### mind - A neural network library built in JavaScript

•        58

A flexible neural network library for Node.js and the browser. Check out a live demo of a movie recommendation engine built with Mind. Use plugins created by the Mind community to configure pre-trained networks that can go straight to making predictions.

http://stevenmiller888.github.io/mindjs.net/
https://github.com/stevenmiller888/mind

Dependencies:

emitter-component : ^1.1.1
htan : 0.0.3
htan-prime : 0.0.1
node-matrix : 0.1.1
samples : 0.0.2
sigmoid : 0.0.1
sigmoid-prime : 0.0.1

 Tags mind neural-network prediction machine-learning learning sigmoid network neuron neural nn Implementation Javascript License MIT Platform OS-Independent

## EmojiIntelligence - Neural Network built in Apple Playground using Swift

•    Swift

I used this challenge to learn more about neural networks and machine learning. A neural network consists of layers, and each layer has neurons. My network has three layers: an input layer, a hidden layer, and an output layer. The input to my network has 64 binary numbers. These inputs are connected to the neurons in the hidden layer. The hidden layer performs some computation and passes the result to the output layer neuron out. This also performs a computation and then outputs a 0 or a 1. The input layer doesn’t actually do anything, they are just placeholders for the input value. Only the neurons in the hidden layer and the output layer perform computations. The neurons from the input layer are connected to the neurons in the hidden layer. Likewise, both neurons from the hidden layer are connected to the output layer. These kinds of layers are called fully-connected because every neuron is connected to every neuron in the next layer. Each connection between two neurons has a weight, which is just a number. These weights form the brain of my network. For the activation function in my network, I use the sigmoid function. Sigmoid is a mathematical function. The sigmoid takes in some number x and converts it into a value between 0 and 1. That is ideal for my purposes, since I am dealing with binary numbers. This will turn a linear equation into something that is non-linear. This is important because without this, the network wouldn’t be able to learn any interesting things. I have already mentioned that the input to this network are 64 binary numbers. I resize the drawn image to 8x8 pixels which makes together 64 pixels. I go through the image and check each pixel if the pixel has a pink color I add a 1 to my array else I add a 0. At the end I will have 64 binary numbers which I can add to my input layer.

## ImageAI - A python library built to empower developers to build applications and systems with self-contained Computer Vision capabilities

•    Python

A python library built to empower developers to build applications and systems with self-contained Deep Learning and Computer Vision capabilities using simple and few lines of code. Built with simplicity in mind, ImageAI supports a list of state-of-the-art Machine Learning algorithms for image prediction, custom image prediction, object detection, video detection, video object tracking and image predictions trainings. ImageAI currently supports image prediction and training using 4 different Machine Learning algorithms trained on the ImageNet-1000 dataset. ImageAI also supports object detection, video detection and object tracking using RetinaNet, YOLOv3 and TinyYOLOv3 trained on COCO dataset. Eventually, ImageAI will provide support for a wider and more specialized aspects of Computer Vision including and not limited to image recognition in special environments and special fields.

## machine_learning_basics - Plain python implementations of basic machine learning algorithms

•    Jupyter

This repository contains implementations of basic machine learning algorithms in plain Python (Python Version 3.6+). All algorithms are implemented from scratch without using additional machine learning libraries. The intention of these notebooks is to provide a basic understanding of the algorithms and their underlying structure, not to provide the most efficient implementations. After several requests I started preparing notebooks on how to preprocess datasets for machine learning. Within the next months I will add one notebook for each kind of dataset (text, images, ...). As before, the intention of these notebooks is to provide a basic understanding of the preprocessing steps, not to provide the most efficient implementations.

