Thursday, June 17, 2021

What is Natural Language Processing and its roadmap to learn NLP?


What is NLP?

Natural Language Processing is another aspect of Artificial Intelligence, with the interactions between computers and human language. NLP is a process to understand human language as a form of spoken or written for a computer program.

NLP has a number of applications in the real world like Search Engines, Business Intelligence, Medical Research.

How does Natural Language Processing work?

NLP helps the computer to understand the natural language like humans, whatever the language is spoken or written. IN NLP it takes raw real-world input, processes it, and makes it understandable for computers. It just like the human brain processes input, the same computers have a program to process their respective input.

It has two major parts in Natural Language Processing: Data Preprocessing and Develops Algorithm.

Data cleaning takes a major role in NLP. There are some ways to clean your data:

Tokenization: The sentence breaks down into smaller units

Remove Pantuations: Remove the punctuations, which are not important in model building.

Remove Stop Words: Remove the common words from the text so unique and important words remain in the text.

Stemming and Lemmatization: It is a process of reducing inflected words to their word stem.

Uses of Natural Language Processing

Some uses of Natural Language Processing

Text Extraction: It automatically summarized the text and finds the important pieces of data. A very well-known example is Key Word Extractions. I this process the algorithm finds one or more important keywords from a text or paragraphs.

Natural Language Generation: In this process, the algorithm analyzes the unstructured data and automatically generates content based on that data. 

Text Classification: In this, the algorithm categorized the texts and tags. It is useful in Sentiment Analysis, which helps natural language processing algorithm to determine the sentiment of emotion behind a text.

Roadmap to learn NLP in 2021

Source: Krish Naik (YouTube)

Projects can do using NLP

This list is suitable for beginners, intermediates, and experts also. If you are looking for NLP projects for the final year, this list should help you out.

You can do these projects to showcase your NLP knowledge

  • Stock Price Prediction using News Headlines
  • Spam Ham Classicifation
  • Fake News Classifier
  • Predictive Text Genaretor
  • Question Generator
  • Customer Support Bot
  • Language Identifier

Real-life examples in NLP

Natural Language Processing and AI technology for businesses are increasing gradually. Regardless of whether it's a physical store with stock or an enormous SaaS brand with many representatives, clients and organizations need to convey previously, during, and after a deal. There is a wide range of uses for NLP. The following are only three distinct ways that organizations can utilize innovation in their business.

Below are a few real-world examples of the NLP uses.

  • Form Spell Check
  • Search Autocomplete
  • Search Autocorrect
  • Machine Translation
  • Messenger Bot
  • Virtual Assistants
  • Knowledge Base Support
  • Customer Service Automation
  • Google Assistance/ Alexa / Siri
  • Social Media Monitoring

Natural language processing has a crucial impact on innovation and the manner in which people associate with it. It is utilized in some certifiable applications in both the business and shopper circles, including chatbots, network protection, web indexes, and enormous information examination. In spite of the fact that not without its difficulties, NLP is required to keep on being a significant piece of both industry and regular daily existence.

Sunday, July 26, 2020

Thursday, April 16, 2020

How Does Cloud Computing Work

What is Cloud Computing?
In simple words, cloud computing is a computer resource such as hardware and software services provided through the network. Python and Java are the best languages to use for cloud computing.

Most of the current cloud is actually a very large data center, where thousands of servers are arranged, spending millions of dollars to keep them cool. But with thousands of these servers, many complex problems of clients are solved very easily.

