https://github.com/shriram-vibhute/deep-learning

Welcome to the Deep Learning Repository! This repository is designed to provide comprehensive resources and practical implementations related to deep learning. It includes foundational concepts, advanced techniques, and theoretical discussions to support both learning and research in deep learning.
https://github.com/shriram-vibhute/deep-learning

ann cnn deep-learning keras natural-language-processing object-detection rnn tensorflow

Last synced: 26 days ago
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Welcome to the Deep Learning Repository! This repository is designed to provide comprehensive resources and practical implementations related to deep learning. It includes foundational concepts, advanced techniques, and theoretical discussions to support both learning and research in deep learning.

• Host: GitHub
• URL: https://github.com/shriram-vibhute/deep-learning
• Owner: Shriram-Vibhute
• Created: 2024-08-30T17:45:52.000Z (2 months ago)
• Default Branch: main
• Last Pushed: 2024-10-09T19:52:33.000Z (30 days ago)
• Last Synced: 2024-10-13T00:41:23.474Z (26 days ago)
• Topics: ann, cnn, deep-learning, keras, natural-language-processing, object-detection, rnn, tensorflow
• Language: Jupyter Notebook
• Homepage: https://github.com/Shriram-Vibhute/Deep-Learning
• Size: 6.28 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: readme.md

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README

        
# Deep Learning Repository

Welcome to the Deep Learning Repository! This repository contains various implementations and theoretical concepts related to deep learning algorithms. Below you'll find detailed descriptions of each section and recent updates. 

## Table of Contents

1. [Perceptron](#perceptron)

2. [Multilayer Perceptron](#multilayer-perceptron)

3. [Backpropagation](#backpropagation)

4. [Vanishing & Exploding Gradient Problems](#vanishing--exploding-gradient-problems)

5. [Early Stopping using Callbacks](#early-stopping-using-callbacks)

6. [Data Scaling](#data-scaling)

7. [Dropout Layers](#dropout-layers)

8. [Regularization](#regularization)

9. [Weight Initialization Techniques](#weight-initialization-techniques)

## 📁 Perceptron

### Description

This folder contains the implementation of the Perceptron model, a fundamental building block in neural networks. It includes code and examples demonstrating the Perceptron algorithm and its application in binary classification tasks.

### Recent Updates

- **Backpropagation Algorithm**: Added last week

## 📁 Multilayer Perceptron

### Description

This section includes the implementation of Multilayer Perceptron (MLP) networks. MLPs are a type of feedforward artificial neural network with one or more layers between input and output layers. The code covers various architectures and applications of MLPs.

### Recent Updates

- **Backpropagation Algorithm**: Added last week

## 📁 Backpropagation

### Description

This folder focuses on the Backpropagation algorithm, a crucial component for training neural networks. It includes detailed explanations and implementations of the algorithm used to optimize network weights.

### Recent Updates

- **Backpropagation Algorithm**: Added last week

## 📁 Vanishing & Exploding Gradient Problems

### Description

Here, you'll find theoretical discussions and solutions related to the vanishing and exploding gradient problems. These issues can severely impact the training of deep neural networks, and this section explores various strategies to mitigate them.

### Recent Updates

- **Theory Added**: 3 days ago

## 📁 Early Stopping using Callbacks

### Description

This folder contains implementations of early stopping techniques using callbacks. Early stopping helps to prevent overfitting by monitoring the model's performance on a validation set and stopping training when performance ceases to improve.

### Recent Updates

- **Dropout Layer and Regularization**: Added yesterday

## 📁 Data Scaling

### Description

Data scaling is crucial for improving the performance of neural networks. This section provides implementations and examples of various data scaling techniques, including normalization and standardization.

### Recent Updates

- **Dropout Layer and Regularization**: Added yesterday

## 📁 Dropout Layers

### Description

The dropout technique is used to prevent overfitting in neural networks by randomly dropping units during training. This section covers the implementation and application of dropout layers.

### Recent Updates

- **Dropout Layer and Regularization**: Added yesterday

## 📁 Regularization

### Description

Regularization techniques are essential for improving model generalization by adding constraints or penalties to the loss function. This folder includes various regularization techniques and their implementations.

### Recent Updates

- **Dropout Layer and Regularization**: Added yesterday

## 📁 Weight Initialization Techniques

### Description

Proper weight initialization is crucial for effective training of neural networks. This section discusses different weight initialization techniques and their impact on model performance.

### Recent Updates

- **Weight Initialization Techniques**: Added recently

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## 🚧 Upcoming Updates

We are continuously working to enhance this repository with more advanced topics and techniques in deep learning. Stay tuned for new additions and updates that will further expand the scope of this repository.

Feel free to explore each folder for detailed implementations and theoretical insights. If you have any questions or contributions, please open an issue or a pull request. Happy learning! 🚀

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For more information, please refer to the [documentation](#) or contact the repository maintainer.