Machine learning is one of the most popular emerging technologies in the digital era, led by big data. It involves feeding data into the software application and writing algorithms to build logic based on the data. Therefore, it is an artificial intelligence branch that predicts accurate outcomes with limited human intervention and explicit programming.
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Brain.js is a reliable resource for creating neural networks and training them on input/output data. You can run the library using Node.js or load a CDN browser directly onto a web page. Brain.js performs GPU computations and falls back to Java when GPU is unavailable. Moreover, you do not need to have in-depth knowledge of neural networks to implement it. And you can easily integrate trained models on your website or import/export them to JSON format. You can read the full documentation and go through the live examples available on the website.
It is a Node.js and browser library that allows developers to build any neural network they want. Synaptic is architecture-agnostic and boasts of an active maintenance community. You can test and compare different ML algorithms with its built-in architectures and go through a comprehensive introduction on neural networks. Synaptic contains many practical demos and tutorials that uncover machine learning and its working.
ConvNetJS is a popular project on GitHub having features and tutorials, most of which are community-driven. As an advanced deep learning library for Java, ConvNetJS works entirely in your browser and supports many learning techniques. Initially, it was developed by a Ph.D. student at Stanford University and later extended by contributors. With ConvNetJS, you can expect to gain an understanding of the following things:
- Common neural network modules
- Specifying and training convolutional networks capable of processing images
- An experimental reinforcement learning module
- Classification and regression
7. Land Lines
Land Lines is a web experiment that allows users to explore the Google Earth dataset without making any calls to the backend server. With machine learning capabilities, data optimization, and graphics card, the application can find satellite images similar to the users’ doodles. Land Lines can also work well on mobile devices. You can find the full source code of this project on GitHub.
8. Thing Translator
9. Deep Playground
If you want to play with neural networks and dig into their components, you can check out the playground library on GitHub. It offers an educational web app complete with a UI (that lets you control the input data) and a number of neurons, algorithms, and metrics. The project documentation is open-source and written in the TypeScript language.
DeepForge provides a developer-friendly environment for deep learning. It is based on Node.js and MongoDB, running directly in the browser. Here are some of its key features:
- Aids design with a simple graphical interface
- Supports training models on remote machines
- Possesses a built-in version control
This free machine learning library for Java is inspired by the Weka bird, a flightless species found in New Zealand. It is a collection of algorithms focused on deep learning. You can learn the following skills with this project:
- Data mining and data preparation tools
- Classification, regression, and clustering
- Visualization, and so on.
It is a deep learning library that makes use of distributed computing frameworks like Apache Spark and Hadoop. Deeplearning4j is compatible with virtual machine languages like Scala and Kotlin. It aims to bring AI to business environments with detailed API documentation and sample projects.
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What are the cons of using WEKA?
Data pre-processing, classification, regression, clustering, association rules, and visualization are all tools included in WEKA. Despite the fact that WEKA may be connected with the Python programming language, customers find the procedure excessively time-consuming. WEKA does not offer a wide range of analytical choices; instead, it is limited to a few. When compared to other tools, WEKA does not support all IDEs. Therefore, connecting WEKA with a user interface designed by any other IDE requires extensive and sophisticated scripting.
What are the pros of using TensorFlow?