Machine Learning in the Browser using TensorFlow.jsπŸ”₯πŸ”₯πŸ”₯

Machine Learning in the Browser using TensorFlow.jsπŸ”₯πŸ”₯πŸ”₯

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.

Trending AI Articles:

1. Bursting the Jargon bubbles β€” Deep Learning> 2. How Can We Improve the Quality of Our Data?> 3. How to train a neural network to code by itself ?> 4. Machine Learning using Logistic Regression in Python with Code
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

Top Machine Learning Framework: 5 Machine Learning Frameworks of 2019

Top Machine Learning Framework: 5 Machine Learning Frameworks of 2019

Machine Learning (ML) is one of the fastest-growing technologies today. ML has a lot of frameworks to build a successful app, and so as a developer, you might be getting confused about using the right framework. Herein we have curated top 5...

Machine Learning (ML) is one of the fastest-growing technologies today. ML has a lot of frameworks to build a successful app, and so as a developer, you might be getting confused about using the right framework. Herein we have curated top 5 machine learning frameworks that are cutting edge technology in your hands.

Through the machine learning frameworks, mobile phones and tablets are getting powerful enough to run the software that can learn and react in real-time. It is a complex discipline. But the implementation of ML models is far less daunting and difficult than it used to be. Now, it automatically improves the performance with the pace of time, interactions, and experiences, and the most important acquisition of useful data pertaining to the tasks allocated.

As we know that ML is considered as a subset of Artificial Intelligence (AI). The scientific study of statistical models and algorithms help a computing system to accomplish designated tasks efficiently. Now, as a mobile app developer, when you are planning to choose machine learning frameworks you must keep the following things in mind.

The framework should be performance-oriented
The grasping and coding should be quick
It allows to distribute the computational process, the framework must have parallelization
It should consist of a facility to create models and provide a developer-friendly tool
Let’s learn about the top five machine learning frameworks to make the right choice for your next ML application development project. Before we dive deeper into these mentioned frameworks, know the different types of ML frameworks that are available on the web. Here are some ML frameworks:

Mathematical oriented
Neural networks-based
Linear algebra tools
Statistical tools
Now, let’s have an insight into ML frameworks that will help you in selecting the right framework for your ML application.

Don’t Miss Out on These 5 Machine Learning Frameworks of 2019
#1 TensorFlow
TensorFlow is an open-source software library for data-based programming across multiple tasks. The framework is based on computational graphs which is essentially a network of codes. Each node represents a mathematical operation that runs some function as simple or as complex as multivariate analysis. This framework is said to be best among all the ML libraries as it supports regressions, classifications, and neural networks like complicated tasks and algorithms.

machine learning frameworks
This machine learning library demands additional efforts while learning TensorFlow Python framework. Your job becomes easy in the n-dimensional array of the framework when you have grasped the Python frameworks and libraries.

The benefits of this framework are flexibility. TensorFlow allows non-automatic migration to newer versions. It runs on the GPU, CPU, servers, desktops, and mobile devices. It provides auto differentiation and performance. There are a few goliaths like Airbus, Twitter, IBM, who have innovatively used the TensorFlow frameworks.

#2 FireBase ML Kit
Firebase machine learning framework is a library that allows effortless, minimal code, with highly accurate, pre-trained deep models. We at Space-O Technologies use this machine learning technology for image classification and object detection. The Firebase framework offers models both locally and on the Google Cloud.

machine learning frameworks
This is one of our ML tutorials to make you understand the Firebase frameworks. First of all, we collected photos of empty glass, half watered glass, full watered glass, and targeted into the machine learning algorithms. This helped the machine to search and analyze according to the nature, behavior, and patterns of the object placed in front of it.

The first photo that we targeted through machine learning algorithms was to recognize an empty glass. Thus, the app did its analysis and search for the correct answer, we provided it with certain empty glass images prior to the experiment.
The other photo that we targeted was a half water glass. The core of the machine learning app is to assemble data and to manage it as per its analysis. It was able to recognize the image accurately because of the little bits and pieces of the glass given to it beforehand.
The last one is a full glass recognition image.
Note: For correct recognition, there has to be 1 label that carries at least 100 images of a particular object.

#3 CAFFE (Convolutional Architecture for Fast Feature Embedding)
CAFFE framework is the fastest way to apply deep neural networks. It is the best machine learning framework known for its model-Zoo a pre-trained ML model that is capable of performing a great variety of tasks. Image classification, machine vision, recommender system are some of the tasks performed easily through this ML library.

machine learning frameworks
This framework is majorly written in CPP. It can run on multiple hardware and can switch between CPU and GPU with the use of a single flag. It has systematically organized the structure of Mat lab and python interface.

Now, if you have to make a machine learning app development, then it is mainly used in academic research projects and to design startups prototypes. It is the aptest machine learning technology for research experiments and industry deployment. At a time this framework can manage 60 million pictures every day with a solitary Nvidia K40 GPU.

#4 Apache Spark
The Apache Spark machine learning is a cluster-computing framework written in different languages like Java, Scala, R, and Python. Spark’s machine learning library, MLlib is considered as foundational for the Spark’s success. Building MLlib on top of Spark makes it possible to tackle the distinct needs of a single tool instead of many disjointed ones.

machine learning frameworks
The advantages of such ML library lower learning curves, less complex development and production environments, which ultimately results in a shorter time to deliver high-performing models. The key benefit of MLlib is that it allows data scientists to solve multiple data problems in addition to their machine learning problems.

It can easily solve graph computations (via GraphX), streaming (real-time calculations), and real-time interactive query processing with Spark SQL and DataFrames. The data professionals can focus on solving the data problems instead of learning and maintaining a different tool for each scenario.

#5 Scikit-Learn
Scikit-learn is said to be one of the greatest feats of Python community. This machine learning framework efficiently handles data mining and supports multiple practical tasks. It is built on foundations like SciPy, Numpy, and matplotlib. This framework is known for supervised & unsupervised learning algorithms as well as cross-validation. The Scikit learn is largely written in Python with some core algorithms in Cython to achieve performance.

machine learning frameworks
The machine learning framework can work on multiple tasks without compromising on speed. There are some remarkable machine learning apps using this framework like Spotify, Evernote, AWeber, Inria.

With the help of machine learning to build iOS apps, Android apps powered by ML have become quite an easy process. With this emerging technology trend varieties of available data, computational processing has become cheaper and more powerful, and affordable data storage. So being an app developer or having an idea for machine learning apps should definitely dive into the niche.

Conclusion
Still have any query or confusion regarding ML frameworks, machine learning app development guide, the difference between Artificial Intelligence and machine learning, ML algorithms from scratch, how this technology is helpful for your business? Just fill our contact us form. Our sales representatives will get back to you shortly and resolve your queries. The consultation is absolutely free of cost.

Author Bio: This blog is written with the help of Jigar Mistry, who has over 13 years of experience in the web and mobile app development industry. He has guided to develop over 200 mobile apps and has special expertise in different mobile app categories like Uber like apps, Health and Fitness apps, On-Demand apps and Machine Learning apps. So, we took his help to write this complete guide on machine learning technology and machine app development areas.

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.

Thanks for reading ❀

If you liked this post, share it with all of your programming buddies!

Follow us on Facebook | Twitter

Further reading about Machine Learning and TensorFlow.js

☞ Machine Learning A-Zβ„’: 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.Β 

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 β€œpdb.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.Β 

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.