Deep Learning with PyTorch Step-by-Step: Part I – Fundamentals

Module 1: Introduction to PyTorch & Deep Learning

⏳ 1 week

• Topics: Deep learning fundamentals, PyTorch ecosystem, CPU vs. GPU execution

• Hands-on: Install PyTorch, run a “Hello, World!” tensor example, and visualize operations

Module 2: Tensors, Autograd & Computation Graphs

⏳ 1 week

• Topics: Tensor operations, broadcasting, gradient tracking, computational graphs

• Hands-on: Compute gradients for simple functions and implement a manual optimizer

Module 3: Building Neural Networks with nn.Module

⏳ 1 week

• Topics: Layers, activations, model definitions, forward/backward methods

• Hands-on: Define and train a feedforward network on MNIST classification

Module 4: Training Loop, Loss & Optimization

⏳ 1 week

• Topics: Loss functions (CrossEntropy, MSE), optimizers (SGD, Adam), batching, and epochs

• Hands-on: Write a full training and validation loop, plot loss and accuracy curves

Module 5: Convolutional Neural Networks & Transfer Learning

⏳ 1 week

• Topics: Conv layers, pooling, pretrained models, fine-tuning strategies

• 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

• Topics: RNN, LSTM, GRU cells, sequence-to-sequence basics, teacher forcing

• Hands-on: Implement a character-level language model and generate text samples

Module 7: Model Deployment & Best Practices

⏳ 1 week

• Topics: Saving/loading models, TorchScript, ONNX export, reproducibility tips

• 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

• Topics: Project scoping, data pipelines, evaluation metrics, presentation

• Hands-on: Tackle a real-world problem—e.g., image segmentation or sentiment analysis—and present results

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