{"id":26179062,"url":"https://github.com/pointer2alvee/complete-deep-learning","last_synced_at":"2025-09-10T17:45:52.211Z","repository":{"id":280972342,"uuid":"943782349","full_name":"pointer2Alvee/complete-deep-learning","owner":"pointer2Alvee","description":"Comprehensive Deep Learning concepts \u0026 Architectures implemented using PyTorch.","archived":false,"fork":false,"pushed_at":"2025-03-06T09:10:32.000Z","size":1,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-06T10:23:50.818Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/pointer2Alvee.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2025-03-06T09:00:37.000Z","updated_at":"2025-03-06T09:10:35.000Z","dependencies_parsed_at":"2025-03-06T10:23:53.346Z","dependency_job_id":"7143ec21-0fb3-4f6d-af01-36ec02814f74","html_url":"https://github.com/pointer2Alvee/complete-deep-learning","commit_stats":null,"previous_names":["pointer2alvee/complete-deep-learning"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pointer2Alvee%2Fcomplete-deep-learning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pointer2Alvee%2Fcomplete-deep-learning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pointer2Alvee%2Fcomplete-deep-learning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pointer2Alvee%2Fcomplete-deep-learning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/pointer2Alvee","download_url":"https://codeload.github.com/pointer2Alvee/complete-deep-learning/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243119625,"owners_count":20239320,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2025-03-11T21:48:48.713Z","updated_at":"2025-09-10T17:45:52.176Z","avatar_url":"https://github.com/pointer2Alvee.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003cdiv style=\"display: flex; justify-content: space-around; align-items: center;\"\u003e\n  \u003cimg src=\"assets/images/1.png\" alt=\"Image 1\" style=\"width: 49%; margin: 10px;\"\u003e\n  \u003cimg src=\"assets/images/2.png\" alt=\"Image 2\" style=\"width: 49%; margin: 10px;\"\u003e\n\u003c!--   \u003cimg src=\"images/3.JPG\" alt=\"Image 3\" style=\"width: 25%; margin: 10px;\"\u003e --\u003e\n\u003c!--   \u003cimg src=\"images/4.JPG\" alt=\"Image 2\" style=\"width: 33%; margin: 10px;\"\u003e --\u003e\n\u003c/div\u003e\n\n## 📜 complete-deep-learning\n#### 🧠 Overview \nComplete Deep Learning concepts \u0026amp; 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.\n\n#### 🎯 Use Cases \n- Implementing DL algorithms/models/concepts using python \u0026 pytorch\n- Learning \u0026 implementing the mathematical foundation of deep learning using python \u0026 pytorch\n- Learn deep learning from scratch with a mathematical + implementation-first approach\n- Study \u0026 build neural networks with PyTorch\n- Study \u0026 build DL architectures with PyTorch\n- Prepare for interviews and research\n- Use as a practical teaching/learning guide\n- Reference architecture and code for deep learning projects\n  \n#### 🟢 Project Status\n- Current Version: V1.0\n- Actively maintained \u0026 expanded\n\n#### 📂 Repository Structure\n```\ncomplete-deep-learning\n├── assets\n│   └── images\n│\n├── datasets\n│   └── images-text-audio-misc\n│\n├── math-foundations\n│   ├── linear-algebra\n│   ├── calculus\n│   └── probability-stats\n│                                              \n├── basic-neural-network-architecture\n│   ├── neuron-perceptron\n│   ├── neural-net-layers\n│   │   ├── input-hidden-output-layers\n│   ├── activation-functions\n│   ├── ann (multilayer-perceptron)\n│   │   ├── geometric-view\n│   │   ├── ann-maths (forwardprop, error-los-cost, backrprop)\n│   │   ├── ann-regression-clasification\n│   │   ├── multi-layer-ann\n│   │   ├── multi-output-ann\n│   │   └── model-depth-breadth\n│   ├── meta-parameters\n│   └── hyper-parameters\n│\n├── neural-network-concepts\n│   ├── regularization\n│   │   ├── prevent-overfitting-underfitting\n│   │   ├── weight-reg\n│   │   ├── dropout\n│   │   ├── data-augmentation\n│   │   ├── nomralization\n│   │   │   ├── batch-nomralization\n│   │   │   └── layer-nomralization\n│   │   └── early-stopping\n│   ├── optimization\n│   │   ├── loss-cost-functions\n│   │   ├── gradient-descent\n│   │   |   ├── vanilla-gd, sgd, minibatch-sgd\n│   │   ├── adaptive-optimization-algorithms\n│   │   |   ├── momentum, nag, adagrad, rmsprop, adam, adamw\n│   │   ├── learning-schedules\n│   │   ├── weight-investigations\n│   │   ├── numerical-stability\n│   │   ├── meta-parameter-optimization\n│   │   └── hyper-parameter-optimization\n│   └── generalization\n│       ├── cross-validation\n│       ├── overfitting-underfitting\n│       └── hyper-parameter-tuning\n│\n├── computational-performance\n│   └── run-on-gpu\n│\n├── advanced-neural-network-architecture\n│   ├── ffn\n│   ├── cnn-modern-cnn\n│   │   ├── convolution\n│   │   ├── cannonical-cnn\n│   │   └── cnn-adv-architectures\n│   ├── rnn\n│   │   ├── lstm\n│   │   ├── gru\n│   ├── gan\n│   ├── gnn\n│   ├── attention-mechanism\n│   ├── transformer-models\n│   │   └── bert\n│   └── encoders\n│       └── autoencoders\n│\n├── model-training\n│   ├── transfer-learning\n│   ├── style-transfer\n|   ├── training-loop-structure (epoch, batch, loss logging)\n|   ├── callbacks (custom logging, checkpointing)\n|   ├── experiment-tracking (Weights \u0026 Biases, TensorBoard)\n│   └──  multitask-learning\n│\n└── model-evaluation\n|   ├── accuracy-precision-recall-f1-auc-roc\n|   └── confusion-matrix\n│\n└── papers-to-code\n```\n\n### ✨ Features\n- Covers Concepts, Mathematical implementations, DL nets and architectures\n- Pure Python and Pytorch\n- Modular, clean, and reusable code\n- Educational and beginner-friendly\n- Covers everything from perceptrons to transformers\n- Clean, modular, and well-commented PyTorch implementations\n- Visualization, training loops, and performance metrics\n- Includes datasets for images, text, audio, and more\n- Papers-to-Code section to implement SOTA research\n\n\u003c!-- ### 🔍 Demo\n\u003ca href=\"https://youtu.be/Qor8kjsCJkA?si=7d1Mhc0KW4GQb3sF\" target=\"_blank\"\u003e\n  \u003cimg src=\"https://img.youtube.com/vi/Qor8kjsCJkA/hqdefault.jpg\" alt=\"YouTube Video\" width=\"390\" height=\"270\"\u003e\n\u003c/a\u003e --\u003e\n\n### 🚀 Getting Started\n- Knowledge Required : python, linear algebra, probability, statistics, numpy, matplotlib, scikit-learn, pytorch\n\n\u003c!-- ### 🛠️ Hardware Requirements\n- None\n--\u003e\n\n#### 💻 Software Requirements\n- IDE (VS Code) or jupyter notebook or google colab\n- Python 3\n  \n#### 🛡️ Tech Stack\n- Python , PyTorch, TorchVision 💻\n- Numpy, Pandas, Matplotlib, Scikit-Learn 🧩\n\n\u003c!--\n### 🖇️ Schematic\n- none\n--\u003e\n\n#### ⚙️ Installation\n```\ngit clone https://github.com/pointer2Alvee/complete-deep-learning.git\ncd comprehensive-deep-learning\n```\n\n#### 📖 Usage\n- Open .ipynb files inside each concept or NN architecture directory and\n- Run them to see training/inference steps, plots, and results.\n\n#### 🔍 Contents Breakdown\n##### 📚 Math Foundations\n- Linear Algebra, Calculus, Probability, Statistics\n\n##### 🧱 Neural Network Basics\n- Perceptrons, Layers, Activations, MLPs\n- Forward \u0026 Backpropagation math from scratch\n- Depth vs Breadth of models\n- Regression \u0026 Classification using ANN\n\n##### 🔧 Deep Learning Concepts\n- Regularization (Dropout, L2, Data Aug)\n- Optimization (SGD, Adam, RMSProp, Schedules)\n- Losses, Weight tuning, Meta \u0026 Hyperparams\n\n##### ⚙️ Advanced Architectures\n- CNNs (classic + modern)\n- RNNs, LSTM, GRU\n- GANs, GNNs\n- Transformers \u0026 BERT\n- Autoencoders\n\n##### 🏋️‍♂️ Model Training \u0026 Tracking\n- Training Loops, Epochs, Batches\n- Custom callbacks\n- TensorBoard, Weights \u0026 Biases logging\n- Transfer Learning \u0026 Style Transfer\n- Multitask learning\n\n##### 📊 Evaluation\n- Accuracy, Precision, Recall, F1, AUC-ROC\n- Confusion Matrix\n\n##### 🔬 Research to Practice\n- Paper Implementations → PyTorch Code\n\n\n### 🧪 Sample Topics Implemented\n- ✅ Forward \u0026 Backpropagation from scratch\n- ✅ CNN with PyTorch\n- ✅ Regularization (Dropout, Weight Decay)\n- ✅ Adam vs SGD Performance Comparison\n- ✅ Image Classification using Transfer Learning\n- ✅ Transformer Attention Visualizations\n- ✅ Autoencoder for Denoising\n- ✅ Style Transfer with Pretrained CNN\n  \n- ⏳ Upcoming  : nlp, cv, llm, data engineering, feature engineering\n\n### 🧭 Roadmap\n- [x] Build foundational math notebooks\n- [ ] Implement perceptron → MLP → CNN\n- [ ] Add reinforcement learning section\n- [ ] Implement GAN, RNN, Transformer\n- [ ] More research paper implementations\n\n### 🤝 Contributing\nContributions are welcomed!\n1. Fork the repo. \n2. Create a branch: ```git checkout -b feature/YourFeature```\n3. Commit changes: ```git commit -m 'Add some feature'```\n4. Push to branch: ```git push origin feature/YourFeature```\n5. Open a Pull Request.\n\n### 📜License\nDistributed under the MIT License. See LICENSE.txt for more information.\n\n### 🙏Acknowledgements\n- Special thanks to the open-source community / youtube for tools and resources.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpointer2alvee%2Fcomplete-deep-learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpointer2alvee%2Fcomplete-deep-learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpointer2alvee%2Fcomplete-deep-learning/lists"}