October 11, 2020 Analytics Vidhya, Beginner, Deep Learning, Listicle, Machine Learning, NLP, Winners Approach Leave a comment

The Winning Approaches from codeFest 2018 – NLP, Computer Vision and Machine Learning!

Introduction Analytics Vidhya’s hackathons are one of the best ways to evaluate how far you’ve traveled in your data science journey. And what better way than to put your skills to the test against the top data scientists from around the globe? Participating in these hackathons also helps you understand where you need to improve and what else you can learn to get a better score in the next competition. And a very popular demand after each hackathon is to […]

October 11, 2020 Classification, Computer Vision, Deep Learning, Image, Libraries, NLP, Programming, Python, PyTorch, Supervised, Unstructured Data Leave a comment

Get Started with PyTorch – Learn How to Build Quick & Accurate Neural Networks (with 4 Case Studies!)

Introduction PyTorch v TensorFlow – how many times have you seen this polarizing question pop up on social media? The rise of deep learning in recent times has been fuelled by the popularity of these frameworks. There are staunch supporters of both, but a clear winner has started to emerge in the last year. PyTorch was one of the most popular frameworks in 2018. It quickly became the preferred go-to deep learning framework among researchers in both academia and the […]

October 11, 2020 Classification, Machine Learning, NLP, Project, Python, Supervised, Technique, Telecom, Text, Unstructured Data Leave a comment

Predicting Movie Genres using NLP – An Awesome Introduction to Multi-Label Classification

Introduction I was intrigued going through this amazing article on building a multi-label image classification model last week. The data scientist in me started exploring possibilities of transforming this idea into a Natural Language Processing (NLP) problem. That article showcases computer vision techniques to predict a movie’s genre. So I had to find a way to convert that problem statement into text-based data. Now, most NLP tutorials look at solving single-label classification challenges (when there’s only one label per observation). […]

October 11, 2020 Computer Vision, Data Science, Intermediate, Listicle, Machine Learning, Maths, NLP, Python, Use Cases Leave a comment

10 Powerful Applications of Linear Algebra in Data Science (with Multiple Resources)

Overview Linear algebra powers various and diverse data science algorithms and applications Here, we present 10 such applications where linear algebra will help you become a better data scientist We have categorized these applications into various fields – Basic Machine Learning, Dimensionality Reduction, Natural Language Processing, and Computer Vision Introduction If Data Science was Batman, Linear Algebra would be Robin. This faithful sidekick is often ignored. But in reality, it powers major areas of Data Science including the hot […]

October 11, 2020 Analytics Vidhya, Intermediate, Listicle, NLP Leave a comment

7 Amazing NLP Hack Sessions to Watch out for at DataHack Summit 2019

Picture a world where: Machines are able to have human-level conversations with us Computers understand the context of the conversation without having to be told what the subject is These machines can even write full-blown essays after being given the theme of the topic This isn’t a movie script or a futuristic scenario – this is all happening right now thanks to the power of Natural Language Processing (NLP)! Here’s the incredible rise charted by Google Trends in the last […]

October 11, 2020 Beginner, Classification, Deep Learning, NLP, Project, Python, PyTorch, Supervised, Technique, Text, Unstructured Data Leave a comment

Build Your First Text Classification model using PyTorch

Overview Learn how to perform text classification using PyTorch Grasp the importance of Pack Padding feature Understand the key points involved while solving text classification Introduction I always turn to State of the Art architectures to make my first submission in data science hackathons. Implementing the State of the Art architectures has become quite easy thanks to deep learning frameworks such as PyTorch, Keras, and TensorFlow. These frameworks provide an easy way to implement complex model architectures and algorithms with […]

October 11, 2020 Beginner, Listicle, NLP Leave a comment

Top 10 Applications of Natural Language Processing (NLP)

Introduction Natural Language Processing is among the hottest topic in the field of data science. Companies are putting tons of money into research in this field. Everyone is trying to understand Natural Language Processing and its applications to make a career around it. Every business out there wants to integrate it into their business somehow. Do you know why? Because just in a few years’ time span, natural language processing has evolved into something so powerful and impactful, which […]

October 11, 2020 Beginner, Machine Learning, NLP, Python, Technique Leave a comment

Machine Learning in Cyber Security — Malicious Software Installation

Introduction Monitoring of user activities performed by local administrators is always a challenge for SOC analysts and security professionals. Most of the security framework will recommend the implementation of a whitelist mechanism. However, the real world is often not ideal. You will always have different developers or users having local administrator rights to bypass controls specified. Is there a way to monitor the local administrator activities?

October 9, 2020 Python

Save Plot as Image with Matplotlib

Introduction Matplotlib is one of the most widely used data visualization libraries in Python. It’s common to share Matplotlib plots and visualizations with others. In this article, we’ll take a look at how to save a plot/graph as an image file using Matplotlib. Creating a Plot Let’s first create a simple plot: import matplotlib.pyplot as plt import numpy as np x = np.arange(0, 10, 0.1) y = np.sin(x) plt.plot(x, y) plt.show() Here, we’ve plotted a sine function, starting at 0 […]

October 9, 2020 Neural Machine Translation (NMT), NMT Leave a comment

Machine Translation Weekly 44: Tangled up in BLEU (and not blue)

For quite a while, machine translation is approached as a behaviorist simulation. Don’t you know what a good translation is? It does not matter, you can just simulate what humans do. Don’t you know how to measure if something is a good translation? It does not matter, you can simulate what humans do again. Things seem easy. We learn how to translate from tons of training data that were translated by humans. When we want to measure how well the […]

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