How many types of cloud computing are there?
There are three main types of cloud computing services.
·       Software-as-a-Service (SaaS): Software-as-a-Service is a model for software distribution and the customers access the software over the internet via a standard web browser. A simple example of SaaS is Gmail and some well-known examples of SaaS are – Netflix, Google Apps, and Cisco WebEx etc.
·       Infrastructure-as-a-Service (IaaS): Infrastructure-as-a-Service is the basic layer in the cloud computing model. It is a public cloud environment. It is a service model that delivers computer infrastructure based on an outsource. Popular examples of IaaS are – Microsoft Azure, Amazon Web Services (AWS), etc.
·       Platform-as-a-Service (PaaS): Platform-as-a-Service is a computing platform including operating system, programming language execution environment, web server, database, etc. Some examples of PaaS are – Windows Azure, Heroku, and Apache Stratos, etc.
Three main cloud deployment models are there-

·       Public Cloud: Systems and services are easily accessible to the public in public clouds. Some examples of public cloud are – Amazon, IBM, Microsoft, Google, etc. Public cloud has low cost than the private or hybrid cloud. It provides a large number of resources to the customer so it is reliable. Public cloud is easily combined with the private cloud because it has flexibility. It is accessible through the internet. But as the resources are shared publicly so it doesn’t give you high-level security.
·       Private Cloud: Private cloud gives you the ability to access the system and services within the organization. Third-party will also be able to manage it internally. It gives you high security. It’s very difficult to propagate private clouds globally and also it is very costly.
·       Hybrid Cloud: Hybrid cloud is the combination of public cloud and private cloud. It has both of the features of public and private cloud. It gives you security and it is cost-effective. As the hybrid cloud is the combination of public and private cloud its networking becomes complex.
How cloud computing works?

Cloud computing is a method where software, resources, and information are shared with the help of network. Physical servers which are maintained and controlled by the providers of cloud computing, store the information. With the help of an internet connection, users can access the stored information.
The presence of these three main components is required for the proper working of cloud computing which is mentioned below. Cloud computing architecture is formed with these three components.
·       Front-end: Front-end refers to the side which the computer user or client sees. Front-end includes the client’s or user’s computer or mobile device and the application required to access the cloud computing system. The web browser is the most required application, but other systems may require any other special applications.
·       Backend: This is the computer infrastructure used by service providers. It includes various servers, computers, operating systems, virtual machines and data storage facilities which are combined and form the cloud technology. Backend is also known as Backend-as-a-Service (BaaS). Monitoring the traffic and client demands and run everything efficiently are done by the central server.
·       Network: This is the most valuable component because nothing can be done without networking. It allows the connection between front-end and backend via the internet.

Why you use cloud computing?
Surely a question arises in your mind that why will you use cloud computing? I’ll answer you. Cloud computing is cost-effective. You don’t have to make huge investments to access the cloud. It is a constantly improving process. It improves its features every day to be faster. Cloud computing gives you the feature of backup and recovery. If any disaster may happen you will be able to recover the data saved in cloud storage. Cloud computing gives you security. The data saved in cloud storage is not stolen or publish but one drawback is that it can be viewed. As cloud computing has so many excellent features you can easily use clouds.
Some benefits of cloud computing are given below-
1.    Flexibility: Cloud computing gives a flexible approach to users or consumers.
2.    Cost-effectiveness: Cloud computing is cost-effective because it doesn’t need any huge investment.
3.    Scalability: Cloud has a feature called scalability. The number of users can be increased or decreased as much as required with the change of time.
4.    Fast implementation: Cloud computing is a constantly improving process. It is a very faster and less complex process.
5.    Access anywhere: Cloud applications are easily accessible and it gives you the security to access the cloud applications from anywhere or from any other device.
6.    Maintenance-free: Patching, upgrading and testing none of these are required for cloud applications because all of these are handled on the cloud.
7.    Better security: Cloud computing gives you better security. It gives you a back-up and recovery feature. In case of any data, loss users can easily recover the data from cloud storage.

