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

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.🌟🌟🌟🌟🌟

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.

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In 2017 deeplearn.js started which aimed for Deep Learning in the browser using JavaScript, but the main concern was Speed. As you know how GPUs can increase the speed in Deep Learning. For that WebGL came to rescue. It enabled running JavaScript Code to run on GPU. Later deeplearn.js merged into TensorFlow which became TensorFlow.js in 2018.

TensorFlow.js* is a library for developing and training ML models in JavaScript, and deploying in browser or on Node.js*

Check this Neural Network Playground made in JavaScript.

Importing The module:

  1. Using the

Introduction to Machine Learning with TensorFlow.js

Introduction to Machine Learning with TensorFlow.js

Learn how to build and train Neural Networks using the most popular Machine Learning framework for javascript, TensorFlow.js.

Learn how to build and train Neural Networks using the most popular Machine Learning framework for javascript, TensorFlow.js.

This is a practical workshop where you'll learn "hands-on" by building several different applications from scratch using TensorFlow.js.

If you have ever been interested in Machine Learning, if you want to get a taste for what this exciting field has to offer, if you want to be able to talk to other Machine Learning/AI specialists in a language they understand, then this workshop is for you.

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Further reading about Machine Learning and TensorFlow.js

☞ Machine Learning A-Z™: Hands-On Python & R In Data Science

☞ Machine Learning In Node.js With TensorFlow.js

☞ Machine Learning in JavaScript with TensorFlow.js

☞ A Complete Machine Learning Project Walk-Through in Python

☞ Top 10 Machine Learning Algorithms You Should Know to Become a Data Scientist

TensorFlow Vs PyTorch: Comparison of the Machine Learning Libraries

TensorFlow Vs PyTorch: Comparison of the Machine Learning Libraries

Libraries play an important role when developers decide to work in Machine Learning or Deep Learning researches. In this article, we list down 10 comparisons between TensorFlow and PyTorch these two Machine Learning Libraries.

According to this article, a survey based on a sample of 1,616 ML developers and data scientists, for every one developer using PyTorch, there are 3.4 developers using TensorFlow. In this article, we list down 10 comparisons between these two Machine Learning Libraries. 

1 - Origin

PyTorch has been developed by Facebook which is based on Torch while TensorFlow, an open sourced Machine Learning Library, developed by Google Brain is based on the idea of data flow graphs for building models.

2 - Features

TensorFlow has some attracting features such as TensorBoard which serves as a great option while visualising a Machine Learning model, it also has TensorFlow Serving which is a specific grpc server that is used during the deployment of models in production. On the other hand, PyTorch has several distinguished features too such as dynamic computation graphs, naive support for Python, support for CUDA which ensures less time for running the code and increase in performance.

3 - Community

TensorFlow is adopted by many researchers of various fields like academics, business organisations, etc. It has a much bigger community than PyTorch which implies that it is easier to find for resources or solutions in TensorFlow. There is a vast amount of tutorials, codes, as well as support in TensorFlow and PyTorch, being the newcomer into play as compared to TensorFlow, it lacks these benefits.

4 - Visualisation

Visualisation plays as a protagonist while presenting any project in an organisation. TensorFlow has TensorBoard for visualising Machine Learning models which helps during training the model and spot the errors quickly. It is a real-time representation of the graphs of a model which not only depicts the graphic representation but also shows the accuracy graphs in real-time. This eye-catching feature is lacked by PyTorch.

5 - Defining Computational Graphs

In TensorFlow, defining computational graph is a lengthy process as you have to build and run the computations within sessions. Also, you will have to use other parameters such as placeholders, variable scoping, etc. On the other hand, Python wins this point as it has the dynamic computation graphs which help id building the graphs dynamically. Here, the graph is built at every point of execution and you can manipulate the graph at run-time.

6 - Debugging

PyTorch being the dynamic computational process, the debugging process is a painless method. You can easily use Python debugging tools like pdb or ipdb, etc. for instance, you can put “pdb.set_trace()” at any line of code and then proceed for executions of further computations, pinpoint the cause of the errors, etc. While, for TensorFlow you have to use the TensorFlow debugger tool, tfdbg which lets you view the internal structure and states of running TensorFlow graphs during training and inference.

7 - Deployment

For now, deployment in TensorFlow is much more supportive as compared to PyTorch. It has the advantage of TensorFlow Serving which is a flexible, high-performance serving system for deploying Machine Learning models, designed for production environments. However, in PyTorch, you can use the Microframework for Python, Flask for deployment of models.

8 - Documentation

The documentation of both frameworks is broadly available as there are examples and tutorials in abundance for both the libraries. You can say, it is a tie between both the frameworks.

Click here for TensorFlow documentation and click here for PyTorch documentation.

9 - Serialisation

The serialisation in TensorFlow can be said as one of the advantages for this framework users. Here, you can save your entire graph as a protocol buffer and then later it can be loaded in other supported languages, however, PyTorch lacks this feature. 

10 - Device Management

By default, Tensorflow maps nearly all of the GPU memory of all GPUs visible to the process which is a comedown but here it automatically presumes that you want to run your code on the GPU because of the well-set defaults and thus result in fair management of the device. On the other hand, PyTorch keeps track of the currently selected GPU and all the CUDA tensors which will be allocated.


TensorFlow Extended (TFX): Machine Learning Pipelines

TensorFlow Extended (TFX): Machine Learning Pipelines

TensorFlow Extended (TFX): Machine Learning Pipelines

TensorFlow Extended (TFX): Machine Learning Pipelines

Speaker: Martin Andrews

Event: Google I/O Recap 2019 Singapore AI - From Model to Device by BigDataX

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Further reading about TensorFlow and Machine Learning

☞ Machine Learning In Node.js With TensorFlow.js

☞ Machine Learning A-Z™: Hands-On Python & R In Data Science

☞ TensorFlow is dead, long live TensorFlow!

☞ A Complete Machine Learning Project Walk-Through in Python

☞ Top 18 Machine Learning Platforms For Developers