A comprehensive repository documenting my deep learning journey with implementations, concepts, and practical projects.
| Phase | Topic | Time | Priority | Status |
|---|---|---|---|---|
| Phase 1 | Neural Network Foundations | 2-3 weeks | 🔴 Critical | ⬜ |
| Phase 2 | CNNs | 2-3 weeks | 🟠 High | ⬜ |
| Phase 3 | Transformers | 3-4 weeks | 🔴 Critical | ⬜ |
| Phase 4 | Generative AI | 3-4 weeks | 🔴 Critical | ⬜ |
| Phase 5 | Training & Deployment | 2 weeks | 🟠 High | ⬜ |
| Phase 6 | RNNs (Optional) | 1-2 weeks | 🟡 Medium | ⬜ |
| Phase 7 | Reinforcement Learning | 2-3 weeks | 🟠 High | ⬜ |
| Phase 8 | Explainable AI | 1-2 weeks | 🟡 Medium | ⬜ |
| Phase 9 | Advanced Topics | 2-3 weeks | 🟢 Low | ⬜ |
| Phase 10 | Real-World Projects | Ongoing | 🔴 Critical | ⬜ |
1️⃣ Neural Network Foundations
- Perceptron & Multi-Layer Perceptron (MLP)
- Activation Functions (ReLU, Sigmoid, Tanh)
- Loss Functions & Optimizers (SGD, Adam)
- Backpropagation Algorithm
Resources:
2️⃣ Convolutional Neural Networks (CNNs)
- Convolution & Pooling Operations
- Architecture: ResNet, VGG, EfficientNet
- Object Detection (YOLO, R-CNN)
- Image Segmentation (U-Net, Mask R-CNN)
Resources:
3️⃣ Recurrent Neural Networks (RNNs)
- LSTM & GRU Architectures
- Sequence Modeling & NLP
- Time-Series Forecasting
- Attention Mechanisms
Resources:
4️⃣ Transformers ⭐ (Most Important)
- Self-Attention Mechanism
- Multi-Head Attention
- Large Language Models (GPT, LLaMA, BERT)
- Vision Transformers (ViT)
Resources:
5️⃣ Generative AI 🔥
- Generative Adversarial Networks (GANs)
- Diffusion Models (DDPM, Stable Diffusion)
- Text-to-Image Generation
- Fine-Tuning & LoRA Techniques
Resources:
6️⃣ Reinforcement Learning
- Q-Learning & Deep Q-Networks (DQN)
- Policy Gradients (REINFORCE, A3C)
- PPO & RLHF (Reinforcement Learning from Human Feedback)
Resources:
7️⃣ Training & Deployment
- Hyperparameter Tuning & Grid Search
- Model Quantization & Pruning
- MLOps, CI/CD, Model Monitoring
- Docker & Kubernetes for ML
Resources:
8️⃣ Explainable AI
- SHAP (SHapley Additive exPlanations)
- LIME (Local Interpretable Model-agnostic Explanations)
- Feature Attribution Methods
- Model Interpretability
Resources:
9️⃣ Advanced Concepts
- Meta-Learning (Learning to Learn)
- Contrastive Learning (SimCLR, MoCo)
- Multimodal Vision-Language Models (CLIP, LLaVA)
- Few-Shot & Zero-Shot Learning
Resources:
🔟 Real-World Applications
- Natural Language Processing (NLP)
- Computer Vision Applications
- Healthcare AI & Medical Imaging
- Finance & Fraud Detection
- Recommendation Systems
Projects:
- Build a chatbot with LLMs
- Create an image classifier
- Develop a recommendation engine
- Medical image segmentation
├── 01-fundamentals/ # Neural network basics
├── 02-cnns/ # Convolutional networks
├── 03-rnns/ # Recurrent networks
├── 04-transformers/ # Transformer models
├── 05-generative-ai/ # GANs, Diffusion, LLMs
├── 06-reinforcement-learning/# RL implementations
├── 07-deployment/ # MLOps & model serving
├── 08-explainable-ai/ # XAI techniques
├── 09-advanced/ # Advanced topics
├── 10-projects/ # Real-world projects
└── resources/ # Papers, notes, datasets
# Clone the repository
git clone https://github.com/yourusername/deep-learning.git
# Navigate to the directory
cd deep-learning-journey
# Install dependencies
pip install -r requirements.txt
# Launch Jupyter Notebook
jupyter notebook START HERE
├─── 1️⃣ NEURAL NETWORK FOUNDATIONS ─────────────────────────┐
│ • Perceptron & MLP │
│ • Activation Functions (ReLU, Sigmoid, GELU) │
│ • Loss Functions & Optimizers (SGD, Adam, AdamW) │
│ • Backpropagation & Gradient Descent │
│ │
├─── 2️⃣ CNNs ───────────────────────────────────────────────┤
│ • Convolution & Pooling │
│ • ResNet, VGG, EfficientNet │
│ • Object Detection (YOLO, Faster R-CNN) │
