What will you learn in Deep Learning with PyTorch Step-by-Step: Part I – Fundamentals Course
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Understand the principles of deep learning and why PyTorch is widely used
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Manipulate tensors, compute gradients, and leverage automatic differentiation
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Build custom neural network architectures using
nn.Moduleandtorchvision
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Train, validate, and tune models with optimizers, loss functions, and learning rate schedules
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Apply convolutional and recurrent networks for computer vision and sequence tasks
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Deploy trained models and follow best practices for reproducibility and performance
Program Overview
Module 1: Introduction to PyTorch & Deep Learning
1 week
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Topics: Deep learning fundamentals, PyTorch ecosystem, CPU vs. GPU execution
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Hands-on: Install PyTorch, run a “Hello, World!” tensor example, and visualize operations
Module 2: Tensors, Autograd & Computation Graphs
1 week
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Topics: Tensor operations, broadcasting, gradient tracking, computational graphs
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Hands-on: Compute gradients for simple functions and implement a manual optimizer
Module 3: Building Neural Networks with nn.Module
1 week
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Topics: Layers, activations, model definitions, forward/backward methods
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Hands-on: Define and train a feedforward network on MNIST classification
Module 4: Training Loop, Loss & Optimization
1 week
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Topics: Loss functions (CrossEntropy, MSE), optimizers (SGD, Adam), batching, and epochs
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Hands-on: Write a full training and validation loop, plot loss and accuracy curves
Module 5: Convolutional Neural Networks & Transfer Learning
1 week
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Topics: Conv layers, pooling, pretrained models, fine-tuning strategies
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Hands-on: Build a CNN for CIFAR-10, then fine-tune ResNet on a custom image dataset
Module 6: Recurrent Networks & Sequence Modeling
1 week
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Topics: RNN, LSTM, GRU cells, sequence-to-sequence basics, teacher forcing
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Hands-on: Implement a character-level language model and generate text samples
Module 7: Model Deployment & Best Practices
1 week
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Topics: Saving/loading models, TorchScript, ONNX export, reproducibility tips
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Hands-on: Export a trained model to TorchScript and run inference in a standalone script
Module 8: Capstone Project – End-to-End Deep Learning
1 week
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Topics: Project scoping, data pipelines, evaluation metrics, presentation
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Hands-on: Tackle a real-world problem—e.g., image segmentation or sentiment analysis—and present results
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Job Outlook
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Deep learning with PyTorch is in high demand for roles like ML Engineer, Research Scientist, and AI Developer
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Industries include healthcare imaging, autonomous vehicles, NLP-driven services, and recommendation systems
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Salaries for entry-level positions start around $90,000, rising to $150,000+ for experienced practitioners
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Mastery of PyTorch fundamentals opens paths to advanced research and specialized AI roles
Explore More Learning Paths
Advance your deep learning and AI skills with these hand-picked courses designed to help you master neural networks, PyTorch, and real-world machine learning applications.
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Last verified: March 12, 2026
