Complete Deep Learning concepts & Architectures implemented using PyTorch. This is a comprehensive Deep Learning roadmap and implementation using PyTorch — starting from core math foundations to state-of-the-art neural network architectures. The repository is designed to give a solid theoretical and practical understanding of deep learning, structured progressively to cover foundational concepts, mathematical intuition, model architectures, training, and evaluation.
- Implementing DL algorithms/models/concepts using python & pytorch
- Learning & implementing the mathematical foundation of deep learning using python & pytorch
- Learn deep learning from scratch with a mathematical + implementation-first approach
- Study & build neural networks with PyTorch
- Study & build DL architectures with PyTorch
- Prepare for interviews and research
- Use as a practical teaching/learning guide
- Reference architecture and code for deep learning projects
- Current Version: V1.0
- Actively maintained & expanded
complete-deep-learning
├── assets
│ └── images
│
├── datasets
│ └── images-text-audio-misc
│
├── math-foundations
│ ├── linear-algebra
│ ├── calculus
│ └── probability-stats
│
├── basic-neural-network-architecture
│ ├── neuron-perceptron
│ ├── neural-net-layers
│ │ ├── input-hidden-output-layers
│ ├── activation-functions
│ ├── ann (multilayer-perceptron)
│ │ ├── geometric-view
│ │ ├── ann-maths (forwardprop, error-los-cost, backrprop)
│ │ ├── ann-regression-clasification
│ │ ├── multi-layer-ann
│ │ ├── multi-output-ann
│ │ └── model-depth-breadth
│ ├── meta-parameters
│ └── hyper-parameters
│
├── neural-network-concepts
│ ├── regularization
│ │ ├── prevent-overfitting-underfitting
│ │ ├── weight-reg
│ │ ├── dropout
│ │ ├── data-augmentation
│ │ ├── nomralization
│ │ │ ├── batch-nomralization
│ │ │ └── layer-nomralization
│ │ └── early-stopping
│ ├── optimization
│ │ ├── loss-cost-functions
│ │ ├── gradient-descent
│ │ | ├── vanilla-gd, sgd, minibatch-sgd
│ │ ├── adaptive-optimization-algorithms
│ │ | ├── momentum, nag, adagrad, rmsprop, adam, adamw
│ │ ├── learning-schedules
│ │ ├── weight-investigations
│ │ ├── numerical-stability
│ │ ├── meta-parameter-optimization
│ │ └── hyper-parameter-optimization
│ └── generalization
│ ├── cross-validation
│ ├── overfitting-underfitting
│ └── hyper-parameter-tuning
│
├── computational-performance
│ └── run-on-gpu
│
├── advanced-neural-network-architecture
│ ├── ffn
│ ├── cnn-modern-cnn
│ │ ├── convolution
│ │ ├── cannonical-cnn
│ │ └── cnn-adv-architectures
│ ├── rnn
│ │ ├── lstm
│ │ ├── gru
│ ├── gan
│ ├── gnn
│ ├── attention-mechanism
│ ├── transformer-models
│ │ └── bert
│ └── encoders
│ └── autoencoders
│
├── model-training
│ ├── transfer-learning
│ ├── style-transfer
| ├── training-loop-structure (epoch, batch, loss logging)
| ├── callbacks (custom logging, checkpointing)
| ├── experiment-tracking (Weights & Biases, TensorBoard)
│ └── multitask-learning
│
└── model-evaluation
| ├── accuracy-precision-recall-f1-auc-roc
| └── confusion-matrix
│
└── papers-to-code
- Covers Concepts, Mathematical implementations, DL nets and architectures
- Pure Python and Pytorch
- Modular, clean, and reusable code
- Educational and beginner-friendly
- Covers everything from perceptrons to transformers
- Clean, modular, and well-commented PyTorch implementations
- Visualization, training loops, and performance metrics
- Includes datasets for images, text, audio, and more
- Papers-to-Code section to implement SOTA research
- Knowledge Required : python, linear algebra, probability, statistics, numpy, matplotlib, scikit-learn, pytorch
- IDE (VS Code) or jupyter notebook or google colab
- Python 3
- Python , PyTorch, TorchVision 💻
- Numpy, Pandas, Matplotlib, Scikit-Learn 🧩
git clone https://github.com/pointer2Alvee/complete-deep-learning.git
cd comprehensive-deep-learning
- Open .ipynb files inside each concept or NN architecture directory and
- Run them to see training/inference steps, plots, and results.
- Linear Algebra, Calculus, Probability, Statistics
- Perceptrons, Layers, Activations, MLPs
- Forward & Backpropagation math from scratch
- Depth vs Breadth of models
- Regression & Classification using ANN
- Regularization (Dropout, L2, Data Aug)
- Optimization (SGD, Adam, RMSProp, Schedules)
- Losses, Weight tuning, Meta & Hyperparams
- CNNs (classic + modern)
- RNNs, LSTM, GRU
- GANs, GNNs
- Transformers & BERT
- Autoencoders
- Training Loops, Epochs, Batches
- Custom callbacks
- TensorBoard, Weights & Biases logging
- Transfer Learning & Style Transfer
- Multitask learning
- Accuracy, Precision, Recall, F1, AUC-ROC
- Confusion Matrix
- Paper Implementations → PyTorch Code
-
✅ Forward & Backpropagation from scratch
-
✅ CNN with PyTorch
-
✅ Regularization (Dropout, Weight Decay)
-
✅ Adam vs SGD Performance Comparison
-
✅ Image Classification using Transfer Learning
-
✅ Transformer Attention Visualizations
-
✅ Autoencoder for Denoising
-
✅ Style Transfer with Pretrained CNN
-
⏳ Upcoming : nlp, cv, llm, data engineering, feature engineering
- Build foundational math notebooks
- Implement perceptron → MLP → CNN
- Add reinforcement learning section
- Implement GAN, RNN, Transformer
- More research paper implementations
Contributions are welcomed!
- Fork the repo.
- Create a branch:
git checkout -b feature/YourFeature - Commit changes:
git commit -m 'Add some feature' - Push to branch:
git push origin feature/YourFeature - Open a Pull Request.
Distributed under the MIT License. See LICENSE.txt for more information.
- Special thanks to the open-source community / youtube for tools and resources.