│ • Image Segmentation (UNet, Mask R-CNN) │
│ │
├─── 3️⃣ RNNs ──────────────────────────────────────────────┤
│ • LSTM & GRU │
│ • Sequence Modeling │
│ • Time-Series Forecasting │
│ │
├─── 4️⃣ TRANSFORMERS ⭐ (MOST IMPORTANT) ──────────────────┤
│ • Self-Attention & Multi-Head Attention │
│ • Positional Encoding │
│ • LLMs: GPT, LLaMA, BERT │
│ • Vision Transformers (ViT) │
│ │
├─── 5️⃣ GENERATIVE AI 🔥 (TOP SKILL 2025) ─────────────────┤
│ • GANs (DCGAN, StyleGAN) │
│ • Diffusion Models (Stable Diffusion, FLUX) │
│ • Text-to-Image & Text-to-Video │
│ • Prompt Engineering & Fine-Tuning (LoRA, PEFT) │
│ │
├─── 6️⃣ REINFORCEMENT LEARNING ────────────────────────────┤
│ • Q-Learning & Deep Q-Networks (DQN) │
│ • Policy Gradients (PPO, A3C, SAC) │
│ • RLHF (Reinforcement Learning from Human Feedback) │
│ │
├─── 7️⃣ TRAINING & DEPLOYMENT 🚢 ──────────────────────────┤
│ • Hyperparameter Tuning & Regularization │
│ • Quantization, Pruning, Distillation │
│ • MLOps (MLflow, Kubernetes, Docker) │
│ • Model Formats (ONNX, TensorRT) │
│ │
├─── 8️⃣ EXPLAINABLE AI 🔍 ─────────────────────────────────┤
│ • SHAP & LIME │
│ • Integrated Gradients │
│ • Feature Attribution Methods │
│ │
├─── 9️⃣ ADVANCED CONCEPTS 🎓 ──────────────────────────────┤
│ • Meta-Learning (MAML) │
│ • Contrastive Learning (SimCLR, CLIP) │
│ • Multimodal Learning (VLMs) │
│ • Federated Learning │
│ │
└─── 🔟 REAL-WORLD APPLICATIONS 💼 ────────────────────────┘
| • NLP & Computer Vision │
| • Healthcare & Finance AI │
| • Recommendation Systems │
| • Speech Processing │
└───────────────────────────────────────────────────────────┘
graph TD
Start([🚀 Start Here]) --> Spacer1[ ]
Spacer1 --> A[1️⃣ Neural Network Foundations]
A --> A1[Perceptron & MLP]
A --> A2[Activation Functions]
A --> A3[Loss Functions & Optimizers]
A --> A4[Backpropagation]
A --> Spacer2[ ]
Spacer2 --> B[2️⃣ CNNs]
B --> B1[Convolution & Pooling]
B --> B2[ResNet, VGG, EfficientNet]
B --> B3[Object Detection]
B --> B4[Image Segmentation]
A --> C[3️⃣ RNNs]
C --> C1[LSTM & GRU]
C --> C2[Sequence Modeling]
C --> C3[Time-Series]
B --> Spacer3[ ]
Spacer3 --> D[4️⃣ Transformers ⭐]
C --> D
D --> D1[Self-Attention]
D --> D2[Multi-Head Attention]
D --> D3[LLMs: GPT, LLaMA]
D --> D4[Vision Transformers]
D --> Spacer4[ ]
Spacer4 --> E[5️⃣ Generative AI 🔥]
E --> E1[GANs & StyleGAN]
E --> E2[Diffusion Models]
E --> E3[Text-to-Image]
E --> E4[Fine-Tuning & LoRA]
A --> F[6️⃣ Reinforcement Learning]
F --> F1[Q-Learning & DQN]
F --> F2[Policy Gradients]
F --> F3[PPO & RLHF]
D --> G[7️⃣ Training & Deployment]
E --> G
G --> G1[Hyperparameter Tuning]
G --> G2[Quantization & Pruning]
G --> G3[MLOps & CI/CD]
G --> Spacer5[ ]
Spacer5 --> H[8️⃣ Explainable AI]
H --> H1[SHAP & LIME]
H --> H2[Feature Attribution]
D --> I[9️⃣ Advanced Concepts]
E --> I
I --> I1[Meta-Learning]
I --> I2[Contrastive Learning]
I --> I3[Multimodal VLMs]
B --> J[🔟 Real-World Applications]
D --> J
E --> J
F --> J
J --> J1[NLP & Computer Vision]
J --> J2[Healthcare & Finance]
J --> J3[Recommendation Systems]
J --> Spacer6[ ]
Spacer6 --> End([🎯 Job Ready!])
style Start fill:#4CAF50,stroke:#2E7D32,color:#fff
style A fill:#2196F3,stroke:#1565C0,color:#fff
style D fill:#FF9800,stroke:#E65100,color:#fff
style E fill:#F44336,stroke:#C62828,color:#fff
style G fill:#9C27B0,stroke:#6A1B9A,color:#fff
style J fill:#00BCD4,stroke:#00838F,color:#fff
style End fill:#4CAF50,stroke:#2E7D32,color:#fff
style Spacer1 fill:none,stroke:none
style Spacer2 fill:none,stroke:none
style Spacer3 fill:none,stroke:none
style Spacer4 fill:none,stroke:none
style Spacer5 fill:none,stroke:none
style Spacer6 fill:none,stroke:none
- GitHub: @ARUNAGIRINATHAN-K
- LinkedIn: arunagirinathan-k
⭐ Star this repo if you find it helpful! Happy Learning! 🚀