## t81_558_deep_learning - Washington University (in St

•    Jupyter

Deep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks of much greater complexity. Deep learning allows a neural network to learn hierarchies of information in a way that is like the function of the human brain. This course will introduce the student to computer vision with Convolution Neural Networks (CNN), time series analysis with Long Short-Term Memory (LSTM), classic neural network structures and application to computer security. High Performance Computing (HPC) aspects will demonstrate how deep learning can be leveraged both on graphical processing units (GPUs), as well as grids. Focus is primarily upon the application of deep learning to problems, with some introduction mathematical foundations. Students will use the Python programming language to implement deep learning using Google TensorFlow and Keras. It is not necessary to know Python prior to this course; however, familiarity of at least one programming language is assumed. This course will be delivered in a hybrid format that includes both classroom and online instruction. This syllabus presents the expected class schedule, due dates, and reading assignments. Download current syllabus.

## one-pixel-attack-keras - Keras reimplementation of "One pixel attack for fooling deep neural networks" using differential evolution on Cifar10 and ImageNet

•    Jupyter

How simple is it to cause a deep neural network to misclassify an image if an attacker is only allowed to modify the color of one pixel and only see the prediction probability? Turns out it is very simple. In many cases, an attacker can even cause the network to return any answer they want. The following project is a Keras reimplementation and tutorial of "One pixel attack for fooling deep neural networks".

## tensorflow-image-detection - A generic image detection program that uses Google's Machine Learning library, Tensorflow and a pre-trained Deep Learning Convolutional Neural Network model called Inception

•    Python

A generic image detection program that uses Google's Machine Learning library, Tensorflow and a pre-trained Deep Learning Convolutional Neural Network model called Inception. This model has been pre-trained for the ImageNet Large Visual Recognition Challenge using the data from 2012, and it can differentiate between 1,000 different classes, like Dalmatian, dishwasher etc. The program applies Transfer Learning to this existing model and re-trains it to classify a new set of images.

## LSTM-Human-Activity-Recognition - Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN (Deep Learning algo)

•    Jupyter

Compared to a classical approach, using a Recurrent Neural Networks (RNN) with Long Short-Term Memory cells (LSTMs) require no or almost no feature engineering. Data can be fed directly into the neural network who acts like a black box, modeling the problem correctly. Other research on the activity recognition dataset can use a big amount of feature engineering, which is rather a signal processing approach combined with classical data science techniques. The approach here is rather very simple in terms of how much was the data preprocessed. Let's use Google's neat Deep Learning library, TensorFlow, demonstrating the usage of an LSTM, a type of Artificial Neural Network that can process sequential data / time series.

## Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials - A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Automotives, Retail, Pharma, Medicine, Healthcare by Tarry Singh until at-least 2020 until he finishes his Ph

•    Python

A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Automotives, Retail, Pharma, Medicine, Healthcare by Tarry Singh until at-least 2020 until he finishes his Ph.D. (which might end up being inter-stellar cosmic networks! Who knows! 😀)

## digit-classifier - A single handwritten digit classifier, using the MNIST dataset. Pure Numpy.

•    Python

An implementation of multilayer neural network using numpy library. The implementation is a modified version of Michael Nielsen's implementation in Neural Networks and Deep Learning book. Note: There are other functions in use other than sigmoid, but this information for now is sufficient for beginners.

## have-fun-with-machine-learning - An absolute beginner's guide to Machine Learning and Image Classification with Neural Networks

•    Python

Also available in Chinese (Traditional). This is a hands-on guide to machine learning for programmers with no background in AI. Using a neural network doesn’t require a PhD, and you don’t need to be the person who makes the next breakthrough in AI in order to use what exists today. What we have now is already breathtaking, and highly usable. I believe that more of us need to play with this stuff like we would any other open source technology, instead of treating it like a research topic.

## ConvNetJS - Javascript implementation of Neural networks

•    Javascript

ConvNetJS is a Javascript implementation of Neural networks, It currently supports Common Neural Network modules, Classification (SVM/Softmax) and Regression (L2) cost functions, A MagicNet class for fully automatic neural network learning (automatic hyperparameter search and cross-validatations), Ability to specify and train Convolutional Networks that process images, An experimental Reinforcement Learning module, based on Deep Q Learning.

