Self-driving cars, smartphones, search engines... Deep learning is now everywhere. Before you begin building complex models, you will become familiar with PyTorch, a deep learning framework. You will learn how to manipulate tensors, create PyTorch data structures, and build your first neural network in PyTorch.


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

Introduction to PyTorch, a Deep Learning Library

To train a neural network in PyTorch, you will first need to understand the job of a loss function. You will then realize that training a network requires minimizing that loss function, which is done by calculating gradients. You will learn how to use these gradients to update your model's parameters, and finally, you will write your first training loop.


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

Training Our First Neural Network with PyTorch

Hyperparameters are parameters, often chosen by the user, that control model training. The type of activation function, the number of layers in the model, and the learning rate are all hyperparameters of neural network training. Together, we will discover the most critical hyperparameters of a neural network and how to modify them.


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

Neural Network Architecture and Hyperparameters

Training a deep learning model is an art, and to make sure our model is trained correctly, we need to keep track of certain metrics during training, such as the loss or the accuracy. We will learn how to calculate such metrics and how to reduce overfitting using an image dataset as an example.


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

Evaluating and Improving Models

Water Potability

Face Mask Dataset

Deep learning is everywhere, from smartphone cameras to voice assistants or self-driving cars. In this course, you will discover this powerful technology and learn how to leverage it using PyTorch, one of the most popular deep learning libraries. By the end of this course, you will be able to leverage PyTorch to solve classification and regression problems on both tabular and image data using deep learning.

<h2>Understanding the power of Deep Learning</h2>
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.<br><br>
<h2>Train your first neural network</h2>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.<br><br><h2>Evaluate and improve your model</h2>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. <br><br>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.

Supervised Learning with scikit-learn

Introduction to NumPy

Python Toolbox

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

Writing a training loop

“Introduction to Deep Learning with PyTorch”

Introduction to Deep Learning with PyTorch

Chapter 1: Introduction to PyTorch, a Deep Learning Library

Chapter 2: Training Our First Neural Network with PyTorch

Chapter 3: Neural Network Architecture and Hyperparameters

Chapter 4: Evaluating and Improving Models

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