Deep Learning for Computer Vision with Python and TensorFlow

A comprehensive implementation of deep learning for computer vision using Python and TensorFlow. This repository contains practical examples and code covering the complete course curriculum from basics to advanced topics.

Course Overview

This repository provides a complete guide to learning computer vision with deep learning. It covers everything from TensorFlow fundamentals to advanced topics like Transformers, object detection, and generative models. All code examples are practical and designed to be run in Google Colab or local Python environments.

Part 1: TensorFlow Fundamentals

Learn the core concepts and data structures of TensorFlow:

• Tensor basics and operations
• Initialization and casting
• Indexing and slicing
• Mathematical operations
• Linear algebra operations
• TensorFlow functions
• Ragged and sparse tensors
• String tensors
• Variables

Part 2: Building Neural Networks

Implement your first neural network for regression:

• Task understanding and data preparation
• Linear regression model development
• Loss functions and error calculation
• Training and optimization
• Performance measurement
• Validation and testing

Part 3: Convolutional Neural Networks

Build CNNs for image classification:

• CNN architecture and theory
• Data preparation and visualization
• Binary and multi-class classification
• Model training and evaluation
• Model persistence

Part 4: Advanced CNN Architectures

Explore different ways to build models:

• Functional API
• Model subclassing
• Custom layers

Part 5: Model Evaluation

Master evaluation metrics and visualization:

• Precision, recall, and accuracy
• Confusion matrices
• ROC curves and AUC scores

Part 6: Performance Optimization

Improve model performance through various techniques:

• TensorFlow callbacks
• Learning rate scheduling
• Model checkpointing
• Overfitting and underfitting mitigation

Part 7: Data Augmentation

Increase training data diversity:

• tf.image and Keras layer augmentation
• Mixup augmentation
• Cutmix augmentation
• Albumentations library

Part 8: Advanced TensorFlow Topics

Dive deeper into TensorFlow capabilities:

• Custom loss functions and metrics
• Eager and graph execution modes
• Custom training loops

Part 9: TensorBoard Integration

Visualize and monitor training:

• Data logging and visualization
• Model graph inspection
• Hyperparameter tuning
• Profiling and performance analysis

Part 10: MLOps with Weights and Biases

Enterprise-level experiment tracking:

• Experiment tracking
• Hyperparameter tuning
• Dataset versioning
• Model versioning

Part 11: Modern CNN Architectures

Study state-of-the-art architectures:

• AlexNet
• VGGNet
• ResNet and coding from scratch
• MobileNet
• EfficientNet

Part 12: Transfer Learning

Leverage pre-trained models:

• Feature extraction
• Fine-tuning strategies

Part 13: Model Interpretability

Understand what your models learn:

• Visualizing intermediate layers
• Grad-CAM visualization

Part 14: Vision Transformers

Explore the transformer architecture for vision:

• Understanding Vision Transformers (ViTs)
• Building ViTs from scratch
• Fine-tuning HuggingFace ViTs
• Model evaluation

Part 15: Model Deployment

Prepare models for production:

• Converting TensorFlow models to ONNX format
• Quantization techniques
• Quantization-aware training
• Converting to TensorFlow Lite
• Building APIs with FastAPI
• Cloud deployment
• Load testing with Locust

Part 16: Object Detection with YOLO

Learn state-of-the-art object detection:

• Object detection fundamentals
• YOLO algorithm and theory
• Dataset preparation
• Loss function implementation
• Data augmentation strategies
• Testing and evaluation

Part 17: Image Generation

Generate synthetic images:

• Variational Autoencoders (VAEs)
• VAE training and digit generation
• Latent space visualization
• Generative Adversarial Networks (GANs)
• GAN loss functions
• Training strategies
• Face generation with GANs

Topics Covered

Core Concepts

• Tensors and tensor operations
• Neural network architectures
• Loss functions and optimization
• Backpropagation and gradient descent
• Regularization techniques

Computer Vision Applications

• Image classification
• Object detection
• Image segmentation
• Image generation
• Face recognition

Model Development

• Data preprocessing and augmentation
• Model training and validation
• Hyperparameter tuning
• Performance evaluation
• Model optimization

Deployment and Production

• Model serialization
• Model quantization
• API development
• Cloud deployment
• Load testing

Projects

This course includes three major projects:

Project 1: Car Price Prediction

A regression task using linear neural networks to predict car prices based on features. Learn fundamentals of data preparation, model building, and performance measurement.

Project 2: Malaria Diagnosis

A binary classification task using CNNs to diagnose malaria from cell images. This project covers data preprocessing, model training, evaluation metrics, and various optimization techniques.

Project 3: Human Emotion Detection

A multi-class classification task using emotion recognition from facial images. This project incorporates transfer learning, Vision Transformers, model interpretability, and deployment.