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Introduction to Deep Learning with PyTorch

18 reviews

Learn the power of deep learning in PyTorch. Build your first neural network, adjust hyperparameters, and tackle classification and regression problems.

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4 Hours16 Videos50 Exercises
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Course Description

Introduction to Deep Learning with PyTorch

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. 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.

Train your first neural network

First, this course tackles the difference between deep learning and "classic" machine learning and will introduce neural networks. 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 of this course, you will learn about 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. 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.
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  1. 1

    Introduction to PyTorch, a Deep Learning Library


    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.

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    Introduction to deep learning with PyTorch
    50 xp
    Machine learning vs. deep learning
    100 xp
    Creating tensors and accessing attributes
    100 xp
    Creating tensors from NumPy arrays
    100 xp
    Creating our first neural network
    50 xp
    Your first neural network
    100 xp
    Stacking linear layers
    100 xp
    Discovering activation functions
    50 xp
    Activate your understanding!
    50 xp
    The sigmoid and softmax functions
    100 xp
  2. 2

    Training Our First Neural Network with PyTorch

    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.

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  3. 3

    Neural Network Architecture and Hyperparameters

    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.

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  4. 4

    Evaluating and Improving Models

    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.

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In the following tracks

Deep Learning in PythonDeveloping Large Language ModelsMachine Learning Fundamentals with PythonMachine Learning Scientist with Python


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George Boorman
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Amy Peterson
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James Chapman

Audio Recorded By

Maham Khan's avatar
Maham Khan
Maham Khan HeadshotMaham Khan

Senior Data Scientist, YouView TV

Maham is a Data Scientist on a mission to make data skills accessible for everyone. She's worked on creating toolkits and exploring experimental applications of data science for urban analytics, disaster risk management, and climate change mitigation at the World Bank. She has a background in Experimental Psychology and Philosophy from the University of Oxford and Urban Data Science from NYU.
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Thomas Hossler HeadshotThomas Hossler

Senior Machine Learning Engineer

Thomas is passionate about AI, the environment, and education, and is always looking for new challenges. He specializes in computer vision, machine learning model training and deployment (cloud and edge), and data pipelines.
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Don’t just take our word for it

from 18 reviews
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  • James C.
    8 months

    Good starter class of Pytorch and Deep Learning

  • willem h.
    9 months

    Lot of things to overthink.

  • asad r.
    about 1 month

    It's was really good content covering all the areas.

  • Tom W.
    3 months

    Really good. But some knowledge in ML is useful and mathematics are not explained but just used (which makes sense when looking at the scope of the course)

  • Evgeni N.
    3 months

    This is an outstanding course and I learned a lot.

"Good starter class of Pytorch and Deep Learning"

James C.

"Lot of things to overthink."

willem h.

"It's was really good content covering all the areas."

asad r.

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