A comprehensive Deep Learning repository built with Python, TensorFlow, Keras, and PyTorch, covering the complete deep learning workflow from Neural Network Fundamentals to Modern State-of-the-Art Architectures. This repository contains well-structured Jupyter notebooks implementing popular Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformers, Generative Models, Graph Neural Networks (GNNs), Computer Vision, Model Optimization, and many other advanced architectures, making it an excellent resource for learning, interview preparation, research, and portfolio projects.
- Comprehensive collection of Deep Learning algorithms and architectures
- Beginner-friendly to advanced learning path
- Well-organized folder structure
- Clean, well-documented Jupyter notebooks
- Step-by-step implementations
- Real-world datasets and practical examples
- TensorFlow, Keras, and PyTorch implementations
- Computer Vision, NLP, and Reinforcement Learning models
- Modern State-of-the-Art (SOTA) architectures
- Ready-to-run examples using Tensorflow and Pytorch
Deep-Learning-Models-Python
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├── 01. Artificial Neural Networks (ANN)
├── 02. Convolutional Neural Networks (CNN)
├── 03. Recurrent Neural Networks (RNN)
├── 04. Attention Mechanisms
├── 05. Transformers
├── 06. Vision Transformers (ViT)
├── 07. Transfer Learning
├── 08. Object Detection
├── 09. Image Segmentation
├── 10. Autoencoders
├── 11. Generative Adversarial Networks (GAN)
├── 12. Diffusion Models
├── 13. Graph Neural Networks (GNN)
├── 14. Capsule Networks
├── 15. Siamese Networks
├── 16. Self-Organizing Maps (SOM)
├── 17. Restricted Boltzmann Machines (RBM)
└── 18. Model Optimization
- Perceptron
- Feedforward Neural Networks (FNN)
- Artificial Neural Networks (ANN)
- Binary Classification
- Multi-Class Classification
- Activation Functions
- Loss Functions
- Backpropagation
- Convolution Operation
- Pooling Layers
- LeNet
- AlexNet
- VGGNet
- GoogLeNet (Inception)
- ResNet
- DenseNet
- Xception
- MobileNet
- EfficientNet
- Image Classification
- Vanilla RNN
- Long Short-Term Memory (LSTM)
- Gated Recurrent Unit (GRU)
- Bidirectional RNN
- Encoder-Decoder Architecture
- Sequence-to-Sequence (Seq2Seq)
- Time Series Forecasting
- Text Classification
- Sequence Prediction
- Bahdanau Attention
- Luong Attention (General)
- Luong Attention (Dot Product)
- Self-Attention
- Single-Head Attention
- Multi-Head Attention
- Self-Attention
- Multi-Head Attention
- Positional Encoding
- Transformer Architecture
- Encoder - Decoder
- BERT, GPT, T5
- Vision Transformer (ViT)
- DeiT
- Swin Transformer
- Image Classification with ViT
- Fine-Tuning Vision Transformers
- Feature Extraction
- Fine-Tuning
- Transfer Learning using TensorFlow
- Transfer Learning using PyTorch
- VGG19
- ResNet
- InceptionV3
- EfficientNet
- MobileNet
- YOLOv8
- SSD (Single Shot Detector)
- Faster R-CNN
- RetinaNet
- Object Localization
- Bounding Box Prediction
- U-Net
- U-Net++
- Mask R-CNN
- DeepLabV3+
- Semantic Segmentation
- Instance Segmentation
- Vanilla Autoencoder
- Sparse Autoencoder
- Denoising Autoencoder
- Convolutional Autoencoder
- Variational Autoencoder (VAE)
- Dimensionality Reduction
- Anomaly Detection
- Vanilla GAN
- Deep Convolutional GAN (DCGAN)
- Conditional GAN (CGAN)
- Pix2Pix
- CycleGAN
- StyleGAN
- Image Generation
- Image-to-Image Translation
- Diffusion Model Fundamentals
- Denoising Diffusion Probabilistic Models (DDPM)
- Stable Diffusion Concepts
- Latent Diffusion Models
- Diffusion Transformer
- Image Generation
- Graph Convolutional Networks (GCN)
- Graph Attention Networks (GAT)
- GraphSAGE
- Node Classification
- Link Prediction
- Graph Embedding
- Zachary's Karate Club Network
- Geometric Neural Networks.ipynb
- Capsule Layers
- Dynamic Routing
- Image Classification
- Pose-aware Feature Learning
- Similarity Learning
- Contrastive Loss
- Triplet Loss
- Face Verification
- Face Recognition
- Signature Verification
- One-Shot Learning
- Kohonen Self-Organizing Maps
- Unsupervised Learning
- Clustering
- Data Visualization
- Energy-Based Models
- Contrastive Divergence
- Feature Learning
- Collaborative Filtering
- Weight Initialization
- Batch Normalization
- Dropout
- Early Stopping
- Learning Rate Scheduling
- Gradient Descent, Stochastic Gradient Descent (SGD)
- Momentum
- RMSProp
- Adam, AdamW
- Adagrad
- Learning Rate Decay
- L1 & L2 Regularization
- Hyperparameter Tuning
- Python
- NumPy
- Pandas
- Matplotlib
- TensorFlow
- Keras
- PyTorch
- OpenCV
- scikit-learn
- Hugging Face Transformers
- torchvision
- Jupyter Notebook
Clone the repository:
git clone https://github.com/shaahmir/Deep-Learning-Python.gitInstall the required dependencies:
pip install numpy pandas matplotlib scikit-learn tensorflow keras torch torchvision opencv-python transformers jupyterLaunch Jupyter Notebook:
jupyter notebookOpen any notebook and run the cells to explore the models.
This repository is designed for:
- Students learning Deep Learning
- Beginners exploring Artificial Intelligence
- Computer Vision enthusiasts
- Interview preparation
- Academic coursework
- Research projects
- Portfolio showcasing
By exploring this repository, you will learn:
- Fundamentals of Deep Learning
- Neural Network design and implementation
- Computer Vision using CNNs and Vision Transformers
- Natural Language Processing using RNNs and Transformers
- Movie Recommendation system using Autoencoders
- Image generation using GANs and Diffusion Models
- Graph learning using Graph Neural Networks
- Model optimization techniques
- Transfer Learning using pretrained models
- Building production-ready Deep Learning models using TensorFlow and PyTorch
This project is licensed under the MIT License.
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