Machine Learning in the Browser using TensorFlow.js🔥🔥🔥

Machine Learning in the Browser using TensorFlow.js - TensorFlow.js appeared and allows you to do ML/DL in JavaScript, without having to use server-side applications. You can use it to define, train and run machine learning models entirely in the browser and a high-level layers API.

I have been using Python for creating and training my Machine Learning Models which requires setting up quiet a few things(I mostly use Google Colab though). Currently, I am learning Machine Learning and web development along side Android App development.

If you are also into Deep Learning then you must have done Basic Linear regression and the MNIST classification challenge which is the basic problem in Computer Vision. So when I learned about TensorFlow Lite it inspired me to make an app which can utilize the features of Android Smartphone, so I created this basic MNIST handwritten digits classification App.

Then I thought It would be great to do such things in something which is widely available, which requires Less/ no setup from user side. For this, What can be better than a browser which is available on all modern PC’s, Smartphones, Tablets, and it also allows JavaScript in which we can Back-propagate. The ML/ DL things were done by utilizing APIs which means an API was developed in a framework which sits at a server. The server response to the request in JavaScript made by the client.

Importing The module:

Using the script tag

<html>
	  <head>
	    <!-- Importing Tensorflow 
	        https://www.tensorflow.org/js/tutorials/setup
	    -->
	   
	    <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@1.0.0/dist/tf.min.js"></script>
	  </head>
	  <body>
	    <p>Hello</p>
	  </body>
	</html>

importing tensorflow.js using script

There are basically two ways to get it into your project

arn add @tensorflow/tfjs
	

npm install @tensorflow/tfjs

install tensorflow js into your system

import * as tf from '@tensorflow/tfjs';

add this line to your main.js file

ML in Browser

So How good is that?!

It allows to Run Machine Learning Models entirely in the browser. You can train them using live examples, load the pre-trained models and run Inference on them. On user’s side, There is no need to install any drivers or libraries, just open the browser and you are good to go.

GPU

TensorFlow.js Runtime

One more advantage is that TensorFlow.js can utilize WebGL to run on an underlying GPU whenever it’s available. So we can use GPU of a mobile device or a gaming PC just in the browser. In mobile phones, through browser your model can take advantage of various sensors’ data.

Privacy

One more Thing, All the data stays in the user’s device, no need to send data to servers for low-latency inference. Which means It is useful for applications where privacy matters!

TensorFlow.js has a very good list of API s.

Lets Code

Let’s start with a simple web page

<!DOCTYPE html>
	<html>
	    <head>
	        <!-- Load TFJS
	             loaded from https://github.com/tensorflow/tfjs -->        
	        <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs/dist/tf.min.js"> </script>
	      
	        <title>Hello TFJS</title>
	    </head>
	  <body>
	    <script>
	    //Code for JS below  
	    </script>  
	  </body>
	 </html>

loading TensorFlow.js using script

‘tf’ is available on the index-page because of the script tag above. we can reference tensorflow as tf.

const model = tf.sequential();

We need to define our model, which tf.sequential() returns, you may find this similar to Keras API which is standardized in TF 2.0. Keras is like a wrapper on top of TensorFlow which makes it easy to define, train, infer, and training of simple models(Sequential as well as Functional APIs).

Now let’s add some layers in the model. Here we will try to do a simple linear regression using a neural net consisting of one hidden layer.

model.add(tf.layers.dense({units: 1, inputShape: [1]}));
	

	// Prepare the model for training: Specify the loss and the optimizer.
	model.compile({
	  loss: 'meanSquaredError', 
	  optimizer: 'sgd', 
	  metrics: ['mse']
	});
	const xs = tf.tensor1d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], [10, 1]);
	//y=3*x-1
	const xs = tf.tensor1d([2, 5, 8, 11, 14, 17, 20, 23, 26, 27], [10, 1]);

Adding layers, compilng

Layers

Here, we have added a Dense layer(Fully connected) with only single neuron. Which takes only single number(like Scaler) for prediction and perform inference on it outputting a number which should be close to 3*(input)-1.

Compiling

The layers API supports all of the Keras layers (including Dense, CNN, LSTM, and so on). Then we have to say the model to use which Optimization technique and a Loss function. We can give metric as a parameter is we want.

