https://github.com/aw-junaid/neural-network-and-machine-learning

python programming for machine learning and deep learning
https://github.com/aw-junaid/neural-network-and-machine-learning

algorithms artificial-intelligence deep-learning machine-learning machine-learning-algorithms neural-network neuron-simulator pandas pandas-library python tensorflow tensorflow-examples

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python programming for machine learning and deep learning

• Host: GitHub
• URL: https://github.com/aw-junaid/neural-network-and-machine-learning
• Owner: aw-junaid
• License: mit
• Created: 2023-10-16T09:18:36.000Z (12 months ago)
• Default Branch: main
• Last Pushed: 2023-11-11T00:53:46.000Z (11 months ago)
• Last Synced: 2024-09-22T08:03:19.378Z (6 days ago)
• Topics: algorithms, artificial-intelligence, deep-learning, machine-learning, machine-learning-algorithms, neural-network, neuron-simulator, pandas, pandas-library, python, tensorflow, tensorflow-examples
• Language: Jupyter Notebook
• Homepage: https://awjunaid.com/
• Size: 117 KB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        

![Logo](https://www.ibm.com/content/dam/connectedassets-adobe-cms/worldwide-content/cdp/cf/ul/g/3a/b8/ICLH_Diagram_Batch_01_03-DeepNeuralNetwork.png)

# Neural-Network-and-Machine-Learning

This repository contains implementations and examples of various neural network architectures and machine learning algorithms. From fundamental feedforward networks to advanced convolutional and recurrent models, this collection serves as a practical resource for understanding and applying machine learning concepts.

## Authors:

- [@aw-junaid](https://github.com/aw-junaid/)

## Read Article About Neural Network:

 - [What is the role of mini-batch training in neural networks](https://awjunaid.com/artificial-intelligence/what-is-the-role-of-mini-batch-training-in-neural-networks)

 - [What is the difference between a supervised and unsupervised learning algorithm?](https://awjunaid.com/artificial-intelligence/what-is-the-difference-between-a-supervised-and-unsupervised-learning-algorithm/)

 - [What is the concept of a state-value function in reinforcement learning](https://awjunaid.com/artificial-intelligence/what-is-the-concept-of-a-state-value-function-in-reinforcement-learning/)

  - [What is the purpose of the Kullback-Leibler (KL) divergence loss function](https://awjunaid.com/artificial-intelligence/what-is-the-purpose-of-the-kullback-leibler-kl-divergence-loss-function/)

   - [Explain the concept of early stopping in neural network training](https://awjunaid.com/artificial-intelligence/explain-the-concept-of-early-stopping-in-neural-network-training/)

- [What is the vanishing gradient problem in Neural Network](https://awjunaid.com/artificial-intelligence/what-is-the-vanishing-gradient-problem-in-neural-network/)

## licenses

[![MIT License](https://img.shields.io/badge/License-MIT-green.svg)](https://choosealicense.com/licenses/mit/)

[![GPLv3 License](https://img.shields.io/badge/License-GPL%20v3-yellow.svg)](https://opensource.org/licenses/)

[![AGPL License](https://img.shields.io/badge/license-AGPL-blue.svg)](http://www.gnu.org/licenses/agpl-3.0)

## Key Features:

- Diverse Implementations: Explore a wide range of neural network architectures, including feedforward, convolutional, and recurrent networks, along with popular machine learning algorithms.

- Comprehensive Examples: Find detailed examples and use cases for each implemented model, demonstrating their application in various domains such as computer vision, natural language processing, and more.

- Modular and Extensible: Each implementation is designed with modularity in mind, making it easy to adapt and extend for specific tasks or research projects.

- Detailed Documentation: Extensive documentation accompanies each implementation, providing insights into the architecture, hyperparameters, and recommended use cases.

- Performance Benchmarks: Compare the performance of different models on benchmark datasets, enabling easy evaluation and selection of appropriate architectures for specific tasks.

## Installation:

### 1. Clone the Repository:

```bash

git clone https://github.com/yourusername/Neural-Network-and-Machine-Learning.git

```bash

# Clone the Repository

git clone https://github.com/aw-junaid/Neural-Network-and-Machine-Learning.git

# Install Dependencies

pip install -r requirements.txt

```

## Support:

For support, please open an issue or reach out to [email protected].