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This repository contains a topic-wise curated list of Machine Learning and Deep Learning tutorials, articles and other resources. Other awesome lists can be found in this list. If you want to contribute to this list, please read Contributing Guidelines.
A simple and well designed structure is essential for any Deep Learning project, so after a lot of practice and contributing in tensorflow projects here's a tensorflow project template that combines simplcity, best practice for folder structure and good OOP design. The main idea is that there's much stuff you do every time you start your tensorflow project, so wrapping all this shared stuff will help you to change just the core idea every time you start a new tensorflow project. You will find a template file and a simple example in the model and trainer folder that shows you how to try your first model simply.
Some examples require MNIST dataset for training and testing. Don't worry, this dataset will automatically be downloaded when running examples (with input_data.py). MNIST is a database of handwritten digits, for a quick description of that dataset, you can check this notebook.
Kur is a system for quickly building and applying state-of-the-art deep learning models to new and exciting problems. Kur was designed to appeal to the entire machine learning community, from novices to veterans. It uses specification files that are simple to read and author, meaning that you can get started building sophisticated models without ever needing to code. Even so, Kur exposes a friendly and extensible API to support advanced deep learning architectures or workflows.
DeepLearning.scala is a DSL for creating complex neural networks. With the help of DeepLearning.scala, regular programmers are able to build complex neural networks from simple code. You write code almost as usual, the only difference being that code based on DeepLearning.scala is differentiable, which enables such code to evolve by modifying its parameters continuously.
Use Case: Learning best color matches of font and background color for an improved web accessibility. This example project demonstrates how neural networks may be used to solve a binary classification problem. It uses deeplearn.js to predict accessible font colors based on background colors. Read more about it. If you have problems to follow the view layer implementation with React, checkout this book to learn the fundamentals of it.
The documentation generated using Doxygen can be found in documentaion folder. Please open documentation/html/index.html to view the documentation. If you are someone looking to understand deep learning models by implementing or if you are an expert and want to improve the code or fix bugs, you are very welcome. Feel free to suggest improvements and fork the repository.