Some disadvantages of cloud computing are-
·      As cloud computing is an internet-based service so services outrage may occur for any reason.
·      Sometimes, unfortunately, the cloud provides less security and privacy.
·      As the cloud is an online process it suffers from online server attacks.
·      Vendor lock-in is another disadvantage of cloud computing.
Cloud computing is accepted globally. The great features of cloud computing make it very popular all over the World and the use of this technology increasing rapidly. In the UK, cloud-based service companies have grown rapidly from 48% in 2010 to 88% in 2017 and most of the users expect to increase their adoption of cloud services. So, it is expected that cloud computing will widely expand in India very quickly. 

Contributed by - Swagata Chakraborty (Regent Education And Research Foundation)

Saturday, April 11, 2020

How Google is Teaching a Robot Dog to Learn to Move like a Real Dog

In the field of robotics, stable progress has been one of the chief difficulties considering the way that the standard robots which have strong speed, every now and again need high authority and manual undertakings to structure. These standard hand-structured controlled robots are only reasonable for a little extent of conditions, and henceforth, getting hard relative for this current reality. To decide this issue, Google has taken the help of deep reinforcement learning as it can make sense of how to control techniques normally without the data on the earth or the robot. Additionally, with this, one doesn't have to set up the robot again for a substitute circumstance.

Saturday, April 4, 2020

The Big Data Revolution

Big data describes a large volume of data both structured and unstructured which helps a business to move on a day-to-day basis. Gathering the data is not important, but what the organizations do with the data that matters most. Big data is analyzed for problem-solving that leads to better decisions and strategical business moves.
Big data surrounds around the large sets of information that grow at increasing rates. It covers the volume of information, the velocity or speed at which it is created and collected at a steady or viable rate, and the variety or scope of the data points being covered. Big data often comes from various sources and arrives in multiple formats. The definition of big data is divided into three V's:

Friday, April 3, 2020

10 Open Source Data Science Projects to Make you Industry Ready!

10 Open Source Data Science Projects to Make you Industry Ready!
10 Open Source Data Science Projects to Make your Industry Ready!

Various newcomers to data science put a great deal of vitality on a fundamental level and deficient on helpful application. To increase authentic ground end route toward transforming into a data scientist, it's fundamental to start building data science reaches out at the most punctual chance.
This is an opportunity to genuinely dig in and tackle data science adventures. A huge amount of individuals all of a sudden have time on their hands which they didn't see coming. Why not utilize that and work on preparing yourself for your dream data science work?
At the present time, share data science adventure models from both Springboard understudies and outside data scientists that will empower you to appreciate what a completed the process of undertaking should take after. We'll similarly give a couple of clues to making your own captivating data science adventures.

10 Open-Source Data Science Projects to Enhance your Skills

CoronavirusTime Series Data

What other spots would we have the option to maybe begin? The coronavirus is ordering the world and paying little mind to which site I go to, COVID-19 is writ gigantic in the highlights.

Luckily, a lot of research labs and affiliations comprehensive have been gathering data around this and have openly discharged it for us. So why not use our data science data and aptitudes to tackle a social government help issue?

10 Open Source Data Science Projects to Make you Industry Ready!

The GitHub file I've associated here consolidates time course of action data following the amount of people affected by the coronavirus all around, including:

asserted cases of the coronavirus
the amount of people who have given due to the coronavirus, and
the amount of people who have recovered from the dangerous illness

The makers of this endeavor update the dataset step by step ina. CSV structure so you can download it and start analyzing today!

You can also check out this GitHub repository containing datasets for the coronavirus cases exclusively in the United States (broken down by state and county).

NLPPaper Summaries

The Natural Language Processing (NLP) field has come far over the latest 3 years. Starting from the Transformer designing in 2017, we have seen countless jumps forward and vital NLP libraries starting now and into the foreseeable future, including Google's BERT, OpenAI's GPT-2, among others.

