Deep Learning with PyTorch

Overview

Comprehensive introduction to deep learning using PyTorch, covering fundamentals, computer vision applications, and practical model creation for AI enthusiasts and developers.

Syllabus

• Introduction to Deep Learning and PyTorch
What is Deep Learning?
Overview of PyTorch
Setting up the PyTorch Environment
• PyTorch Basics
Tensors in PyTorch
Introduction to Autograd and Dynamic Computation Graphs
Building and Training a Simple Model
• Neural Networks with PyTorch
Understanding Neural Networks
The nn.Module Class
Activation Functions
Loss Functions and Optimization
• Deep Learning Models
Convolutional Neural Networks (CNNs)
Basics of CNNs
Implementing CNNs in PyTorch
Recurrent Neural Networks (RNNs)
Basics of RNNs
Implementing RNNs in PyTorch
• Practical Applications in Computer Vision
Image Classification
Transfer Learning and Pre-trained Models
Object Detection and Segmentation
• Training and Optimizing Models
Data Loading and Augmentation
Hyperparameter Tuning
Using GPUs for Training
• Advanced Topics
Generative Adversarial Networks (GANs)
Sequence-to-Sequence Models
Reinforcement Learning Basics
• Real-world Projects and Case Studies
Project: Building an Image Classifier
Case Study: PyTorch in Industry Applications
Group Project: End-to-End Model Development
• Conclusion and Next Steps
Review and Summary of Key Concepts
Resources for Further Learning
Capstone Project Presentation and Feedback
• Final Exam and Certificate of Completion

---

Subjects

Conference Talks