Comprehensive Introduction to PyTorch | CodeSignal Learn

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Comprehensive Introduction to PyTorch

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4 courses

89 practices

11 hours

Master PyTorch with this learning path, designed for those experienced in Python and machine learning. From tensor basics to advanced modeling, it includes practical exercises focused on real-world datasets, such as the wine dataset, enhancing your deep learning skills through PyTorch.

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1,584 learners

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Verified skills you'll gain

INTERMEDIATE

Deep Learning and Neural Networks

ADVANCED

Model Validation and Selection

Tools you'll use

Python

PyTorch

Scikit-learn

Course 1

Introduction to PyTorch Tensors

5 lessons

25 practices

Step into the world of PyTorch, a leading library for deep learning and neural network development. This beginner-oriented course introduces the foundational building blocks of PyTorch, emphasizing tensors and their pivotal role in constructing neural networks. Through practical examples and exercises, you'll develop the skills to start building and experimenting with tensors in PyTorch.

Course 2

Building a Neural Network in PyTorch

5 lessons

24 practices

Embark on a journey to understand and build simple neural networks using PyTorch. This course explores neural networks, including essential concepts like layers, neurons, activation functions, and training a model. You’ll grasp these elements through progressive, interlocking code examples, culminating in the construction and evaluation of a simple neural network model for binary classification.

Course 3

Modeling the Wine Dataset with PyTorch

4 lessons

20 practices

Learn to model the Wine dataset with PyTorch in this detailed course. Start by preprocessing the data for PyTorch, then construct and train a multi-class classification model. Explore model evaluation with various metrics and plots to identify strengths and improvements. The course concludes with methods to save and deploy your model, maximizing PyTorch's features for practical application.

Course 4

PyTorch Techniques for Model Optimization

4 lessons

20 practices

Explore advanced PyTorch techniques to boost model performance. Learn about regularization, dropout to avoid overfitting, batch normalization for stable and quick training, and efficient training through learning rate scheduling. Also, discover how to save the best model with checkpointing. Each concise module offers practical skills to improve your machine learning projects.

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