Deep Learning Roadmap
Dive deep into neural networks and state-of-the-art deep learning models.
Prerequisites
- Strong Python programming
- Linear algebra and calculus
- Basic machine learning knowledge
- PyTorch or TensorFlow familiarity
Phase 1: Neural Network Fundamentals
Core Concepts
- Forward propagation
- Backpropagation
- Gradient descent variants
- Activation functions (ReLU, sigmoid, tanh)
Network Architecture
- Fully connected layers
- Loss functions
- Regularization (L1, L2, dropout)
- Batch normalization
Phase 2: Computer Vision
Convolutional Neural Networks
- Convolution operations
- Pooling layers
- CNN architectures (LeNet, AlexNet, VGG)
- Modern architectures (ResNet, Inception, EfficientNet)
Advanced CV Topics
- Object detection (YOLO, R-CNN)
- Semantic segmentation
- Transfer learning
- Data augmentation
Phase 3: Natural Language Processing
Text Processing
- Word embeddings (Word2Vec, GloVe)
- Sequence models
- RNNs and LSTMs
- GRUs
Transformers
- Attention mechanism
- Self-attention
- BERT, GPT architecture
- Fine-tuning pre-trained models
Phase 4: Advanced Topics
Generative Models
- Autoencoders
- Variational autoencoders (VAE)
- Generative adversarial networks (GANs)
- Diffusion models
Reinforcement Learning
- Markov decision processes
- Q-learning
- Policy gradients
- Deep Q-networks (DQN)
Phase 5: Production & Optimization
Model Optimization
- Quantization
- Pruning
- Knowledge distillation
- Mixed precision training
Deployment
- Model serving (TensorFlow Serving, TorchServe)
- ONNX format
- Edge deployment
- GPU optimization
Resources
- Courses: Deep Learning Specialization (Coursera), Fast dot ai
- Papers: ArXiv, Papers with Code
- Communities: Reddit r/MachineLearning, Discord servers
Timeline
Estimated completion: 6-12 months with project work