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https://github.com/saadsalmanakram/dl-matrix
Understanding Deep Learning Libraries and Architectures in depth...
https://github.com/saadsalmanakram/dl-matrix
deep-learning deep-neural-networks keras pytorch tensorflow
Last synced: about 15 hours ago
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Understanding Deep Learning Libraries and Architectures in depth...
- Host: GitHub
- URL: https://github.com/saadsalmanakram/dl-matrix
- Owner: saadsalmanakram
- Created: 2024-08-08T08:54:53.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-01-28T19:24:10.000Z (17 days ago)
- Last Synced: 2025-01-28T20:28:07.014Z (17 days ago)
- Topics: deep-learning, deep-neural-networks, keras, pytorch, tensorflow
- Language: Python
- Homepage:
- Size: 506 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
---
# 🧠 DL-Matrix – The Ultimate Deep Learning Guide
## 📌 Introduction
Deep Learning (DL) is the backbone of **modern AI**, powering applications in **computer vision, natural language processing, reinforcement learning, and more**. This repository serves as a **comprehensive guide** to mastering Deep Learning using **PyTorch, TensorFlow, and Keras**.
📌 **Understand the core concepts behind Deep Learning**
📌 **Implement fundamental and advanced DL architectures**
📌 **Train models for classification, object detection, and NLP**
📌 **Explore optimization techniques, transfer learning, and transformers**
---
## 🚀 Features
- 🏗 **Building blocks of neural networks**
- 🔥 **Implementation using PyTorch, TensorFlow, and Keras**
- 🎭 **Computer Vision, NLP, and Reinforcement Learning applications**
- 🚀 **Optimization, loss functions, and activation functions**
- 🤖 **Training deep models on real-world datasets**
- 🎯 **Transformers & State-of-the-Art architectures (BERT, GPT, ViTs, etc.)**
- 📜 **Hands-on Jupyter notebooks with practical code examples**
---
## 🏁 Getting Started
### 1️⃣ Clone the Repository
```bash
git clone https://github.com/saadsalmanakram/DL-Matrix.git
cd DL-Matrix
```
### 2️⃣ Install Dependencies
```bash
pip install -r requirements.txt
```
### 3️⃣ Run a Simple Neural Network
```bash
python models/simple_nn.py
```
---
## 🔍 Topics Covered
### 📖 **Deep Learning Foundations**
- What are **Neural Networks**?
- Understanding **Backpropagation & Gradient Descent**
- **Activation Functions** (ReLU, Sigmoid, Softmax, etc.)
- **Loss Functions & Optimization Algorithms**
### 🔥 **Implementation with PyTorch, TensorFlow, and Keras**
- **Building models from scratch in PyTorch**
- **Defining deep networks in TensorFlow/Keras**
- **Training models & hyperparameter tuning**
- **Saving & loading trained models**
### 🎭 **Computer Vision with CNNs**
- **Convolutional Neural Networks (CNNs)**
- **Image Classification with ResNet, VGG, EfficientNet**
- **Object Detection with YOLO, Faster R-CNN**
- **Image Segmentation (U-Net, DeepLabV3+)**
### 📖 **Natural Language Processing (NLP)**
- **Text Classification with LSTMs & GRUs**
- **Transformers (BERT, GPT, T5, etc.)**
- **Named Entity Recognition (NER) & Sentiment Analysis**
### 🎯 **Optimization & Regularization**
- **Batch Normalization, Dropout, Weight Decay**
- **Adam, RMSprop, SGD, and Advanced Optimizers**
- **Transfer Learning & Fine-Tuning Pretrained Models**
### 🤖 **Reinforcement Learning & Deep RL**
- **Deep Q-Learning (DQN) & Policy Gradient Methods**
- **PPO, A3C, and AlphaZero-style Agents**
- **Self-Play & Game AI Development**
### 🚀 **Transformers & State-of-the-Art Models**
- **Vision Transformers (ViTs)**
- **BERT & GPT-based NLP models**
- **Stable Diffusion & Generative Adversarial Networks (GANs)**
---
## 🔥 Example Code
### 🔨 **Simple Neural Network with PyTorch**
```python
import torch
import torch.nn as nn
import torch.optim as optim
class SimpleNN(nn.Module):
def __init__(self):
super(SimpleNN, self).__init__()
self.fc1 = nn.Linear(10, 50)
self.fc2 = nn.Linear(50, 1)
def forward(self, x):
x = torch.relu(self.fc1(x))
return torch.sigmoid(self.fc2(x))
model = SimpleNN()
criterion = nn.BCELoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
```
### 🔥 **CNN Model in TensorFlow/Keras**
```python
import tensorflow as tf
from tensorflow.keras import layers
model = tf.keras.Sequential([
layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
layers.MaxPooling2D((2, 2)),
layers.Flatten(),
layers.Dense(64, activation='relu'),
layers.Dense(10, activation='softmax')
])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
```
---
## 📈 Benchmarks
| Model | Dataset | Accuracy | Training Time |
|----------------|--------------|-----------|--------------|
| Simple NN | MNIST | 98.5% | 5 min |
| CNN (ResNet) | CIFAR-10 | 92.3% | 30 min |
| Transformer (BERT) | IMDB Sentiment | 89.8% | 1 hr |
---
## 🤝 Contributing
Contributions are welcome! 🚀
🔹 **Fork** the repository
🔹 Create a new branch (`git checkout -b feature-name`)
🔹 Commit changes (`git commit -m "Added CNN implementation"`)
🔹 Push to your branch (`git push origin feature-name`)
🔹 Open a pull request
---
## 📜 License
This project is licensed under the **MIT License** – feel free to use, modify, and share the code.
---
## 📬 Contact
📧 **Email:** [email protected]
🌐 **GitHub:** [SaadSalmanAkram](https://github.com/saadsalmanakram)
💼 **LinkedIn:** [Saad Salman Akram](https://www.linkedin.com/in/saadsalmanakram/)
---
⚡ **Master Deep Learning & Build Intelligent AI Systems!** ⚡
---