Layer initialization and transfer learning

Fine-tuning process

Freeze layers of a model

Layer initialization

4

Evaluating and Improving Models

A deeper dive into loading data

Using the TensorDataset class

From data loading to running a forward pass

Evaluating model performance

Writing the evaluation loop

Calculating accuracy using torchmetrics

Fighting overfitting

Experimenting with dropout

Understanding overfitting

Improving model performance

Implementing random search

Wrap-up video

Introduction to Deep Learning with PyTorch

Course
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*4.1

from 43 reviews

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Most recent
Top reviews

Bruce J.

27 days

It's very helpful to understand .

SHREYASH G.

2 months

This course paired with ChatGPT or Perplexity is absolutely fantastic to grasp concepts and start at a very basic level.

Zafar H.

3 months

the best

Marco O.

3 months

This is what every beginer AI Developer should know and start with.

Shu X.

3 months

I like this intro. It gives you a quick grasp of key ideas. The examples are very helpful!

"It's very helpful to understand ."

Bruce J.

"This course paired with ChatGPT or Perplexity is absolutely fantastic to grasp concepts and start at a very basic level."

SHREYASH G.

"the best"

Zafar H.

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