Course
Introduction to Deep Learning with PyTorch
Intermediate
Updated 03/2025Start Course for Free
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PyTorchArtificial Intelligence4 hours16 videos49 Exercises3,900 XP46,135Statement 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 Toolbox1
Introduction to PyTorch, a Deep Learning Library
2
Neural Network Architecture and Hyperparameters
3
Training a Neural Network with PyTorch
4
Evaluating and Improving Models
Introduction to Deep Learning with PyTorch
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