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https://github.com/shaheennabi/deep-learning-practices-and-mini-projects

Welcome to Deep Learning & Math with Python! 🚀💥 Here, we blend code and theory to build deep learning algorithms from scratch and explore the math behind them. 🧠⚡ Whether you're just starting or a seasoned pro, this space is all about learning, experimenting, and creating AI magic together! 🔥🎆 Let's code, learn, and innovate!
https://github.com/shaheennabi/deep-learning-practices-and-mini-projects

activation-functions backpropagation calculus deep-learning deep-neural-networks linear-algebra maths-behind-neural-network opencv pytorch tensorflow

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Welcome to Deep Learning & Math with Python! 🚀💥 Here, we blend code and theory to build deep learning algorithms from scratch and explore the math behind them. 🧠⚡ Whether you're just starting or a seasoned pro, this space is all about learning, experimenting, and creating AI magic together! 🔥🎆 Let's code, learn, and innovate!

Awesome Lists containing this project

README 

        
# 🚀 Welcome to My Deep Learning & Math with Python Repository! 🎇



Welcome to **Deep Learning** & **Mathematical Practices with Python**, where **theory meets code** and experiments turn into real-world applications! This repository is a collection of my **hands-on experiments** with **deep learning** techniques and the mathematical concepts behind them. If you’re passionate about building deep learning algorithms from scratch, understanding their mathematical foundation, and practicing coding techniques, you’re in the right place! 🔥



Whether you are just starting your deep learning journey or a seasoned enthusiast, I aim to make this space a playground where we can all learn together and push the boundaries of deep learning through **Python code**! 🌟



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## 🧠 What’s Inside? 🔍



In this repository, you’ll find a series of **mini-notebooks**, **experiments**, and **projects** that cover the following exciting areas:



### 💻 **Deep Learning Algorithms from Scratch**

- **Perceptrons, Feedforward Neural Networks**: Learn by implementing the most fundamental models used in deep learning.

- **Backpropagation**: The heart of neural networks—let’s code it from scratch and understand its core mechanism!

- **Activation Functions**: Implement and experiment with various activation functions like **Sigmoid**, **ReLU**, **Tanh**, etc.

- **Gradient Descent**: Dive into the math behind the optimization process, and implement basic to advanced optimization algorithms.



### ⚙️ **Building Optimizers from Scratch**

- Explore key optimization algorithms like **Stochastic Gradient Descent (SGD)**, **Adam**, and **RMSProp**.

- Understand their mathematics and apply them to different deep learning models to optimize training.



### 🔢 **Mathematics Behind Deep Learning**

- **Linear Algebra**: Matrix operations, Eigenvectors, and Singular Value Decomposition (SVD) as the backbone of neural networks.

- **Calculus**: Derivatives, chain rule, and integrals that power backpropagation and the learning process.

- **Probability & Statistics**: For building generative models, understanding overfitting/underfitting, and analyzing model performance.



### 🎯 **Mini-Projects & Practical Applications**

- Apply deep learning techniques to **real-world datasets** and build simple but powerful **projects** like classification and regression models.

- Explore **TensorFlow**, **Keras**, and **PyTorch** for applying theoretical learning in real projects.

- Dive into **Computer Vision** (CV) and **Natural Language Processing (NLP)** with deep learning models!



### 📐 **Design Patterns & Clean Code**

- Practice best software engineering principles to write **clean**, **reusable**, and **scalable code**.

- Apply **design patterns** such as **Singleton**, **Factory**, and **Observer** to improve code structure.



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## 🎇 Why This Repository? 🤩



- **Hands-On Learning**: Don’t just read about deep learning—**build it** from scratch and experience the magic of learning by doing! 🎉

- **Mathematics Made Fun**: Deep learning is powered by math, and this repository brings **theory to life** through **Python code**. I break down complex concepts to make them easier to understand! 💡

- **Projects that Work**: Dive into mini-projects that allow you to apply what you've learned and gain **practical experience** with real data. 📊

- **Continuous Updates**: This repository will be **constantly updated** with new algorithms, techniques, and projects as I experiment with the latest trends in deep learning. Expect to find **new insights** regularly! 🚀



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## 📅 Regularly Updated & Expanding 🚀



I’m always experimenting, learning, and improving my skills in deep learning. Expect new notebooks, fresh experiments, and exciting deep learning breakthroughs added regularly. 🚀💫 Each notebook is designed to break down deep learning concepts into digestible, **hands-on examples**, allowing you to fully understand the process.



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## ✨ Contributions Welcome! 🌟



I’m always looking for ways to improve this repository and make it a community-driven resource! 🚀 If you’d like to contribute, feel free to:



- Open an **issue** or **pull request** to suggest improvements, new experiments, or bug fixes.

- Share **ideas** for new projects or techniques you’d like to see.

- **Fork** the repository, try the code, and share your experiments with the world!



Let’s collaborate and learn together! 💪



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## 🛠 License & Usage 📄



This repository is licensed under the **MIT License** 🎉. You are free to use, modify, and distribute this repository in accordance with the terms of the license. 



Please make sure to give appropriate credit to the original author and reference the **license file** for detailed information. 🌟



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🎆 **Let’s Unlock the Power of Deep Learning and Math with Python!** 🎇  

Thank you for being a part of this journey. I hope this repository helps you learn, experiment, and grow as much as it helps me. Let’s continue pushing the limits of AI together! 🌐✨