PyTorch Basics
90 LearnersCourse for Beginner Level
1 modules
Interactive environment
20 chapters
AI Assistant
20 assignments
Shareable CertificateCourse content
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PyTorch Essentials
Learn the fundamental and advanced concepts needed to work with PyTorch efficiently. Gain a solid understanding tensors, including creation, operations, and reshaping. Explore the essentials of gradients, backpropagation, and linear regression before moving on to handling datasets. Master the skill needed build, train, and evaluate neural networks.
Neural Networks and Deep Learning Foundations with PyTorch
Learn the fundamental and advanced concepts needed to work with PyTorch efficiently. Gain a solid understanding tensors, including creation, operations, and reshaping. Explore the essentials of gradients, backpropagation, and linear regression before moving on to handling datasets. Master the skill needed build, train, and evaluate neural networks.
PyTorch Essentials for ML Engineer
Transition from high-level libraries to the flexibility of PyTorch. Master the building blocks of deep learning by working with Tensors, the core data structure of PyTorch. Explore automatic differentiation with gradients, implement backpropagation, and build complete neural networks from scratch. Learn to handle real-world datasets and manage the full training cycle – from weight initialization to model evaluation.
PyTorch Essentials
Learn the fundamental and advanced concepts needed to work with PyTorch efficiently. Gain a solid understanding tensors, including creation, operations, and reshaping. Explore the essentials of gradients, backpropagation, and linear regression before moving on to handling datasets. Master the skill needed build, train, and evaluate neural networks.
Neural Networks and Deep Learning Foundations with PyTorch
Learn the fundamental and advanced concepts needed to work with PyTorch efficiently. Gain a solid understanding tensors, including creation, operations, and reshaping. Explore the essentials of gradients, backpropagation, and linear regression before moving on to handling datasets. Master the skill needed build, train, and evaluate neural networks.
PyTorch Essentials for ML Engineer
Transition from high-level libraries to the flexibility of PyTorch. Master the building blocks of deep learning by working with Tensors, the core data structure of PyTorch. Explore automatic differentiation with gradients, implement backpropagation, and build complete neural networks from scratch. Learn to handle real-world datasets and manage the full training cycle – from weight initialization to model evaluation.
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