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Deep Learning Roadmap
Linear Algebra for DL (Vectors, Matrices, Tensors)
Calculus (Derivatives, Chain Rule, Partial Derivatives)
Understanding Loss Functions
Foundations
Probability & Statistics for DL
Optimization Basics (Gradient Descent, Learning Rate)
Perceptrons
Activation Functions (ReLU, Sigmoid, Tanh, Softmax)
Training, Validation, Testing
Neural Networks Basics
Feedforward Neural Networks
Forward & Backpropagation
Regularization (L1, L2, Dropout, BatchNorm)
Stochastic Gradient Descent (SGD)
Weight Initialization Techniques
Vanishing & Exploding Gradient Problem
Optimization & Training Techniques
Momentum, RMSProp, Adam, AdamW
Learning Rate Schedulers
Convolutional Neural Networks (CNNs)
Long Short-Term Memory (LSTMs)
Attention Mechanism
Graph Neural Networks (GNNs)
Generative Adversarial Networks (GANs)
Deep Architectures
Recurrent Neural Networks (RNNs)
Gated Recurrent Units (GRUs)
Transformers
Autoencoders & Variational Autoencoders
Data Preprocessing & Augmentation
Building DL Models with TensorFlow
Hyperparameter Tuning
Practical Deep Learning
Transfer Learning & Fine-Tuning
Building DL Models with PyTorch
Experiment Tracking (TensorBoard, MLflow)
Computer Vision (Image Classification, Object Detection, Segmentation)
Speech Recognition & Audio Processing
Reinforcement Learning with Deep Q-Networks
Specialized Domains
Natural Language Processing (Embeddings, Transformers, BERT, GPT)
Time Series Forecasting with DL
Model Compression (Pruning, Quantization, Distillation)
Serving Models with Flask/FastAPI
Dockerizing Deep Learning Models
Scaling & Deployment
Distributed Training (Data Parallelism, Model Parallelism)
Deploying with TensorFlow Serving & TorchServe
Edge & Mobile Deployment (TensorFlow Lite, ONNX)
Self-Supervised Learning
Large Language Models (LLMs)
Federated Learning
Advanced Topics
Few-Shot & Zero-Shot Learning
Diffusion Models (Stable Diffusion, DALL·E)
Ethics & Bias in Deep Learning
Image Classification (CIFAR-10, MNIST)
Text Sentiment Analysis
Music or Image Generation with GANs
Projects
Object Detection (YOLO, Faster R-CNN)
Machine Translation (Seq2Seq with Attention)
Fine-tuning BERT or GPT on Custom Dataset
Neural Network Fundamentals Q&A
Optimization & Training Questions
System Design for Deep Learning Models
Interview Preparation
CNNs, RNNs & Transformers Q&A
Case Studies (Image Search, Chatbots, Recommendation)
Deep Learning Roadmap
