Deep Learning & Neural Networks Roadmap — Step-by-step Learning Path
Neural network architectures, CNNs, RNNs and modern deep learning
The Deep Learning & Neural Networks roadmap
- Python Programming Fundamentals — beginner
- Python Syntax & Data Types — beginner
- Control Flow & Functions — beginner
- Data Structures (Lists, Dicts, Sets, Tuples) — beginner
- Object-Oriented Python — beginner
- File I/O & Working with Modules — beginner
- Virtual Environments & pip — beginner
- Mathematics for Deep Learning — beginner
- Linear Algebra for Deep Learning — beginner
- Calculus & the Chain Rule — beginner
- Probability & Information Theory Basics — beginner
- Optimization Landscapes — beginner
- Vector Calculus for Backpropagation — beginner
- Python for Deep Learning — beginner
- NumPy for Numerical Computing — beginner
- Pandas for Data Handling — beginner
- Introduction to PyTorch & TensorFlow — beginner
- GPU Computing Basics — beginner
- Neural Network Fundamentals — intermediate
- The Perceptron & Biological Inspiration — intermediate
- Feedforward Neural Networks — intermediate
- Activation Functions — intermediate
- Loss Functions — intermediate
- Backpropagation Explained — intermediate
- Gradient Descent Variants (SGD, Momentum, RMSprop, Adam) — intermediate
- Weight Initialization Strategies — intermediate
- Deep Learning Frameworks in Depth — intermediate
- PyTorch Tensors & Autograd — intermediate
- Building Models with nn.Module — intermediate
- Custom Datasets & DataLoaders — intermediate
- Writing Training & Evaluation Loops — intermediate
- TensorFlow & Keras Essentials — intermediate
- Mixed Precision & Performance Basics — advanced
- Training Deep Neural Networks — intermediate
- Regularization (L1/L2, Dropout) — intermediate
- Batch Normalization & Layer Normalization — intermediate
- Learning Rate Scheduling — intermediate
- Vanishing & Exploding Gradients — intermediate
- Hyperparameter Tuning for Deep Learning — advanced
- Early Stopping & Checkpointing — intermediate
- Convolutional Neural Networks (CNNs) — advanced
- The Convolution Operation — advanced
- Pooling Layers — advanced
- Classic CNN Architectures (LeNet, AlexNet, VGG) — advanced
- Modern CNN Architectures (ResNet, EfficientNet) — advanced
- Transfer Learning with Pretrained CNNs — advanced
- Data Augmentation for Images — advanced
- Object Detection Basics (YOLO, Faster R-CNN) — advanced
- Image Segmentation Basics (U-Net) — advanced
- Recurrent Neural Networks & Sequence Models — advanced
- RNN Fundamentals — advanced
- The Vanishing Gradient Problem in RNNs — advanced
- Long Short-Term Memory (LSTM) — advanced
- Gated Recurrent Units (GRU) — advanced
- Sequence-to-Sequence Models — advanced
- Introduction to the Attention Mechanism — advanced
- NLP with Deep Learning — advanced
- Text Preprocessing for Deep Learning — advanced
- Word Embeddings (Word2Vec, GloVe) — advanced
- Text Classification with RNNs/LSTMs — advanced
- Subword Tokenization (BPE, WordPiece) — advanced
- Sequence Labeling Tasks (NER, POS Tagging) — advanced
- Transformers & Modern Architectures — advanced
- Self-Attention Mechanism — advanced
- The Transformer Architecture (Encoder-Decoder) — advanced
- BERT & Encoder-Only Models — advanced
- GPT & Decoder-Only Models — advanced
- Vision Transformers (ViT) — advanced
- Fine-Tuning Pretrained Transformers — advanced
- Parameter-Efficient Fine-Tuning (LoRA, Adapters) — advanced
- Generative Models — advanced
- Autoencoders — advanced
- Variational Autoencoders (VAEs) — advanced
- Generative Adversarial Networks (GANs) — advanced
- Diffusion Models Basics — advanced
- Applications of Generative Models — advanced
- Model Optimization & Deployment for Deep Learning — advanced
- Model Quantization — advanced
- Model Pruning — advanced
- Knowledge Distillation — advanced
- Exporting Models (ONNX, TorchScript) — advanced
- Serving Deep Learning Models — advanced
- GPU/TPU Inference Considerations — advanced
- Edge Deployment for Deep Learning — advanced
- MLOps for Deep Learning — advanced
- Experiment Tracking for Deep Learning — advanced
- Distributed Training Basics — advanced
- Data Pipelines for Large-Scale Training — advanced
- Model Versioning for Deep Learning — advanced
- Responsible & Explainable Deep Learning — advanced
- Bias in Deep Learning Models — advanced
- Explainability for Neural Networks (Grad-CAM, Saliency Maps) — advanced
- Adversarial Examples & Robustness — advanced
- Ethical Considerations in Deep Learning — advanced
- Case Studies & Capstone — advanced
- Case Study: Image Classification Pipeline — advanced
- Case Study: Sentiment Analysis with Transformers — advanced
- Case Study: Building an LLM-Powered Chatbot — advanced
- Case Study: Fine-Tuning a Pretrained Model for a Custom Task — advanced
- Capstone: End-to-End Deep Learning Project — advanced