Liam Hurst

Liam Hurst

1591089636

Learning and Practicing Natural Language Processing with TensorFlow

Not all the data is present in a standardized form. Data is created when we talk, when we tweet, when we send messages on Whatsapp and in other activities. The majority of this data is in the textual form, which is of a highly unstructured nature.

While having data of high dimensions, the information stored therein is not directly available unless it is manually interpreted (understand by reading) or analyzed by an automated device. It is important to familiarise ourselves with the techniques and principles of Natural Language Processing ( NLP) in order to gain meaningful insights from text data.

So in this article, we will see how we can gain insights into text data and hands-on on how to use those insights to train NLP models and perform some human mimicking tasks. Let’s dive in and look at some of the basics of NLP.

Tokenization:

Representing the words in a way that a computer can process them, with a view to later training a Neural network that can understand their meaning. This process is called tokenization.

Let’s look at how we can tokenize the sentences using TensorFlow tools.

#python #deep-learning #nlp #tensorflow #machine-learning

What is GEEK

Buddha Community

Learning and Practicing Natural Language Processing with TensorFlow
Ray  Patel

Ray Patel

1623250620

Introduction to Natural Language Processing

We’re officially a part of a digitally dominated world where our lives revolve around technology and its innovations. Each second the world produces an incomprehensible amount of data, a majority of which is unstructured. And ever since Big Data and Data Science have started gaining traction both in the IT and business domains, it has become crucial to making sense of this vast trove of raw, unstructured data to foster data-driven decisions and innovations. But how exactly are we able to give coherence to the unstructured data?

The answer is simple – through Natural Language Processing (NLP).

Natural Language Processing (NLP)

In simple terms, NLP refers to the ability of computers to understand human speech or text as it is spoken or written. In a more comprehensive way, natural language processing can be defined as a branch of Artificial Intelligence that enables computers to grasp, understand, interpret, and also manipulate the ways in which computers interact with humans and human languages. It draws inspiration both from computational linguistics and computer science to bridge the gap that exists between human language and a computer’s understanding.

Deep Learning: Dive into the World of Machine Learning!

The concept of natural language processing isn’t new – nearly seventy years ago, computer programmers made use of ‘punch cards’ to communicate with the computers. Now, however, we have smart personal assistants like Siri and Alexa with whom we can easily communicate in human terms. For instance, if you ask Siri, “Hey, Siri, play me the song Careless Whisper”, Siri will be quick to respond to you with an “Okay” or “Sure” and play the song for you! How cool is that?

Nope, it is not magic! It is solely possible because of NLP powered by AI, ML, and Deep Learning technologies. Let’s break it down for you – as you speak into your device, it becomes activated. Once activated, it executes a specific action to process your speech and understand it. Then, very cleverly, it responds to you with a well-articulated reply in a human-like voice. And the most impressive thing is that all of this is done in less than five seconds!

#artificial intelligence #big data #data sciences #machine learning #natural language processing #introduction to natural language processing

James Briggs

James Briggs

1616415747

Evolution of Natural Language Processing

https://towardsdatascience.com/evolution-of-natural-language-processing-8e4532211cfe

Friend link for free access

Attention is all you need. That is the name of the 2017 paper that introduced attention as an independent learning model — the herald of our now transformer dominant world in natural language processing (NLP).

Transformers are the new cutting-edge in NLP, and they may seem somewhat abstract — but when we look at the past decade of developments in NLP they begin to make sense.

We will cover these developments, and look at how they have led to the Transformers being used today. This article makes no assumptions in you already understanding these concepts — we will build an intuitive understanding without getting overly technical.

We will cover:

  • Natural Language Neural Nets
    • Recurrence
    • Vanishing Gradients
    • Long-Short Term Memory
    • Attention
  • Attention is All You Need
    • Self-Attention
    • Multi-Head Attention
    • Positional Encoding
    • Transformers

#python #deep-learning #machine-learning #data-science #natural-language-processing #tensorflow

Paula  Hall

Paula Hall

1623392820

Structured natural language processing with Pandas and spaCy

Accelerate analysis by bringing structure to unstructured data

Working with natural language data can often be challenging due to its lack of structure. Most data scientists, analysts and product managers are familiar with structured tables, consisting of rows and columns, but less familiar with unstructured documents, consisting of sentences and words. For this reason, knowing how to approach a natural language dataset can be quite challenging. In this post I want to demonstrate how you can use the awesome Python packages, spaCy and Pandas, to structure natural language and extract interesting insights quickly.

Introduction to Spacy

spaCy is a very popular Python package for advanced NLP — I have a beginner friendly introduction to NLP with SpaCy here. spaCy is the perfect toolkit for applied data scientists when working on NLP projects. The api is very intuitive, the package is blazing fast and it is very well documented. It’s probably fair to say that it is the best general purpose package for NLP available. Before diving into structuring NLP data, it is useful to get familiar with the basics of the spaCy library and api.

After installing the package, you can load a model (in this case I am loading the simple Engilsh model, which is optimized for efficiency rather than accuracy) — i.e. the underlying neural network has fewer parameters.

import spacy
nlp = spacy.load("en_core_web_sm")

We instantiate this model as nlp by convention. Throughout this post I’ll work with this dataset of famous motivational quotes. Let’s apply the nlp model to a single quote from the data and store it in a variable.

#analytics #nlp #machine-learning #data-science #structured natural language processing with pandas and spacy #natural language processing

Liam Hurst

Liam Hurst

1591089636

Learning and Practicing Natural Language Processing with TensorFlow

Not all the data is present in a standardized form. Data is created when we talk, when we tweet, when we send messages on Whatsapp and in other activities. The majority of this data is in the textual form, which is of a highly unstructured nature.

While having data of high dimensions, the information stored therein is not directly available unless it is manually interpreted (understand by reading) or analyzed by an automated device. It is important to familiarise ourselves with the techniques and principles of Natural Language Processing ( NLP) in order to gain meaningful insights from text data.

So in this article, we will see how we can gain insights into text data and hands-on on how to use those insights to train NLP models and perform some human mimicking tasks. Let’s dive in and look at some of the basics of NLP.

Tokenization:

Representing the words in a way that a computer can process them, with a view to later training a Neural network that can understand their meaning. This process is called tokenization.

Let’s look at how we can tokenize the sentences using TensorFlow tools.

#python #deep-learning #nlp #tensorflow #machine-learning

Houston  Sipes

Houston Sipes

1600430400

10 Free Online Resources To Learn Swift Language

Swift is a fast and efficient general-purpose programming language that provides real-time feedback and can be seamlessly incorporated into existing Objective-C code. This is why developers are able to write safer, more reliable code while saving time. It aims to be the best language that can be used for various purposes ranging from systems programming to mobile as well as desktop apps and scaling up to cloud services.

Below here, we list down the 10 best online resources to learn Swift language.

(The list is in no particular order)

#developers corner #free online resources to learn swift language #learn swift #learn swift free #learn swift online free #resources to learn swift #swift language #swift programming