10 Open Source Data Science Projects to Make you Industry Ready!

This GitHub storage facility is a collection of key NLP papers sketched out for a progressively broad course of action of data science specialists. Here is a key overview of focuses campaigned at the present time:

  • Dialogue and Interactive Systems
  • Ethics and NLP
  • Text Generation
  • Information Extraction
  • Information Retrieval and Text Mining
  • Interpretability and Analysis of Models for NLP
  • Language Grounding to Vision, Robotics and Beyond
  • Language Modeling
  • Machine Learning for NLP
  • Machine Translation
  • Multi-Task Learning
  • NLP Applications
  • Question Answering
  • Resources and Evaluation
  • Semantics
  • Sentiment Analysis, Stylistic Analysis, and Argument Mining
  • Speech and Multimodality
  • Text Summarization

Syntax: Tagging, Chunking, and Parsing
There are plenty more NLP topics inside. This is as good a project as any to pass the time during the lockdown! Pick an NLP paper and start parsing through it. That is a LOT of knowledge available under one umbrella.

GoogleBrain AutoML

Modernized Machine Learning, or AutoML, considers automating certain assignments of the normal AI pipeline. What started as a side assignment several years preceding extra time is by and by an unmitigated domain of research. There are gigantic measures of AutoML gadgets in the market that can modernize the entire ML pipeline for affiliations.

AutoML is especially getting a balance for associations that don't have a given data science gathering or can't remain to contract one without any planning. Essentially every tech goliath has an AutoML plan in the market, from Google's Cloud AutoML to Baidu's EZDL.

10 Open Source Data Science Projects to Make you Industry Ready!

This data science adventure by the Google Brain bunch contains a once-over of AutoML related models and libraries. The GitHub storage facility has amassed in excess of 1,600 stars since it was freely discharged 6 days earlier. Bewildering!


Here's another awesome open-source adventure by the Google Research gathering. This identifies with the Natural Language Processing (NLP) territory and the Transformer designing I referenced previously.

Here’s how the Google Research team defines ELECTRA:

“ELECTRA is a new method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish “real” input tokens vs “fake” input tokens generated by another neural network.”

What captivated me about ELECTRA is the precision we can achieve even on a single GPU. ELECTRA goes to a substitute level totally for tremendous extension datasets and achieves forefront execution on the SQuAD 2.0 benchmark.

You can read about ELECTRA in-depth in Google’s research paper.

You need to have the below requirements installed on your machine before you begin:

  • Python 3
  • TensorFlow 1.15
  • NumPy
  • scikit-learn and SciPy

GAN Compression

GANs, or Generative Adversarial Networks, overpowered the data science world when Ian Goodfellow introduced them in 2014. These GANs have since changed into significant (and routinely captivating) applications, for instance, delivering workmanship and making films.

But a significant issue with training a GAN model is the sheer computational power required. This is where GAN Compression comes in.

GAN Compression is "a comprehensively helpful procedure for compacting unforeseen GANs". It reduces the count of notable GAN-based models, for instance, pix2pix, CycleGAN, etc. Just gander at this superb model:

10 Open Source Data Science Projects to Make you Industry Ready!

Amazon vs. eBay

Ever pulled the trigger on a purchase only to discover shortly afterward that the item was significantly cheaper at another outlet?

On a Chrome expansion he was building, Chase Roberts decided to take a gander at the expenses of 3,500 things on eBay and Amazon. With his tendencies perceived, Chase walks perusers of this blog passage through his endeavor, starting with how he gathered the data and recording the troubles he looked during this methodology.

10 Open Source Data Science Projects to Make you Industry Ready!

The results demonstrated potential for liberal save reserves: "Our shopping container has 3,520 exceptional things and if you picked an improper stage to buy all of these things (by consistently shopping at whichever site has a continuously expensive worth), this truck would cost you $193,498.45. Or of course, you could deal with your home advance. This is the most critical result comprehensible for our shopping container. The best circumstance for our shopping bushel, expecting you found minimal expense among eBay and Amazon on everything, is $149,650.94. This is a $44,000 differentiate, or 23%!"

