Only a few of them are coming from experienced developers. They know the details of these technologies very well.
Each library has its own unique functionality. You can also use as many or as few as you need for your project.
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This library offers an interface to Google’s TensorFlow, an open-source machine learning library designed to work with numerical data.
Because it is a web-based library, so you don’t need to install anything on your computer in order to use it.
However, you will need some data that you want to pass through a neural network. That make predictions obviously easy and accurate about other inputs of similar characteristics.
TensorFlow.js can make better computations over large dataset. Which allows computers to make sense of their environment in new ways.
It’s so easy that there are already many experiments available in the market like The Great AI Awakening or Voicebox.
One of Mind’s features is using a matrix implementation to analyze training data while letting programmers alter the network architecture.
This library makes it very easy to get start on your projects. It is faster pluggable and simpler to download and submit plugins than other libraries.
Another benefit of Mind is the simplicity with which pre-trained networks may be configured.
There are plenty more great libraries out there. The ones mentioned here are some of the most popular and have high development activity.
Andrej Karpathy, a Stanford Ph.D. student, developed this project in an effort to make neural networks more approachable.
The library can use WebGL shaders. It works with popular frameworks like Keras, TensorFlow.js, Google’s Protobuf Compiler, or Caffe2 from Facebook among others.
It uses Math.js for its mathematical functions to provide flexible numerical operations between tensors or with its built-in data types. Which allows you to create complex structures from scratch or from existing ones using functional transformations like maps, filters, etc.
The library also comes with a GUI that allows you to develop your own networks in an intuitive way. It has a fairly rich set of features. It includes a native BatchNorm layer and Caffe-ConvNet model conversion tool among others.
Deeplearn.js is a product of the Google Brain PAIR team. Its purpose is to create powerful interactive machine learning tools for the browser.
This library is useful for a variety of purposes ranging from education to model understanding to art projects.
It is already in production by Yahoo! Labs and Google DeepMind, among others. It is not a research prototype but a robust library that is ready to solve real-world problems.
Library of Neural Network models that is using a simple syntax. Models include neural networks, support vector machines, deep belief networks, recurrent neural networks, and more.
You can combine models by stacking them on top of each other to create more complex models. The brain.js also supports online learning for most of its models.
This allows them to adjust their parameters as new data is provided. It makes it useful in supervised learning situations where data is not static.
The library includes built-in training functionality for many types of models. This allows code to run both synchronously or asynchronously making it ideal for large datasets or tight latency requirements.
Because of its architecture-free algorithm and pre-manufactured structure, this library can build and train any first-order or second-order neural network architecture.
It can import and export networks to JSON in order to connect to other networks or even gate connections.
Hopfield networks, state machines, multilayer perceptrons, long short-term memory networks, and other interesting architectures are the best examples of it.
This library can train any neural network using tests such as an Embedded Reber Grammar test, solving an XOR, etc.
It also aids in the comparison of the performance of various neural network architectures.