DL Notes

Hub Activation Function Comparison Guide Activation Functions Overview Leaky ReLU: Solving Dead Neurons ReLU Activation Function Sigmoid Activation Function Softmax Function for Multi-class Classification Tanh Activation Function Computer Vision Projects with Deep Learning Face Recognition with Deep Learning Faster R-CNN Object Detection Image Processing Fundamentals Image Segmentation — Deep Learning Object Detection Architectures U-Net Architecture YOLO Real-time Object Detection AlexNet CNN Architecture CNN Architecture Design Introduction to CNNs Convolution Operation Image Classification Project LeNet Architecture Pooling Layers ResNet and Residual Networks Transfer Learning in Deep Learning VGGNet Architecture GPU Acceleration for Deep Learning Keras Deep Learning Framework PyTorch Deep Learning Framework Setting Up Deep Learning Environment TensorFlow Framework Guide TensorFlow vs PyTorch Comparison Deploying Deep Learning Models to Cloud Deep Learning APIs and Services Docker For Deep Learning Fastapi For Deployment Flask For Deployment Model Deployment Model Monitoring Ai Agent Basics Autoencoders Diffusion Models Generative Adversarial Networks Gan Generative Ai Introduction Large Language Models Llms Prompt Engineering Variational Autoencoders Cnn Interview Questions Deep Learning Cheatsheet Deep Learning Interview Questions — Deep Learning Neural Network Questions Transformer Interview Questions AI vs ML vs Deep Learning — Deep Learning Applications of Deep Learning Deep Learning Roadmap History of Deep Learning What is Deep Learning?Batch Normalization Explained Dropout Hyperparameter Tuning Model Debugging Model Evaluation Metrics Regularization Methods Train Validation Test Split Chatbot Development Glove Nlp Projects Nlp With Deep Learning Sentiment Analysis Seq2Seq Models Word Embeddings Word2Vec Artificial Neural Networks (ANN)Backpropagation Biological vs Artificial Neurons Forward Propagation Gradient Descent Layers in Neural Networks Loss Functions in Deep Learning The Perceptron Adam Optimizer Batch Gradient Descent Learning Rate Scheduling Mini Batch Gradient Descent Momentum Optimization Basics Rmsprop Stochastic Gradient Descent Calculus for Deep Learning Data Preprocessing for Deep Learning Linear Algebra for Deep Learning NumPy Basics for Deep Learning Probability and Statistics for Deep Learning Python for Deep Learning Ai Chatbot Project Face Mask Detection Handwritten Digit Recognition Image Classification System Object Detection System Personal Ai Assistant Recommendation System — Deep Learning Sentiment Analysis System Bidirectional Rnn GRU Networks LSTM Networks Rnn Introduction Sequence Modeling Text Generation Project Vanishing Gradient Problem Datasets For Practice Deep Learning Glossary Important Formulas — Deep Learning Recommended Books And Papers Attention Mechanism Bert Model Encoder Decoder Architecture Gpt Models Positional Encoding Self Attention Transformer Projects Transformers Introduction

Deep Learning

Deep learning from fundamentals to advanced — neural networks, CNNs, RNNs, transformers, GANs, model training, optimization, and real-world projects.

Welcome to the comprehensive Deep Learning course. This learning path takes you from the mathematical foundations all the way through modern architectures like Transformers and Diffusion Models, with hands-on code in PyTorch and TensorFlow at every step.

Learning Path Overview

Deep learning is the engine behind today's most impressive AI systems — from ChatGPT to self-driving cars, from AlphaFold to Stable Diffusion. This course is structured to build your understanding layer by layer, just like the networks you'll learn to build.

What You Will Learn

Foundation (Weeks 1-2)

Prerequisites: Linear algebra, calculus, probability — the mathematical language of deep learning
Introduction: What deep learning is, its history, and where it fits in the AI landscape
Neural Network Fundamentals: Perceptrons, forward propagation, backpropagation, loss functions

Core Architectures (Weeks 3-6)

Activation Functions: ReLU, sigmoid, tanh, softmax — the non-linearities that give networks their power
Optimization: SGD, momentum, Adam — how networks actually learn
CNNs: Convolutional networks for image understanding (LeNet → AlexNet → VGG → ResNet)
RNNs: Recurrent networks for sequential data (vanilla RNN → LSTM → GRU)
Transformers: The attention revolution (self-attention → BERT → GPT)

Advanced Topics (Weeks 7-10)

Generative AI: GANs, VAEs, Diffusion Models, Large Language Models
Computer Vision: Object detection (YOLO, Faster R-CNN), segmentation, face recognition
NLP: Word embeddings, sequence-to-sequence, sentiment analysis
Training & Regularization: Dropout, batch normalization, hyperparameter tuning

Production (Weeks 11-12)

Frameworks: PyTorch vs TensorFlow, Keras, GPU acceleration
Deployment: Flask/FastAPI APIs, Docker, cloud deployment, monitoring
Projects: End-to-end implementations to solidify your skills

Prerequisites

You should be comfortable with:

Python programming (functions, classes, NumPy)
Basic linear algebra (matrices, vectors, dot products)
Calculus fundamentals (derivatives, chain rule)
Basic probability and statistics

How to Use This Course

Follow the order — each section builds on the previous
Run the code — every page includes working Python examples
Do the projects — applying knowledge cements understanding
Review the interview questions — test your conceptual understanding

Tools You'll Use

PyTorch — primary framework for most examples
TensorFlow/Keras — alternative implementations provided
NumPy — for understanding operations from scratch
Matplotlib — for visualizing training and results

Let's begin your deep learning journey.

Course Structure

Deep Learning topics by unit

Choose a unit and open the topic you want to study. Each topic includes definitions, diagrams, examples, and revision notes.

Continue with related tutorials and placement topics

MDX JavaScript Java Python SQL DBMS

Learning Path Overview

What You Will Learn

Foundation (Weeks 1-2)

Core Architectures (Weeks 3-6)

Advanced Topics (Weeks 7-10)

Production (Weeks 11-12)

Prerequisites

How to Use This Course

Tools You'll Use

Deep Learning topics by unit

Activation Functions

Computer Vision

Convolutional Neural Networks

Deep Learning Frameworks

Deployment And Mlops

Generative Ai

Interview Preparation

Introduction

Model Training And Evaluation

Natural Language Processing

Neural Network Fundamentals

Optimization Techniques

Prerequisites

Projects

Recurrent Neural Networks

Resources

Transformers

More Full Courses and Tutorials

Continue with related tutorials and placement topics

Learning Path Overview

What You Will Learn

Foundation (Weeks 1-2)

Core Architectures (Weeks 3-6)

Advanced Topics (Weeks 7-10)

Production (Weeks 11-12)

Prerequisites

How to Use This Course

Tools You'll Use

Deep Learning topics by unit

Activation Functions

Computer Vision

Convolutional Neural Networks

Deep Learning Frameworks

Deployment And Mlops

Generative Ai

Interview Preparation

Introduction

Model Training And Evaluation

Natural Language Processing

Neural Network Fundamentals

Optimization Techniques

Prerequisites

Projects

Recurrent Neural Networks

Resources

Transformers

More Full Courses and Tutorials

Continue with related tutorials and placement topics