Audio Snowflake

Right when you consider data science adventures, chances are you think about how to deal with a particular issue, as found in the models above. Regardless, shouldn't something be said about making an endeavor for the sheer heavenliness of the data? That is really what WendyDherin did.

10 Open Source Data Science Projects to Make you Industry Ready!

The explanation behind her Hackbright Academy adventure was to make a stunning visual depiction of music as it played, getting different portions, for instance, beat, length, key, and air. The web application Wendy made usages an introduced Spotify web player, an API to scratch point by point tune data, and trigonometry to move a motion of splendid shapes around the screen. Sound Snowflake maps both quantitative and emotional characteristics of songs to visual traits, for instance, concealing, inundation, upheaval speed, and the conditions of figures it makes.

She explains a bit about how it works:

Each line forms a geometric shape called a hypotrochoid (pronounced hai-po-tro-koid).

Hypotrochoids are numerical roulettes followed by a P that is associated with a circle that moves around within a greater circle. In case you have played with Spirograph, you may be OK with the thought.

The condition of any hypotrochoid is directed by the range an of the tremendous circle, the range b of the little circle, and the detachment h between the point of convergence of the tinier circle and point P.

For Audio Snowflake, these values are determined as follows:

  • song duration
  • section duration
  • song duration minus section duration

StyleGAN2 – A New State-of-the-Art GAN!

I'm eager to draw out another top tier GAN building at the present time. StyleGAN was a hit in the PC vision system and StyleGAN2 takes things towards a much progressively handy level.

“StyleGAN2 is a state-of-the-art network in generating realistic images. Besides, it was explicitly trained to have disentangled directions in latent space, which allows efficient image manipulation by varying latent factors.”

10 Open Source Data Science Projects to Make you Industry Ready!

That is the force of StyleGAN2. Fairly stunning anyway unimaginably notable. You can get some answers concerning StyleGAN2 in the official research paper here.

Ultra-Light and Fast Face Detector

This is an uncommon open-source release. Do whatever it takes not to be put off by the Chinese page (you can without quite a bit of a stretch makes a translation of it into English). This is an ultra-light type of a face area model – a very accommodating utilization of PC vision.

10 Open Source Data Science Projects to Make you Industry Ready!

The size of this face discovery model is simply 1MB! I genuinely needed to peruse that a couple of times to trust it.

This model is a lightweight face discovery model for edge figuring gadgets dependent on the libfacedetection design. There are two forms of the model:

Version-slim (slightly faster simplification)
Version-RFB (with the modified RFB module, higher precision)
This is an extraordinary store to get your hands on. We don't ordinarily get such a splendid chance to assemble PC vision models on our nearby machine – how about we not miss this one.

Largest Chinese Knowledge Map in History

I have gone over a huge amount of articles on graphs starting late. How they work, what are the different pieces of an outline, how data streams in a chart, how does the thought apply to data science, etc – these are questions I'm sure you're asking right now.

There are sure branches of chart hypothesis that we can apply in information science, for example, information trees and information maps.

10 Open Source Data Science Projects to Make you Industry Ready!

This endeavor is a behemoth in that sense. It is the greatest Chinese data map ever, with in excess of 140 million core interests! The dataset is sifted through as (component, trademark, regard), (component, relationship, component). The data is in .csv position. It's a superb open-source undertaking to show your graph capacities – don't stop for one moment to take a dive.

This is the ideal time to get a data science adventure and start working on it. We haven't the foggiest when this crisis will end yet we can utilize this chance to place assets into our learning and our future.

Which adventure would you say you are needing to start straight away? Are there other open-source data science adventures you have to bestow to the system? Let me know in the comments fragment underneath and I'll give a valiant exertion to get the word out!

Wednesday, December 4, 2019

Find Your Friend's Birthday | Fun Project | JavaScript

Find Your Friend's Birthday | Fun Project

This is a fun project build with HTML and JavaScript.
Here, you store your friend’s birthdays, and search the friend’s name and get the birthday.