ILearnDeepLearning.py: A Deep Learning Journey through Practice
Introduction
ILearnDeepLearning.py is a project designed to provide a hands-on learning experience in the fields of Deep Learning and Data Science. The repository houses a collection of small projects aimed at deepening the understanding of neural networks and machine learning principles. It complements various articles published on Medium, providing practical applications and deeper insights into the discussed topics. This project serves as a learning tool both for the creator and others interested in understanding more about neural networks.
Setting Up and Running the Project
To get started with ILearnDeepLearning.py, follow these steps:
-
Clone the Repository:
git clone https://github.com/SkalskiP/ILearnDeepLearning.py.git cd ILearnDeepLearning.py -
Set Up a Python Environment:
apt-get install python3-venv python3 -m venv .env source .env/bin/activate -
Install Required Packages:
pip install -r requirements.txt
Key Projects and Features
Deep Dive into Math Behind Deep Networks
This project visualizes complex concepts such as gradient descent and activation functions. By doing so, it helps users understand the mathematical foundations that underpin neural networks. The visualizations created include animations showing how classification boundaries evolve as a model trains. These visual tools are invaluable for understanding how neural networks learn from data.
Coding a Neural Network in Plain NumPy
Here, the focus is on building a neural network using only NumPy. This approach demystifies the operations inside a neural network and allows users to see the step-by-step process of neural network training without relying on high-level libraries like Keras. The project includes performance comparisons between this basic implementation and models created with Keras.
Preventing Deep Neural Networks from Overfitting
Overfitting is a common issue in machine learning models, where a model learns the training data too well and fails to generalize to new data. This section explores methods to prevent overfitting, using visualizations to demonstrate the effects of different regularization techniques on the weight matrix of a neural network.
Speeding Up Neural Network Training with Optimizers
This project delves into optimization strategies that can improve the speed and accuracy of neural network training. By understanding these optimizations, users can make informed decisions about their machine learning workflows to enhance performance.
Creating Animated Graphs
Visualization is a powerful tool for understanding and communicating complex data. This section provides a tutorial on creating animated visualizations using Matplotlib, showing users how to effectively display data and insights from neural network training processes.
Understanding the Math Behind Convolutional Neural Networks
A theoretical exploration of Convolutional Neural Networks (CNNs), covering the mathematical concepts and providing simple visualizations of convolution effects using selected filters. This foundational knowledge prepares users for implementing CNNs in future projects.
Custom Object Detection Model with YOLO
Branching into practical applications, this project demonstrates how to train a YOLO (You Only Look Once) model for detecting custom objects. It shows that with minimal coding, substantial results in object detection can be achieved, using implementations of YOLO.
Explainable AI: Understanding Image Classifier Predictions
With the growing importance of explainable AI, this section compares different libraries—ELI5, LIME, and SHAP—for providing explanations of image classifier predictions. Understanding these tools is crucial for building responsible AI systems that not only make predictions but also explain their reasoning.
License and Resources
ILearnDeepLearning.py is licensed under the MIT License, allowing for wide use and modification. The project also includes a curated list of insightful articles and papers that could serve as inspiration for future projects.
Overall, ILearnDeepLearning.py offers a wealth of resources for anyone looking to deepen their understanding of deep learning through practical, hands-on projects. Whether you're a beginner or have some experience, this project is a helpful companion in your learning journey.