## autonomous-rc-car - Autonomous RC car using Raspberry Pi and ANN

•    Python

This project aims to build an autonomous rc car using supervised learning of a neural network with a single hidden layer. We have not used any Machine Learning libraries since we wanted to implement the neural network from scratch to understand the concepts better. We have modified a remote controlled car to remove the dependency on the RF remote controller. A Raspberry Pi controls the DC motors via an L293D Motor Driver IC. You can find a post explaining this project in detail here. Here's a video of the car in action. We will be referring the DC motor controlling the left/right direction as the front motor and the motor controlling the forward/reverse direction as the back motor. Connect the BACK_MOTOR_DATA_ONE and BACK_MOTOR_DATA_TWO GPIO pins(GPIO17 and GPIO27) of the Raspberry Pi to the Input pins for Motor 1(Input 1, Input 2) and the BACK_MOTOR_ENABLE_PIN GPIO pin(GPIO22) to the Enable pin for Motor 1(Enable 1,2) in the L293D Motor Driver IC. Connect the Output pins for Motor 1(Output 1, Output 2) of the IC to the back motor.

## PyTorch-Tutorial - Build your neural network easy and fast

•    Jupyter

In these tutorials for pyTorch, we will build our first Neural Network and try to build some advanced Neural Network architectures developed recent years. Thanks for liufuyang's notebook files which is a great contribution to this tutorial.

## tensorflow-tutorial - TensorFlow and Deep Learning Tutorials

•

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.

## deep-learning-book - Repository for "Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python"

•    Jupyter

Repository for the book Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python. Deep learning is not just the talk of the town among tech folks. Deep learning allows us to tackle complex problems, training artificial neural networks to recognize complex patterns for image and speech recognition. In this book, we'll continue where we left off in Python Machine Learning and implement deep learning algorithms in PyTorch.

## DeepNeuralClassifier - Deep neural network using rectified linear units to classify hand written symbols from the MNIST dataset

•    Julia

Example scripts for a deep, feed-forward neural network have been written from scratch. No machine learning packages are used, providing an example of how to implement the underlying algorithms of an artificial neural network. The code is written in the Julia, a programming language with a syntax similar to Matlab. The neural network is trained on the MNIST dataset of handwritten digits. On the test dataset, the neural network correctly classifies 98.42 % of the handwritten digits. The results are pretty good for a fully connected neural network that does not contain a priori knowledge about the geometric invariances of the dataset like a Convolutional Neural Network would.

## Machine-Learning-Tutorials - machine learning and deep learning tutorials, articles and other resources

•

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.

## glow - Compiler for Neural Network hardware accelerators

•    C++

Glow is a machine learning compiler and execution engine for various hardware targets. It is designed to be used as a backend for high-level machine learning frameworks. The compiler is designed to allow state of the art compiler optimizations and code generation of neural network graphs. This library is experimental and in active development. Glow lowers a traditional neural network dataflow graph into a two-phase strongly-typed intermediate representation (IR). The high-level IR allows the optimizer to perform domain-specific optimizations. The lower-level instruction-based address-only IR allows the compiler to perform memory-related optimizations, such as instruction scheduling, static memory allocation and copy elimination. At the lowest level, the optimizer performs machine-specific code generation to take advantage of specialized hardware features. Glow features a lowering phase which enables the compiler to support a high number of input operators as well as a large number of hardware targets by eliminating the need to implement all operators on all targets. The lowering phase is designed to reduce the input space and allow new hardware backends to focus on a small number of linear algebra primitives. The design philosophy is described in an arXiv paper.

## PyTorch-NLP - Supporting Rapid Prototyping with a Toolkit (incl. Datasets and Neural Network Layers)

•    Python

PyTorch-NLP, or torchnlp for short, is a library of neural network layers, text processing modules and datasets designed to accelerate Natural Language Processing (NLP) research. Join our community, add datasets and neural network layers! Chat with us on Gitter and join the Google Group, we're eager to collaborate with you.

## Accord.NET - Machine learning, Computer vision, Statistics and general scientific computing for .NET

•    CSharp

The Accord.NET project provides machine learning, statistics, artificial intelligence, computer vision and image processing methods to .NET. It can be used on Microsoft Windows, Xamarin, Unity3D, Windows Store applications, Linux or mobile.

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