PyTorch for Deep Learning with Python Bootcamp Course Syllabus

Overview: This comprehensive PyTorch bootcamp is designed for beginners to learn deep learning through hands-on coding and real-world projects. The course spans approximately 8 hours of on-demand video, covering core concepts from tensors to model deployment. You'll gain practical experience building neural networks, CNNs, RNNs, and applying transfer learning, all using PyTorch. With a strong balance of theory and implementation, this course prepares you for real-world AI applications.

Module 1: Introduction to PyTorch & Setup

Estimated time: 0.5 hours

• Installing PyTorch and setting up the development environment
• Configuring Jupyter Notebooks and Python environments
• Overview of PyTorch ecosystem and capabilities
• Understanding PyTorch vs other deep learning frameworks

Module 2: PyTorch Fundamentals & Tensors

Estimated time: 0.75 hours

• Creating and manipulating tensors in PyTorch
• Performing tensor operations and broadcasting
• Understanding dynamic computation graphs
• Using autograd for automatic differentiation

Module 3: Neural Networks from Scratch

Estimated time: 1 hour

• Building feedforward neural networks using torch.nn
• Defining layers, activation functions, and architectures
• Selecting and implementing loss functions
• Using optimizers like SGD and Adam for training

Module 4: Model Training Workflow

Estimated time: 1 hour

• Creating training and evaluation loops
• Monitoring model performance and convergence
• Utilizing GPU acceleration with CUDA
• Debugging common training issues

Module 5: Convolutional Neural Networks (CNNs)

Estimated time: 1 hour

• Designing CNN architectures for image recognition
• Applying convolutional, pooling, and dropout layers
• Training CNNs on MNIST and CIFAR-10 datasets
• Evaluating model accuracy and overfitting

Module 6: Recurrent Neural Networks (RNNs) & Time Series

Estimated time: 1 hour

• Building RNNs for sequential data processing
• Implementing LSTM and GRU layers
• Applying RNNs to text and time series forecasting
• Handling variable-length sequences

Module 7: Transfer Learning & Pretrained Models

Estimated time: 0.75 hours

• Understanding transfer learning concepts
• Using pretrained models like ResNet
• Feature extraction vs. fine-tuning strategies
• Adapting models for custom datasets

Module 8: TensorBoard & Model Visualization

Estimated time: 0.75 hours

• Integrating TensorBoard with PyTorch
• Visualizing model architecture and gradients
• Tracking training metrics and loss curves
• Debugging models using visual insights

Module 9: Saving, Loading & Deployment

Estimated time: 0.75 hours

• Saving and loading model checkpoints
• Serializing models using TorchScript
• Preparing models for inference
• Overview of deployment options and best practices

Module 10: Final Projects & Applications

Estimated time: 1.25 hours

• Building an image classification pipeline
• Developing a time series forecasting model
• Applying best practices in data preprocessing and training
• Presenting results and model evaluation

Prerequisites

• Basic knowledge of Python programming
• Familiarity with fundamental machine learning concepts
• Understanding of linear algebra and gradients (helpful but not required)

What You'll Be Able to Do After

• Build and train deep learning models using PyTorch
• Implement CNNs for image classification tasks
• Develop RNNs for time series and text data
• Apply transfer learning to improve model performance
• Deploy trained models and visualize training with TensorBoard