What is Machine Learning?
Simply put, machine learning can be defined as the use and development of algorithms for computer systems that can learn and adapt without supervision. First, the data is captured through various programs. Then, statistical models are generated for analysis and inferences are drawn from data patterns.
Key Concepts and Terms of Machine Learning
- Artificial Intelligence - It simulates human intelligence in machines, mimicking human actions.
- Deep Learning - It teaches computers to mimic humans by learning data behavior from previous examples (data patterns).
- Supervised Learning - Where an ML program is trained on a pre-defined dataset, this program can make accurate decisions with new data based on previous data patterns.
- Unsupervised Learning - Here is a program, when provided with a dataset, can automatically find patterns and relationships in it.
- Classification - It is used when predictions are either discrete or of Boolean nature. This means the prediction is either a “yes or no” nature.
- Regression - These systems could be used, for example, to answer questions of “how much?” or “how many?” nature and analyze data on a continuous spectrum.
- Decision Trees - This tool uses a tree-like graph or model of decisions and their possible consequences. It is also a method of visual algorithm representation.
- Machine Learning Models - After training the system to detect patterns in the data, a model is created to make automatic predictions. These are called machine learning models.
- Neural Network – It is a network of interconnected nodes that make up a model. It is used to estimate functions that depend on a large number of inputs.
- Datasets - It is a group of raw historical data which is used to train the system that detects patterns. It is composed of instances and characteristics.
- Data Mining - It is an exploratory function which supports machine learning models for prediction.
- Feature Engineering - Before creating the prediction model (here, analysis, cleaning, and structuring of the data fields are done), you can eliminate the unnecessary fields that are not useful in making the prediction.
Why you Should use TensorFlow.js?
If you are wondering why you should use TensorFlow Js, then there are many advantages of building ML apps using tfjs file:
- You can build models easily – You can get multiple levels of abstraction. You can test and choose the right model for your experiment with the help of Keras API.
- Strong ML production anywhere – TensorFlow.js is so light and easy to use that it allows the user to run it on any device or platform.
- Powerful testing for research – You get to build and test a model with high execution speed. You can use any API for generating any kind of topology. You can build prototypes easily with faster debugging. Many powerful add-on libraries can be used with TensorFlow Js.
Features of TensorFlow.js
- Open-source library – This allows TensorFlow Js models to be fast with easy ML calculations. It also supports the model with frontend APIs.
- Runs easily – Since it has an easy execution process, it can be used across various devices and platforms.
- Easy debugging – You can check all the nodes on the dashboard and fix the errors in your model live. Moreover, you will see the changes in the code show instantaneously.
- Scalable – You can use the same models for different datasets. You can synchronize and desynchronize these datasets to get out all sorts of conclusions from the processed data.
- Experiment easily – By building neural networks, you can transform the raw data into an estimator. Notably, estimators find out the approximate calculation of the data.
- Abstraction for simplifying tasks – TensorFlow Js allows you to reduce the code and cut down on the development time.
- Flexibility of handling the system – TensorFlow Js resolves complex topologies by receiving support from Keras API and data input pipelines. This way, you can create different real-time models.
- Transforms ML code from other programming languages – TensorFlow Js transforms ML models, which are built in Python and Java.
Why is TensorFlow.js Suitable for Web and ML Apps?
Cases of Using TensorFlow.js for ML
There are many ML projects which have used TensorFlow Js for their completion. One of the most famous ones is face recognition software. This piece of software allows you to recognize faces and emotions. It also lets you find out the gender. You can also find and verify landmarks with it.
Other TensorFlow Js projects include image editing apps where the style of images could be transferred. This software can also help to manipulate images by selectively blurring unsafe pictures while displaying safe ones.
You can also generate graphs from raw data with the help of TensorFlow Js. The list of possibilities for using TensorFlow Js is endless.
Steps of Deploying ML Apps Using TensorFlow.js
There are two ways in which ML apps can be deployed using TensorFlow Js:
1. By using the browser – In the browser, you can launch the ML app through Heroku, Github, or Firebase. A paid option would be Digital Ocean. These hosting service providers have the necessary support to back up ML apps. There are many tutorials over the web that will explain to you how to deploy the app. Small ML apps can be deployed for free.
2. By using Node.js – After installing Node.js on your local server, you can deploy the ML app. Your app will get hosted on the 8080 port. The URL will look something like this - http://localhost:8080/xxxx. The method of deployment can be seen through a tutorial video.
There are many TensorFlow.js tutorials on the web. Pick the best TensorFlow Js tutorial or join a course to learn the ML deployment procedure in detail.
How to Deploy ML Apps in the Browser?
- Select your model and train it
- Use a cloud GPU cluster
- Export the resultant model and save it
- Get this model into Tensorflow Js by using the Tensorflow Js converter
- To use this converter, install it in Python (pip install Tensorflow Js)
- Then, run TensorFlow Js converter in the command prompt
- Load your ML app and run it. Your app will be deployed
How to Deploy Ml Model using Node.js?
- Node.js is another deployment environment for TensorFlow Js
- Install TensorFlow Js through npm install
- For CPU version, use npm install tfjs-node
- For GPU, use npm install tfjs-node-gpu
- Load your ML app file and run it to deploy your app
So far we’ve take a look at building apps for machine learning with Java and how TensorFlow.js can be an integral part of it.
To learn how you can make use of TensorFlow.js and other tools to build ML apps and models, check out Hero Vired’s Integrated Program in Data Science, Machine Learning, and Artificial Intelligence to get a jumpstart in your career and realize your dream of becoming a machine learning developer.