Usually, people apply machine learning (ML) methods and algorithms using one of two programming languages: Python or R. Books, courses, and tutorials about machine learning most often use one of these languages as well (or both).
Python is a general-purpose programming language used not only for machine learning but also for scientific computing, back-end web development, desktop applications, etc. R is created primarily for statisticians. However, they have at least two common characteristics:
In many cases, ML algorithms are implemented in Fortran, C, C++, or Cython and called from Python or R.
Java is also used for Machine Learning, but usually by professional programmers.
Supported supervised learning methods are:
Besides, ml.js offers several unsupervised learning methods:
TensorFlow is one of the most popular Machine Learning libraries. It focuses on various types and structures of artificial neural networks, including deep networks as well as the components of the networks.
TensorFlow is a very comprehensive library that still enables building and training models easily. It supports a huge variety of network layers, activation functions, optimizers, and other components. It has good performance and offers GPU support.
License: Apache 2.0.
It provides advanced options like:
brain.js saves and loads models to/from JSON files.
ConvNetJS is another library for neural networks and deep learning. It enables training neural networks in browsers. In addition to classification and regression problems, it has the reinforcement learning module (using Q-learning) that is still experimental. ConvNetJS provides support for convolutional neural networks that excel in image recognition.
In ConvNetJS, neural networks are lists of layers. It provides the following layers:
It supports several important activation functions like:
as well as the optimizers such as:
It also provides the possibility of GPU execution in browsers.
A very convenient feature of WebDNN is the possibility to convert and use the models pre-trained with PyTorch, TensorFlow, Keras, Caffemodel, or Chainer.
Have a lot of fun exploring them and thank you for reading!
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You got intrigued by the machine learning world and wanted to get started as soon as possible, read all the articles, watched all the videos, but still isn’t sure about where to start, welcome to the club.
Machine learning is quite an exciting field to study and rightly so. It is all around us in this modern world. From Facebook’s feed to Google Maps for navigation, machine learning finds its application in almost every aspect of our lives. It is quite frightening and interesting to think of how our lives would have been without the use of machine learning. That is why it becomes quite important to understand what is machine learning, its applications and importance.
Machine Learning is an utilization of Artificial Intelligence (AI) that provides frameworks the capacity to naturally absorb and improve as a matter of fact without being expressly modified. AI centers round the improvement of PC programs which will get to information and use it learn for themselves.The way toward learning starts with perceptions or information, for instance , models, direct understanding, or guidance, so on look for designs in information and choose better choices afterward hooked in to the models that we give. The essential point is to allow the PCs adapt consequently without human intercession or help and modify activities as needs be.