Description

Welcome to the fascinating world of Deep Learning! This repository serves as a playground for small projects predominantly revolving around Deep Learning and Data Science. The ideas here are intricately linked to my articles published on Medium, functioning as an educational scaffolding for my readers. My aim is to document my learning journey while empowering others to comprehend the intricate aspects of neural networks. The hope is that the insights provided in this repository spark curiosity and utility.

Hit the Ground Running

To get you started on your journey, here’s a step-by-step guide:

1. Clone the Repository: Use the command below to get a local copy of the repository.

git clone https://github.com/SkalskiPI/LearnDeepLearning.py.git

2. Navigate to the Main Directory:

cd ILearnDeepLearning.py

3. Set Up and Activate Python Environment:
• Install Python Virtual Environment

apt-get install python3-venv

• Create a new virtual environment

python3 -m venv .env

• Activate the environment

source .env/bin/activate

4. Install All Required Packages:

pip install -r requirements.txt

Deep Dive into Math Behind Deep Networks

This project helps visualize complicated concepts like gradient descent and activation functions. This part is designed to correlate with my Medium article. Through dynamic visualizations, you will not only grasp these ideas but also see them animated!

Let’s Code a Neural Network in Plain NumPy

Its time for action! In this section, we dive into creating a neural network using only NumPy, which results in a better understanding of how these magical models function. For additional details, refer to my comprehensive article. The accompanying code allows you to explore differences between a basic NumPy implementation and a Keras model.

Preventing Deep Neural Network from Overfitting

This project showcases methods to prevent overfitting in deep neural networks. Understand the reasons behind overfitting, analyze regularization methods, and scrutinize the impact on model performance. For detailed guidance, kindly check out my Medium article.

Troubleshooting

• If you encounter issues while cloning the repository, ensure that your Git is properly installed and configured.
• For activation-related issues with your Python environment, verify your installation steps and check if you’re using a supported Python version.
• Should you struggle with package installations, confirm that the packages listed in requirements.txt are available in your Python environment.
• If any visualizations don’t render correctly, check the file paths to ensure all dependencies are correctly linked.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Continuing the Exploration

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.