https://github.com/sayande01/deep_learning
This repository serves as a comprehensive guide to deep learning concepts, designed to evolve from fundamental ideas to advanced techniques. Starting with the basics of perceptrons and moving through the intricacies of multilayer perceptrons (MLPs), this repository aims to provide a structured learning path for anyone interested in Deep learning
https://github.com/sayande01/deep_learning
neural-network perceptron
Last synced: 2 months ago
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This repository serves as a comprehensive guide to deep learning concepts, designed to evolve from fundamental ideas to advanced techniques. Starting with the basics of perceptrons and moving through the intricacies of multilayer perceptrons (MLPs), this repository aims to provide a structured learning path for anyone interested in Deep learning
- Host: GitHub
- URL: https://github.com/sayande01/deep_learning
- Owner: sayande01
- Created: 2024-08-12T18:19:05.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2024-08-19T18:05:10.000Z (10 months ago)
- Last Synced: 2025-02-13T02:38:42.152Z (4 months ago)
- Topics: neural-network, perceptron
- Language: Jupyter Notebook
- Homepage:
- Size: 3.02 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
### Title: **Deep Learning**
### Description:
This repository serves as a comprehensive guide to deep learning concepts, designed to evolve from fundamental ideas to advanced techniques. Starting with the basics of perceptrons and moving through the intricacies of multilayer perceptrons (MLPs), this repository aims to provide a structured learning path for anyone interested in mastering deep learning. It includes explanations, code examples, and practical applications to facilitate understanding and hands-on experience with each concept.
### Objective:
The objective of this repository is to offer a well-organized and progressively detailed resource for learning deep learning. It aims to:
1. **Introduce Basic Concepts**: Begin with the foundational elements of perceptrons and their role in neural networks.
2. **Explore MLPs**: Delve into multilayer perceptrons, understanding their architecture, training, and applications.
3. **Facilitate Practical Learning**: Provide code snippets and examples to help users implement and experiment with each concept.
4. **Prepare for Advanced Topics**: Lay the groundwork for advanced deep learning topics by ensuring a solid grasp of basic and intermediate concepts.