November 9, 2020 Intermediate, NLP, Python, Technique, Text, Unstructured Data Leave a comment

How to Get Started with NLP – 6 Unique Methods to Perform Tokenization

Overview Looking to get started with Natural Language Processing (NLP)? Here’s the perfect first step Learn how to perform tokenization – a key aspect to preparing your data for building NLP models We present 6 different ways to perform tokenization on text data Introduction Are you fascinated by the amount of text data available on the internet? Are you looking for ways to work with this text data but aren’t sure where to begin? Machines, after all, recognize numbers, […]

November 7, 2020 Beginner, Computer Vision, Deep Learning, Infographic, Infographics, Listicle, NLP, Python Leave a comment

5 Amazing Deep Learning Frameworks Every Data Scientist Must Know! (with Illustrated Infographic)

Introduction I have been a programmer since before I can remember. I enjoy writing codes from scratch – this helps me understand that topic (or technique) clearly. This approach is especially helpful when we’re learning data science initially. Try to implement a neural network from scratch and you’ll understand a lot of interest things. But do you think this is a good idea when building deep learning models on a real-world dataset? It’s definitely possible if you have days or […]

November 4, 2020 Advanced, Computer Vision, Deep Learning, Image, Image Analysis, NLP, Python, Technique, Unstructured Data Leave a comment

Create your Own Image Caption Generator using Keras!

Overview Understand how image caption generator works using the encoder-decoder Know how to create your own image caption generator using Keras Introduction Image caption Generator is a popular research area of Artificial Intelligence that deals with image understanding and a language description for that image. Generating well-formed sentences requires both syntactic and semantic understanding of the language. Being able to describe the content of an image using accurately formed sentences is a very challenging task, but it could also […]

October 31, 2020 Advanced, Classification, NLP, Python, Supervised, Technique, Text, Unstructured Data Leave a comment

Simple Text Multi Classification Task Using Keras BERT

This article was published as a part of the Data Science Blogathon. Introduction BERT is a really powerful language representation model that has been a big milestone in the field of NLP. It has greatly increased our capacity to do transfer learning in NLP. It comes with great promise to solve a wide variety of NLP tasks. Definitely you will gain great knowledge by the end of this article, keep reading. I am sure you will get good hands-on experience […]

October 29, 2020 Advanced, Deep Learning, NLP, Project, Python, Sequence Modeling, Supervised, Text, Unstructured Data Leave a comment

A Must-Read NLP Tutorial on Neural Machine Translation – The Technique Powering Google Translate

Introduction “If you talk to a man in a language he understands, that goes to his head. If you talk to him in his own language, that goes to his heart.” – Nelson Mandela The beauty of language transcends boundaries and cultures. Learning a language other than our mother tongue is a huge advantage. But the path to bilingualism, or multilingualism, can often be a long, never-ending one. There are so many little nuances that we get lost in the […]

October 19, 2020 Apache

Apache Kafka + KSQL + TensorFlow for Data Scientists via Python + Jupyter Notebook

Why would a data scientist use Kafka Jupyter Python KSQL TensorFlow all together in a single notebook? There is an impedance mismatch between model development using Python and its Machine Learning tool stack and a scalable, reliable data platform. The former is what you need for quick and easy prototyping to build analytic models. The latter is what you need to use for data ingestion, preprocessing, model deployment and monitoring at scale. It requires low latency, high throughput, zero data […]

October 6, 2020 Python

Deep Learning in Keras – Building a Deep Learning Model

Introduction Deep learning is one of the most interesting and promising areas of artificial intelligence (AI) and machine learning currently. With great advances in technology and algorithms in recent years, deep learning has opened the door to a new era of AI applications. In many of these applications, deep learning algorithms performed equal to human experts and sometimes surpassed them. Python has become the go-to language for Machine Learning and many of the most popular and powerful deep learning libraries […]

September 25, 2020 Python

Image Recognition in Python with TensorFlow and Keras

Introduction One of the most common utilizations of TensorFlow and Keras is the recognition/classification of images. If you want to learn how to use Keras to classify or recognize images, this article will teach you how. Definitions If you aren’t clear on the basic concepts behind image recognition, it will be difficult to completely understand the rest of this article. So before we proceed any further, let’s take a moment to define some terms. TensorFlow/Keras Credit: commons.wikimedia.org TensorFlow is an […]

September 25, 2020 Python

Python for NLP: Word Embeddings for Deep Learning in Keras

This is the 16th article in my series of articles on Python for NLP. In my previous article I explained how N-Grams technique can be used to develop a simple automatic text filler in Python. N-Gram model is basically a way to convert text data into numeric form so that it can be used by statisitcal algorithms. Before N-Grams, I explained the bag of words and TF-IDF approaches, which can also be used to generate numeric feature vectors from text […]

September 25, 2020 Python

Python for NLP: Movie Sentiment Analysis using Deep Learning in Keras

This is the 17th article in my series of articles on Python for NLP. In the last article, we started our discussion about deep learning for natural language processing. The previous article was focused primarily towards word embeddings, where we saw how the word embeddings can be used to convert text to a corresponding dense vector, which can be subsequently used as input to any deep learning model. We perform basic classification task using word embeddings. We used custom dataset […]

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