Data

Tensor is the basic term used for multi-dimensional arrays. Here we have 10x1, 1-D Tensor(Array) as xs(input) and a 10x1, 1-D Tensor as their respective outputs. we have to learn mapping between these two using our model. (Neural nets can be seen as ‘Universal Function Estimators’!). An image can be represented as 2-D Tensor(Matrix) in gray scale, and 3-D in case of RGB channels(HeightWidthChannels). ConvNets will be explained in future.

Now, Lets Learn Mapping

await model.fit(xs, ys);
	// model.fit(xs, ys, epochs=100)
	

	model.predict(tf.tensor1d([6], [1, 1])).print();

We can train the model using model.fit(x, y), which try to learn mapping between x and y. Epoch is the parameter which says how many times to train over entire data set. It defines the total number of Forward propagate + Backward Propagate.

Predict for Future

model.predict(Tensor) do a forward pass and predicts using the given input as a Tensor of same dimension of X at the time of training. the print function prints the output on the console.

await keyword makes browser wait until the process finishes. You need to put await in an async function. just put the code inside an await function and run. It will be shown later in the article.

Full Code

<!DOCTYPE html>
	<html>
	    <head>
	            <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs/dist/tf.min.js"> </script>
	    </head>
	    <body>
	        <p id="prediction">Prediction(100):  </p>
	        <script>
	            async function run(){
	            const model=tf.sequential();
	            model.add(
	                tf.layers.dense({
	                    units:1,
	                    inputShape:[1],
	                    bias: true
	                })
	            );
	
	            model.compile({
	                loss:'meanSquaredError',
	                optimizer: 'sgd',
	                metrics: ['mse']
	            });
	
	            const xs = tf.tensor1d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]);
	            const ys = tf.tensor1d([2, 5, 8, 12, 14, 18, 21, 23, 26, 29]);
	
	            await model.fit(xs, ys, {epochs:100});
	            document.getElementById('prediction').innerText+=
	            model.predict(tf.tensor1d([100])).dataSync();
	
	            }
	            run();
	
	        </script>
	    </body>
	</html>

Full code. You may need internet connection for script in the head tags

Save this as a .html file and open it in the browser.

What can you do with TensorFlow.js?

If you’re developing with TensorFlow.js, here are three workflows you can consider.

**You can import an existing, pre-trained model for inference. **If you have an existing TensorFlow or Keras model you’ve previously trained offline, you can convert into TensorFlow.js format, and load it into the browser for inference.
**You can re-train an imported model. **As in the Pac-Man demo above, you can use transfer learning to augment an existing model trained offline using a small amount of data collected in the browser using a technique called Image Retraining. This is one way to train an accurate model quickly, using only a small amount of data.
**Author models directly in browser. **You can also use TensorFlow.js to define, train, and run models entirely in the browser using Javascript and a high-level layers API. If you’re familiar with Keras, the high-level layers API should feel familiar.

I have tried to implement something like this but with more flexibility.

#machine-learning #tensorflow

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Machine Learning in the Browser using TensorFlow.js🔥🔥🔥

sophia tondon

1620898103

5 Latest Technology Trends of Machine Learning for 2021

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Introduction

When installing Machine Learning Services in SQL Server by default few Python Packages are installed. In this article, we will have a look on how to get those installed python package information.

Python Packages

When we choose Python as Machine Learning Service during installation, the following packages are installed in SQL Server,

revoscalepy – This Microsoft Python package is used for remote compute contexts, streaming, parallel execution of rx functions for data import and transformation, modeling, visualization, and analysis.
microsoftml – This is another Microsoft Python package which adds machine learning algorithms in Python.
Anaconda 4.2 – Anaconda is an opensource Python package

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Transportation industry

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ML and AI can offer high security in the transportation industry.
It offers high reliability of their services or vehicles.
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Healthcare industry

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Outbreak Prediction

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Finance industry**

In financial industries organizations like banks, fintech, regulators and insurance are Adopting machine learning to improve their facilities.Following are the use cases of machine learning in finance.

Fraud prevention
Risk management
Investment predictions
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Loan underwriting
Algorithmic trading
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Education industry

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Future of machine learning
Continuous technological advances are bound to hit the field of machine learning, which will shape the future of machine learning as an intensively evolving language.

