Categories
Blog

Getting Started with Natural Language Processing (NLP)

using simple Python libraries There’s so much going on in natural language processing these days (GRUs, LSTMs, XLNet, BERT and so on!). It can be confusing figuring out where to begin. This article talks about the basics of natural language processing including data cleaning, normalization, encoding, sentiment analysis and a simple text classifier using basic […]

Categories
Blog

Exploratory Data Analysis (EDA) —  Understanding the Gender Divide in Data Science Roles

with Shreejaya Bharathan on 2018 Kaggle ML & DS Survey data Women have been historically underrepresented in STEM fields and face discrimination in the workplace. According to a study conducted in 2018, “63 percent of the time, women receive lower salary offers than men for the same job at the same company.’’ Does the Data […]

Categories
Blog

Creating a web application powered by a fastai model

using React, Flask, Render.com, and Firebase Developing an end-to-end Machine Learning based web-app can appear quite daunting. However, with libraries like fastai, training models has become a lot easier compared to the past. Deployment takes less than ten minutes with sites like Render.com and Firebase. This article describes a simple, step by step approach to […]

Categories
Blog

Training Deep Neural Networks on a GPU with PyTorch

Part 4 of “PyTorch: Zero to GANs” This post is the fourth in a series of tutorials on building deep learning models with PyTorch, an open source neural networks library. Check out the full series: PyTorch Basics: Tensors & Gradients Linear Regression & Gradient Descent Classification using Logistic Regression Feedforward Neural Networks & Training on GPUs […]

Categories
Blog

“How’s that movie?” — Neural collaborative filtering with FastAI

Build a state-of-the-art movie recommendation system with just 10 lines of code Recommender systems are at the core of pretty much every online service we interact with. Social networking sites like Facebook, Twitter and Instagram recommend posts you might like, or people you might know. Video streaming services like YouTube and Netflix recommend videos, movies […]

Categories
Blog

Image Classification using Logistic Regression in PyTorch

Part 3 of “PyTorch: Zero to GANs” This post is the third in a series of tutorials on building deep learning models with PyTorch, an open source neural networks library. Check out the full series: PyTorch Basics: Tensors & Gradients Linear Regression & Gradient Descent Classification using Logistic Regression (this post) Feedforward Neural Networks & Training […]

Categories
Blog

Linear Regression and Gradient Descent from scratch in PyTorch

Part 2 of “PyTorch: Zero to GANs” This post is the second in a series of tutorials on building deep learning models with PyTorch, an open source neural networks library developed and maintained by Facebook. Check out the full series: PyTorch Basics: Tensors & Gradients Linear Regression & Gradient Descent (this post) Classification using Logistic Regression […]

Categories
Blog

PyTorch Basics: Tensors and Gradients

Part 1 of “PyTorch: Zero to GANs” This post is the first in a series of tutorials on building deep learning models with PyTorch, an open source neural networks library developed and maintained by Facebook. Check out the full series: PyTorch Basics: Tensors & Gradients (this post) Linear Regression & Gradient Descent Classification using Logistic […]

Categories
Blog

Linear Regression using PyTorch

Regression involves trying to predict results within a continuous output, meaning that we try to map input variables to some continuous function. In linear regression, this continuous function is a straight line. For example, the cost of an ice-cream could have the following linear equation: ice_cream_price = w1*cost_of_ingredients + w2*temperature + w3*rent_of_shop + … We […]

Categories
Blog

Building a movie genre classifier using a dataset created using Google Images

using fast-ai ‘Google Images’ is a great source to find relevant images while constructing a database for a classification problem. Let’s take the problem of classifying movie posters based on their genre. We’re going to take three classes that have the least overlap: romance, horror, and superhero. Creating the Dataset Getting a list of URLs: The first […]