PyTorch Fundamentals
Learn PyTorch fundamentals from setup to neural networks, tensors, and optimization techniques for effective AI and ML project development.
Learn PyTorch fundamentals from setup to neural networks, tensors, and optimization techniques for effective AI and ML project development.
This comprehensive course provides a solid foundation in PyTorch, one of the most powerful deep learning frameworks available today. Starting with environment setup and configuration, students progress through fundamental AI and machine learning concepts before diving into the intricate details of deep learning. The curriculum covers essential topics including model performance evaluation, activation and loss functions, and optimization techniques. Participants learn to build neural networks from scratch, understanding every component from data preparation to the backpropagation process. The course explores tensor operations and computational graphs, culminating in hands-on PyTorch modeling exercises such as implementing linear regression and hyperparameter tuning. With a balance of theoretical knowledge and practical application, students develop the skills to tackle complex deep learning problems using PyTorch's powerful features. This course serves as an excellent entry point for tech professionals, data scientists, and AI enthusiasts looking to master PyTorch for deep learning projects.
4.2
(21 ratings)
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What you'll learn
Set up and configure a PyTorch environment for deep learning projects
Understand fundamental AI and machine learning concepts necessary for deep learning
Build neural networks from scratch with forward and backward propagation
Work with tensors and computational graphs in PyTorch
Implement linear regression models using PyTorch's modeling capabilities
Manage data effectively with datasets, dataloaders, and batch processing
Skills you'll gain
This course includes:
PreRecorded video
4 assignments
Access on Mobile, Tablet, Desktop
Limited Access access
Shareable certificate
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There are 7 modules in this course
This course provides a comprehensive introduction to PyTorch, covering the essential components needed to build effective deep learning models. It begins with system setup and environment configuration before establishing foundational knowledge in artificial intelligence and machine learning concepts. Students explore deep learning principles, including model performance evaluation, activation and loss functions, and optimization techniques. The course teaches neural network construction from scratch, covering data preparation, model initialization, forward and backward propagation, and training methods. Special attention is given to tensors and their relationship to computational graphs. The final sections focus on practical PyTorch implementation, including building linear regression models, working with datasets and dataloaders, batch processing, and model optimization through hyperparameter tuning. Throughout the curriculum, theoretical concepts are reinforced with coding exercises and hands-on applications, ensuring students develop both conceptual understanding and practical skills in PyTorch-based deep learning.
Course Overview and System Setup
Module 1 · 42 Minutes to complete
Machine Learning
Module 2 · 18 Minutes to complete
Deep Learning Introduction
Module 3 · 49 Minutes to complete
Model Evaluation
Module 4 · 19 Minutes to complete
Neural Network from Scratch
Module 5 · 1 Hours to complete
Tensors
Module 6 · 22 Minutes to complete
PyTorch Modeling Introduction
Module 7 · 2 Hours to complete
Instructor
Enhancing IT Education Through Expert-Led Learning
Packt Course Instructors are dedicated to delivering high-quality educational content across a wide range of IT topics, offering over 5,000 eBooks and courses designed to improve student outcomes in technology-related fields. With a focus on practical knowledge, instructors leverage their industry expertise to create engaging learning experiences that help students grasp complex concepts and apply them effectively. The courses cover diverse subjects, from programming languages to advanced data analysis, ensuring that learners at all levels can find relevant resources to enhance their skills. Additionally, Packt emphasizes personalized learning paths and provides analytics tools for educators to monitor student engagement and success, making it a valuable partner in academic settings.
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