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.brain.js
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!
This complete Machine Learning full course video covers all the topics that you need to know to become a master in the field of Machine Learning.
Machine Learning Full Course | Learn Machine Learning | Machine Learning Tutorial
It covers all the basics of Machine Learning (01:46), the different types of Machine Learning (18:32), and the various applications of Machine Learning used in different industries (04:54:48).This video will help you learn different Machine Learning algorithms in Python. Linear Regression, Logistic Regression (23:38), K Means Clustering (01:26:20), Decision Tree (02:15:15), and Support Vector Machines (03:48:31) are some of the important algorithms you will understand with a hands-on demo. Finally, you will see the essential skills required to become a Machine Learning Engineer (04:59:46) and come across a few important Machine Learning interview questions (05:09:03). Now, let's get started with Machine Learning.
Below topics are explained in this Machine Learning course for beginners:
Basics of Machine Learning - 01:46
Why Machine Learning - 09:18
What is Machine Learning - 13:25
Types of Machine Learning - 18:32
Supervised Learning - 18:44
Reinforcement Learning - 21:06
Supervised VS Unsupervised - 22:26
Linear Regression - 23:38
Introduction to Machine Learning - 25:08
Application of Linear Regression - 26:40
Understanding Linear Regression - 27:19
Regression Equation - 28:00
Multiple Linear Regression - 35:57
Logistic Regression - 55:45
What is Logistic Regression - 56:04
What is Linear Regression - 59:35
Comparing Linear & Logistic Regression - 01:05:28
What is K-Means Clustering - 01:26:20
How does K-Means Clustering work - 01:38:00
What is Decision Tree - 02:15:15
How does Decision Tree work - 02:25:15
Random Forest Tutorial - 02:39:56
Why Random Forest - 02:41:52
What is Random Forest - 02:43:21
How does Decision Tree work- 02:52:02
K-Nearest Neighbors Algorithm Tutorial - 03:22:02
Why KNN - 03:24:11
What is KNN - 03:24:24
How do we choose 'K' - 03:25:38
When do we use KNN - 03:27:37
Applications of Support Vector Machine - 03:48:31
Why Support Vector Machine - 03:48:55
What Support Vector Machine - 03:50:34
Advantages of Support Vector Machine - 03:54:54
What is Naive Bayes - 04:13:06
Where is Naive Bayes used - 04:17:45
Top 10 Application of Machine Learning - 04:54:48
How to become a Machine Learning Engineer - 04:59:46
Machine Learning Interview Questions - 05:09:03
By the end of this video tutorial, you will have built and deployed a web application that runs a neural network in the browser to classify images! To get there, we'll learn about client-server deep learning architectures, converting Keras models to TFJS models, serving models with Node.js, tensor operations, and more!
⌨️ 0:00 - Intro to deep learning with client-side neural networks
⌨️ 6:06 - Convert Keras model to Layers API format
⌨️ 11:16 - Serve deep learning models with Node.js and Express
⌨️ 19:22 - Building UI for neural network web app
⌨️ 27:08 - Loading model into a neural network web app
⌨️ 36:55 - Explore tensor operations with VGG16 preprocessing
⌨️ 45:16 - Examining tensors with the debugger
⌨️ 1:00:37 - Broadcasting with tensors
⌨️ 1:11:30 - Running MobileNet in the browser
Google releases and showcases TensorFlow.js at the TensorFlow Dev Summit 2018. Machine learning in the browser has never been better. Here is the presentation:ML in JS with TensorFlow.js Video Transcript
Oh wow, quite a few. I'm very glad. So, for those of you that haven't seen it you can check it out after our talk at https://playground.tensorflow.org - it is an in-browser visualization of a small
neural network and it shows in real time all the internals of the network as it's training and this was a lot of fun to make and had a huge educational success.
We've been getting emails from high schools and universities that have been using this to teach students about machine learning. After we launched playground we were wondering why was it so successful and we think one big reason was that it was in the browser and the browser is this unique platform where with the things you build you can share with anyone with just a link and those people that open your app, don't have to install any drivers or any software it just works.
Another thing is it's the browser is highly interactive and so the user is going to be engaged with whatever you're building. Another big thing is that browsers, we didn't take advantage of this in the playground, but browsers have access to sensors like the microphone and the camera and the accelerometer and all of these sensors are behind standardized APIs that work on all
browsers, and the last thing, the most important thing, is the data that comes from these sensors doesn't ever have to leave the client, you don't have to upload anything to the server, which preserves privacy.
the plunges and took existing models in Python and ported it to the browser and build interactive fun things with it.
So one example is the style transfer. Another person ported the character RNN and then built a novel interface that allows you to explore all the different possible endings of a sentence, all generated by the model in real time.
You can imagine other types of applications of this. Like make a browser extension that lets you control the page for accessibility purposes so again all this code is online please go for kit and play and make something else with it okay Daniel I know this is fine I know .all right