Improved Unsupervised Algorithms
Increased Adoption of Quantum Computing
Enhanced Personalization
Improved Cognitive Services
Rise of Robots

**Conclusion
**
Today most of the business from different industries are hire machine learning developers in India and achieve their business goals. This technology may have multiple applications, and, interestingly, it hasn’t even started yet but having taken such a massive leap, it also opens up so many possibilities in the existing business models in such a short period of time. There is no question that the increase of machine learning also brings the demand for mobile apps, so most companies and agencies employ Android developers and hire iOS developers to incorporate machine learning features into them.

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Transportation industry

Machine learning is one of the technologies that have already begun their promising marks in the transportation industry.Autonomous Vehicles,Smartphone Apps,Traffic Management Solutions,Law Enforcement,Passenger Transportation etc are the applications of AI and ML in the transportation industry.Following challenges in the transportation industry can be solved by machine learning and Artificial Intelligence.

ML and AI can offer high security in the transportation industry.
It offers high reliability of their services or vehicles.
The adoption of this technology in the transportation industry can increase the efficiency of the service.
In the transportation industry ML helps scientists and engineers come up with far more environmentally sustainable methods for powering and operating vehicles and machinery for travel and transport.

Healthcare industry

Technology-enabled smart healthcare is the latest trend in the healthcare industry. Different areas of healthcare, such as patient care, medical records, billing, alternative models of staffing, IP capitalization, smart healthcare, and administrative and supply cost reduction. Hire dedicated machine learning developers for any of the following applications.

Identifying Diseases and Diagnosis
Drug Discovery and Manufacturing
Medical Imaging Diagnosis
Personalized Medicine
Machine Learning-based Behavioral Modification
Smart Health Records
Clinical Trial and Research
Better Radiotherapy
Crowdsourced Data Collection
Outbreak Prediction

**
Finance industry**

In financial industries organizations like banks, fintech, regulators and insurance are Adopting machine learning to improve their facilities.Following are the use cases of machine learning in finance.

Fraud prevention
Risk management
Investment predictions
Customer service
Digital assistants
Marketing
Network security
Loan underwriting
Algorithmic trading
Process automation
Document interpretation
Content creation
Trade settlements
Money-laundering prevention
Custom machine learning solutions

Education industry

Education industry is one of the industries which is investing in machine learning as it offers more efficient and easierlearning.AdaptiveLearning,IncreasingEfficiency,Learning Analytics,Predictive Analytics,Personalized Learning,Evaluating Assessments etc are the applications of machine learning in the education industry.

Outsource your machine learning solution to India,India is the best outsourcing destination offering best in class high performing tasks at an affordable price.Business** hire dedicated machine learning developers in India for making your machine learning app idea into reality.
**
Future of machine learning
Continuous technological advances are bound to hit the field of machine learning, which will shape the future of machine learning as an intensively evolving language.

Improved Unsupervised Algorithms
Increased Adoption of Quantum Computing
Enhanced Personalization
Improved Cognitive Services
Rise of Robots

**Conclusion
**
Today most of the business from different industries are hire machine learning developers in India and achieve their business goals. This technology may have multiple applications, and, interestingly, it hasn’t even started yet but having taken such a massive leap, it also opens up so many possibilities in the existing business models in such a short period of time. There is no question that the increase of machine learning also brings the demand for mobile apps, so most companies and agencies employ Android developers and hire iOS developers to incorporate machine learning features into them.

#hire machine learning developers in india #hire dedicated machine learning developers in india #hire machine learning programmers in india #hire machine learning programmers #hire dedicated machine learning developers #hire machine learning developers

Machine Learning in the Browser using TensorFlow.js🔥🔥🔥

Trending AI Articles:

Importing The module:

ML in Browser

Lets Code

What can you do with TensorFlow.js?

What is GEEK

Buddha Community

5 Latest Technology Trends of Machine Learning for 2021

Python Packages in SQL Server – Get Started with SQL Server Machine Learning Services

Introduction

Python Packages

Hire Machine Learning Developers in India

Applications of machine learning in different industry domains

Hire Machine Learning Developer | Hire ML Experts in India