Overview
Comprehensive introduction to deep learning using PyTorch, covering fundamentals, computer vision applications, and practical model creation for AI enthusiasts and developers.
Syllabus
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- 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
Taught by
Tags
On-Demand
