Machine Learning is, undoubtedly, one of the most talked-about technologies of today’s date. No wonder you would be eager to start working on a Machine Learning project as soon as you get the opportunity. However, it is always better to get your hands on the necessary resources before you begin working on such important projects. You might check assignment help for more information
Image Source: Pixabay.com
This library can be further used for plotting and graphics functionality for various purposes, including data analysis, data visualisation and more. Some of the crucial features of this library are:
- 35+ probability distributions for handling probabilistic data
- 150+ special in-built math functions including limit-continuity, integration, differentiation, data analysis, etc.)
- 50+ sample dataset for conducting tests and development.
You can use TensorFlow.js for various purposes, including training the neural network in the application, developing Machine Learning models and application, diagnosis of disease, and more. You can also import the pre-existing trained models to the browser. It automatically provides support for WebGL and Node.js.
Use the following codes to set up Brain.js:
npm install brain.js
Also, you can use the code mentioned below to add the library in the browser:
However, if you want to install the Naïve Bayesian Classifier, you need to use the following code:
npm install classifier
This available open-source framework allows you to run Machine Learning models in the browser. It allows you to run Machine Learning models in the browser. You can even use it to run Keras models in the browser quite easily with the support of GPU via WebGL. If CPU mode is allowed, these models also work in Node.js.
Keras.js also offers support for model training using any backend framework, such as Next.js, Microsoft Cognitive Toolkit (CNTK), Metero.js, etc. There are several Keras models which can be deployed on the client-side browser. Some of them are 50-layer Residual Network (trained on ImageNet), Inception v3 (trained on ImageNet), and Convolutional variation auto-encoder (trained on MNIST).
Here are some of the Keras models which you can run in the browser:
- Convolutional variation auto-encoder
- Inception v3
npm i convnetjs
Beginners may find this library difficult to use, and it is complex and hard to manage. In order to use this library, you need to have common knowledge of this field. Also, the processing may become slower in this when compared to other tools equivalent to this.
ML.js gives us the ability to take machine learning to the next level. ML Hub-Team prefers working with new technologies and the great implementations of them. You can use the following code to set up the ML.js tool:
ML.js supports all the Machine Learning algorithms mentioned below:
- Decision tree: CART
- K-Nearest Neighbor (KNN)
- Logistic regression
- Multi-variate linear regression
- Naïve Bayes
- Partial least squares (PLS)
- Random forest
- Simple linear regression
- K-means clustering
- Principal component analysis (PCA)
Author bio: Eric jones is a web developer who is currently working for a reputed eCommerce company based in Australia. He is also a part of the team of experts at MyAssignmenthelp.com, where he offers Java assignment help to students on request.