// 7 courses indexed
The course directory
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PyTorch: Fundamentals
This course introduces you to the core principles of deep learning through hands-on coding in PyTorch. You’ll start by learning how PyTorch represents data with tensors and how datasets and data loaders fit into the training process. Step by step, you’ll build and train neural networks, experiment with different architectures, and explore how models learn from examples. You’ll also learn how to monitor training progress, interpret results, and evaluate performance. By the end of the course, you’ll understand PyTorch’s workflow and be ready to design, train, and test your own neural network
All levels
Foundations and Core Concepts of PyTorch
Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this comprehensive course, you'll embark on a journey through the foundational elements and core concepts of PyTorch, one of the most popular deep learning frameworks. Starting with a detailed overview and system setup, you'll be guided through installing and configuring your environment to ensure a smooth learning experience. The course t
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Deep Learning: Advanced Backbones and Efficient GPU Training
Master advanced deep learning architectures and efficient training techniques using PyTorch Lightning, timm, ConvNeXt, Vision Transformers, RoPE, SwiGLU, RMSNorm, and Weights & Biases. This course equips you to design, train, and benchmark modern backbones on limited GPU hardware for real-world production use. Module 1 introduces modern backbone architectures, tracing the evolution from ResNets to ConvNeXt and Vision Transformers, covering patch embeddings, multi-head self-attention, and position encodings. Module 2 dives into training dynamics and stabilization techniques including RMSNorm,
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Deep Learning Foundations & Azure Environments
Production-grade deep learning starts with solid engineering foundations. This course builds the core PyTorch and Azure ML skills needed to build, train, and track deep learning models in enterprise environments. You'll implement feedforward neural networks from scratch, including forward and backward passes, loss functions, and optimizers, then move into custom PyTorch module design, efficient DataLoader pipelines, and mixed precision training with torch.compile. You'll configure Azure ML workspaces, compute clusters, and GPU targets using the Azure ML SDK v2, manage training jobs, and track
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Deep Learning with PyTorch
This course offers a comprehensive and practical introduction to deep learning using PyTorch, a leading open-source framework. Learners will develop a solid understanding of foundational concepts such as neural networks, activation functions, forward and backward propagation, and optimization algorithms. Through a structured progression, the course covers essential architectures including perceptrons, multi-layer networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) models, and Transformers. Learners will apply these models to real-wor
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Introduction to RNN and DNN
Artificial Intelligence is transforming industries by enabling machines to learn from data and make intelligent decisions. This course offers an in-depth exploration of Recurrent Neural Networks (RNN) and Deep Neural Networks (DNN), two pivotal AI technologies. You’ll start with the basics of RNNs and their applications, followed by an examination of DNNs, including their architecture and implementation using PyTorch. You will master building and deploying sophisticated AI models, develop RNN models for tasks like speech recognition and machine translation, understand and implement DNN archit
All levels