PyTorch for Deep Learning Bootcamp Course Course

Module 1: Introduction to Deep Learning and PyTorch

⏳ 30 minutes

• Overview of AI and deep learning workflows.

• Installing PyTorch and setting up the development environment.

Module 2: PyTorch Basics and Tensor Operations

⏳ 45 minutes

• Understanding tensors, gradients, and automatic differentiation.

• Writing basic PyTorch programs and exploring tensor operations.

Module 3: Building Neural Networks

⏳ 60 minutes

• Constructing feedforward neural networks.

• Using nn.Module and custom model classes in PyTorch.

Module 4: Training and Evaluation Loops

⏳ 60 minutes

• Implementing training, validation, and testing loops.

• Working with optimizers and loss functions.

Module 5: Convolutional Neural Networks (CNNs)

⏳ 60 minutes

• Building CNNs for image classification.

• Applying CNNs to MNIST and CIFAR-10 datasets.

Module 6: Transfer Learning and Fine-Tuning

⏳ 60 minutes

• Leveraging pre-trained models like ResNet.

• Fine-tuning for custom datasets and tasks.

Module 7: Saving, Loading, and Inference

⏳ 45 minutes

• Persisting trained models with torch.save().

• Performing inference on new data using saved models.

Module 8: End-to-End Project

⏳ 75 minutes

• Full model development cycle: data prep, model building, training, and deployment.

• Best practices for production-ready AI applications.

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