A-Z Guide to Deep Learning👋🛒

Welcome to the A-Z Guide to Deep Learning repository! This comprehensive supplement serves as your gateway to the expansive world of Deep Learning, offering in-depth coverage of algorithms, statistical methods, and techniques essential for mastering this cutting-edge field.

Overview👋🛒

The A-Z Guide to Deep Learning is designed to provide a comprehensive roadmap for both beginners and experienced practitioners seeking to delve into the realm of Deep Learning. Whether you're just starting your journey or looking to expand your expertise, this repository offers a wealth of resources to support your learning and exploration.

Features👋🛒

1- Extensive Coverage: Explore a wide range of topics, including fundamental concepts, advanced algorithms, statistical methods, and practical techniques crucial for understanding and implementing Deep Learning models.

2-Hands-On Implementations: Dive into practical implementations of Deep Learning algorithms and techniques using Python, alongside detailed explanations, code examples, and real-world applications.

3-Progressive Learning Path: Follow a structured learning path that progresses from foundational concepts to advanced topics, ensuring a gradual and comprehensive understanding of Deep Learning principles and methodologies.

4-Supplementary Resources: Access supplementary materials, such as articles, tutorials, research papers, and curated datasets, to enrich your learning experience and stay updated with the latest developments in Deep Learning.

Contents

Fundamental Concepts: Covering essential concepts such as neural networks, activation functions, optimization algorithms, loss functions, and regularization techniques.

Advanced Algorithms: Exploring advanced Deep Learning architectures and algorithms, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and reinforcement learning.

Statistical Methods and Techniques: Discussing statistical methods and techniques commonly used in Deep Learning, such as hypothesis testing, probability distributions, dimensionality reduction, and Bayesian inference.

Usage

Explore the repository's contents, follow the structured learning path, and leverage the provided code examples, exercises, and projects to deepen your understanding of Deep Learning concepts and techniques.

Contributing🙌

Contributions are welcome! Whether it's fixing a bug, enhancing existing content, or adding new material, your contributions can help enrich the learning experience for others. Please contact to the my skype ID:themushtaq48 for guidelines on how to contribute.

Star this repo if you find it useful ⭐

📬 Contact

If you want to contact me, you can reach me through social handles.

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Course 1 - 🧠Deep Learning-Neural Networks

Week 1-📚Chapter1: Introduction of Deep learning

Topic Name/Tutorial	Video	Code
🌐1-Understanding Basic Neural Networks⭐	1-2-3-4-5	Content 3
🌐2-Supervised Learning with Neural Networks⭐	1	Content 6
🌐3-Exploring the Different Types of Artificial Neural Networks⭐	-1	---
🌐4- Why is Deep Learning taking off?⭐	1	---
🌐5-Best Free Resources to Learn Deep learning (DL)⭐	---	---

Week 2-📚Chapter1:2 Logistic Regression as a Neural Network

Topic Name/Tutorial	Video	Notebook
🌐1- Binary Classification⭐	1	Content 3
🌐2- Logistic Regression⭐	1-2	Content 6
🌐3- Understanding the Logistic Regression Cost Function⭐	1	---
🌐4-Understanding the Logistic Regression Gradient Descent⭐	1-2	---
🌐5-Intuition about Derivatives⭐	1
🌐6-Computation Graph	1-2	---
🌐7-Derivatives with a Computation Graph	1	---
🌐8-Logistic Regression Gradient Descent	1	---
🌐9-Gradient Descent on m Examples	1

Week 3-📚Chapter 3 Python and Vectorization

Topic Name/Tutorial	Video	Notebook
🌐1-Vectorization	1
🌐2-More Vectorization Examples	1
🌐3-Vectorizing Logistic Regression	1
🌐4-Vectorizing Logistic Regression’s Gradient Output	1

Week 4-📚Chapter4: Shallow Neural Network

Topic Name/Tutorial	Video	Notebook
🌐1-Neural Networks Overview	1-2
🌐2-Neural Network Representation	1
🌐3-Computing a Neural Network's Output	1-2
🌐4-Vectorizing Across Multiple Examples	1
🌐5-Explanation for Vectorized Implementation	1
🌐6-Activation functions	1
🌐7-Why do you need Non-Linear Activation Functions?	1
🌐8-Derivatives of Activation Functions?	1
🌐9-Gradient Descent for Neural Networks?	1
🌐10-Backpropagation Intuition?	1
🌐11-Random Initialization?	1

Week 5-📚Chapter5:Deep Neural Network

Topic Name/Tutorial	Video	Notebook
🌐1-Deep L-layer Neural Network	1
🌐2-Forward Propagation in a Deep Network	1
🌐3-Getting your Matrix Dimensions Right	1
🌐4-Why Deep Representations?	1
🌐5-Building Blocks of Deep Neural Networks?	1
🌐6-Forward and Backward Propagation?	1
🌐7-Parameters vs Hyperparameters	1

Course 2 - 🧠Improving Deep Neural Network

Week 1-📚Chapter1:Practical Aspects of Deep Learning

Topic Name/Tutorial	Video	Code
🌐1-Train / Dev / Test sets	1
🌐2-Bias Variance	1
🌐3-Basic Recipe for Machine Learning	-1
🌐4- Regularization	1
🌐5-Why Regularization Reduces Overfitting	1	---
🌐6- Dropout Regularization	1-2
🌐7- Other Regularization Methods	1-2
🌐8- Normalizing Inputs	1
🌐9- Vanishing-Exploding Gradients	1
🌐10- Weight Initialization for Deep Networks	1
🌐11- Numerical Approximation of Gradients	1
🌐12- How Gradient Checking Can Save You Time and Help Debug Neural Networks	1-2

Week 2-📚Chapter2:Optimization Algorithms

Topic Name/Tutorial	Video	Code
🌐1-Mini-batch Gradient Descent⭐	1

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🗞️📚Other Best Free Resources to Learn Deep Learning

• Assignment 1: Python Basics with Numpy

##Alogrithems - DL0101EN-3-1-Regression-with-Keras-py-v1.0.ipynb - DL0101EN-3-2-Classification-with-Keras-py-v1.0.ipynb - Keras - Tutorial - Happy House v1.ipynb - Keras_for_Beginners_Implementing_a_Convolutional_Neural_Network - Keras_for_Beginners_Building_Your_First_Neural_Network.ipynb

• Need to Implement
  • Text Classification — CNN with LSTM
  • Step-by-Step Guide — Building a Prediction Model in Python
• Important Website
  • victorzhou

💻 Workflow:

• Fork the repository

• Clone your forked repository using terminal or gitbash.

• Make changes to the cloned repository

• Add, Commit and Push

• Then in Github, in your cloned repository find the option to make a pull request

print("Start contributing for Deep Learning")

⚙️ Things to Note

• Make sure you do not copy codes from external sources because that work will not be considered. Plagiarism is strictly not allowed.
• You can only work on issues that have been assigned to you.
• If you want to contribute the algorithm, it's preferrable that you create a new issue before making a PR and link your PR to that issue.
• If you have modified/added code work, make sure the code compiles before submitting.
• Strictly use snake_case (underscore_separated) in your file_name and push it in correct folder.
• Do not update the README.md.

✨Top Contributors

We would love your help in making this repository even better! If you know of an amazing AI course that isn't listed here, or if you have any suggestions for improvement in any course content, feel free to open an issue or submit a course contribution request.

                   Together, let's make this the best AI learning hub website! 🚀

Thanks goes to these Wonderful People. Contributions of any kind are welcome!🚀