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Introduction to Deep Learning with PyTorch

IntermediateSkill Level
4.8+
1,381 reviews
Updated 04/2025
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 videos49 Exercises3,900 XP53,853Statement 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-learnIntroduction to NumPyPython Toolbox
1

Introduction to PyTorch, a Deep Learning Library

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2

Neural Network Architecture and Hyperparameters

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3

Training a Neural Network with PyTorch

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4

Evaluating and Improving Models

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Introduction to Deep Learning with PyTorch
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*4.8
from 1,381 reviews
84%
15%
1%
0%
0%
  • Bharani
    about 2 hours

  • Abhishek
    about 3 hours

  • Mohammed
    about 6 hours

  • Khiêm
    about 8 hours

  • Carlo
    about 3 hours

    A little bit too soft on math. Adding at least formulas to the math functions shown would be nice!

  • Chethan
    about 7 hours

Bharani

Abhishek

Mohammed

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