Deep Learning (DL) Training Course

Introduction to Deep Learning
Difference between Machine Learning and Deep Learning
Biological Inspiration: Neurons and the Human Brain
Artificial Neural Networks (ANNs)
Structure of a Neural Network (Input, Hidden, Output Layers)
Activation Functions (Sigmoid, ReLU, Tanh, Softmax)
Forward Propagation and Backpropagation
Loss Functions and Optimization
Gradient Descent and Learning Rate
Overfitting, Underfitting, and Regularization Techniques

Building Neural Networks from Scratch (NumPy)
Introduction to Deep Learning Frameworks (TensorFlow & PyTorch)
Creating and Training Models in TensorFlow
Creating and Training Models in PyTorch
Hyperparameter Tuning
Model Evaluation and Metrics
Saving and Loading Models
Batch Normalization and Dropout
Early Stopping and Callbacks
Practical Implementation on Simple Datasets

Introduction to TensorFlow Ecosystem
Tensors, Variables, and Operations
Building Neural Networks with Keras API
Model Compilation, Training, and Evaluation
Callbacks (EarlyStopping, ModelCheckpoint)
TensorBoard for Visualization
Implementing CNNs, RNNs, and Transfer Learning
Hyperparameter Tuning (KerasTuner)
Saving & Loading Models (SavedModel, H5)
Hands-on Projects using TensorFlow

Introduction to PyTorch Framework
Tensors, Autograd, and Computational Graph
Building Neural Networks using nn.Module
Optimizers and Loss Functions
Training Loops and Backpropagation
GPU Acceleration (CUDA)
Implementing CNNs, RNNs, and Transformers
Using torchvision and torchtext
Model Saving, Loading, and Evaluation
Real-World Projects with PyTorch

Introduction to CV and Image Processing
Image Representation and Color Models
Image Filtering and Edge Detection
Object Detection (Haar, HOG)
CNNs for Image Classification
Image Augmentation Techniques
Face Detection and Recognition
Transfer Learning with CNNs
Image Segmentation
OCR (Optical Character Recognition)

Convolutional Neural Networks (CNNs) – Theory and Architecture
Applications of CNNs (Image Classification, Object Detection)
Recurrent Neural Networks (RNNs)
Long Short-Term Memory (LSTM) and GRU Networks
Autoencoders and Variational Autoencoders (VAEs)
Generative Adversarial Networks (GANs)
Transfer Learning and Pretrained Models
Attention Mechanism
Transformers Overview
Practical Implementation with Real-World Use Cases

Introduction to NLP
NLP Pipeline and Text Preprocessing
Tokenization, Stopwords Removal, Stemming, and Lemmatization
Bag of Words and TF-IDF
Word Embeddings (Word2Vec, GloVe, FastText)
Part-of-Speech (PoS) Tagging and Named Entity Recognition (NER)
Language Models and Probability-Based NLP
Text Classification and Sentiment Analysis
Sequence-to-Sequence Models
Evaluation Metrics in NLP

Embedding Layers in Deep Learning
RNNs, LSTMs, and GRUs for Text Data
Encoder-Decoder Architecture
Attention Mechanism in NLP
Transformer Models (BERT, GPT, T5)
Fine-tuning Pretrained Models for NLP Tasks
Text Generation and Summarization
Machine Translation
Chatbot and Question-Answering Systems

Gradient Descent Variants (SGD, Momentum, RMSProp, Adam)
Learning Rate Scheduling
Weight Initialization Techniques
Dropout and Early Stopping
Batch Normalization and Layer Normalization
Data Augmentation as Regularization
Loss Function Selection (MSE, Cross-Entropy, Huber, etc.)
Dealing with Overfitting and Underfitting
Practical Tips for Training Stability
Tuning and Debugging Deep Models

Evaluation Metrics for Classification and Regression
Confusion Matrix and ROC-AUC
Precision-Recall and F1-Score Analysis
Cross-Validation Techniques
Hyperparameter Tuning (Grid Search, Random Search, Bayesian)
Experiment Tracking Tools (MLflow, Weights & Biases)
Error Analysis and Model Diagnostics
Handling Imbalanced Data (SMOTE, Class Weights)
Model Comparison and Selection
Reproducibility and Documentation

Model Serialization (Pickle, Joblib, SavedModel)
Creating REST APIs using Flask/FastAPI
Containerization with Docker
Deploying Models on Cloud (AWS, GCP, Azure)
Model Monitoring and Logging
CI/CD for ML Pipelines
Streamlit/Gradio for Interactive Demos
Model Versioning and Rollback
Model Governance and Lifecycle Management

Defining Project Objectives
Problem Formulation
Data Collection and Cleaning
Model Selection
Training and Tuning
Evaluation and Metrics
Deployment Strategy
Feedback Loop and Maintenance
Documentation and Reporting
Case Study Development