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Introduction to Deep Learning with PyTorch
4.8(4,557)87k studentsINTERMEDIATEEnglish
Korshub's take
Course Overview
PyTorch is a leading Python framework for deep learning research and production. This intermediate course guides you through writing custom training loops, calculating loss function derivatives, tuning neural architecture hyperparameters, and evaluating models with TorchMetrics on tabular and image data.
Who Should Enroll?
Designed for intermediate data scientists and machine learning engineers looking to master PyTorch neural network fundamentals and evaluation tools.
Key Takeaways
- PyTorch Training Loops: Construct custom training loops, loss functions, and autograd derivative calculations for regression and classification.
- Model Evaluation with TorchMetrics: Utilize TorchMetrics to systematically evaluate neural network performance across tasks.
- Architecture Optimization: Build deeper neural architectures and tune hyperparameters for tabular and image datasets.
About this course
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.
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