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This chapter intends to introduce the main objects and concepts in TensorFlow. We also introduce how to access the data for the rest of the book and provide additional resources for learning about TensorFlow. After we have established the basic objects and methods in TensorFlow, we now want to establish the components that make up TensorFlow algorithms. We start by introducing computational graphs, and then move to loss functions and back propagation. We end with creating a simple classifier and then show an example of evaluating regression and classification algorithms.
Python implementations of some of the fundamental Machine Learning models and algorithms from scratch. The purpose of this project is not to produce as optimized and computationally efficient algorithms as possible but rather to present the inner workings of them in a transparent and accessible way.
Genann is a minimal, well-tested library for training and using feedforward artificial neural networks (ANN) in C. Its primary focus is on being simple, fast, reliable, and hackable. It achieves this by providing only the necessary functions and little extra. Genann is self-contained in two files: genann.c and genann.h. To use Genann, simply add those two files to your project.
DEvol (DeepEvolution) is a basic proof of concept for genetic architecture search in Keras. The current setup is designed for classification problems, though this could be extended to include any other output type as well. See example/demo.ipynb for a simple example.
Read the research paper BF-Programmer: A Counterintuitive Approach to Autonomously Building Simplistic Programs Using Genetic Algorithms. AI-Programmer is an experiment with using artificial intelligence and genetic algorithms to automatically generate programs. Successfully created programs by the AI include: hello world, hello , addition, subtraction, reversing a string, fibonnaci sequence, 99 bottles of beer on the wall, and more. It's getting smarter. In short, it's an AI genetic algorithm implementation with self modifying code.
Cars have to navigate through a course without touching the walls or any other obstacles of the course. A car has five front-facing sensors which measure the distance to obstacles in a given direction. The readings of these sensors serve as the input of the car's neural network. Each sensor points into a different direction, covering a front facing range of approximately 90 degrees. The maximum range of a sensor is 10 unity units. The output of the Neural Network then determines the car’s current engine and turning force. If you would like to tinker with the parameters of the simulation, you can do so in the Unity Editor. If you would simply like to run the simulation with default parameters, you can start the built file [Builds/Applying EANNs.exe](Builds/Applying EANNs.exe).
The following example attempts to minimize the Drop-Wave function using a genetic algorithm. The Drop-Wave function is known to have a minimum value of -1 when each of it's arguments is equal to 0. All the examples can be found in this repository.
There is a lot of intellectual fog around the concept of genetic algorithms (GAs). It's important to appreciate the fact that GAs are composed of many nuts and bolts. There isn't a single definition of genetic algorithms. gago is intended to be a toolkit where one may run many kinds of genetic algorithms, with different evolution models and various genetic operators.
This repository contains the code and pdf of a series of blog post called "dissecting reinforcement learning" which I published on my blog mpatacchiola.io/blog. Moreover there are links to resources that can be useful for a reinforcement learning practitioner. If you have some good references which may be of interest please send me a pull request and I will integrate them in the README. The source code is contained in src with the name of the subfolders following the post number. In pdf there are the A3 documents of each post for offline reading. In images there are the raw svg file containing the images used in each post.
In these tutorials, we will demonstrate and visualize algorithms like Genetic Algorithm, Evolution Strategy, NEAT etc. All methods mentioned below have their video and text tutorial in Chinese. Visit 莫烦 Python for more.
CURRENTLY NOT WORKING! There will be a further notice when it's updated.NEAT (NeuroEvolution of Augmenting Topologies) is a neuroevolution algorithm by Dr. Kenneth O. Stanley which evolves not only neural networks' weights but also their topologies. This method starts the evolution process with genomes with minimal structure, then complexifies the structure of each genome as it progresses. You can read the original paper from here.
Collection of Python libraries to parse bioinformatics files, or perform computation related to assembly, annotation, and comparative genomics. Following modules are available as generic Bioinformatics handling methods.
Symbolic regression solver, based on genetic programming methodology. In practice, on of the most generic problems - is reconstruction of original function, having the information about its values in some specific points.
The Blue Brain Python Optimisation Library (BluePyOpt) is an extensible framework for data-driven model parameter optimisation that wraps and standardises several existing open-source tools. It simplifies the task of creating and sharing these optimisations, and the associated techniques and knowledge. This is achieved by abstracting the optimisation and evaluation tasks into various reusable and flexible discrete elements according to established best-practices.