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Multiclass Text Classification using LSTM in Pytorch

Predicting item ratings based on customer reviews Human language is filled with ambiguity, many-a-times the same phrase can have multiple interpretations based on the context and can even appear confusing to humans. Such challenges make natural language processing an interesting but hard problem to solve. However, we’ve seen a lot of advancement in NLP in the […]

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Image Processing Techniques for Computer Vision

Image Processing is an integral part of Computer vision. We almost always want to resize images, do data augmentation, see images in a grid, etc. OpenCV (Open source computer vision), scikit-image, Pillow are some popular image processing libraries in Python. In this article, I’ve covered some of the most commonly used Image processing techniques. Here’s […]

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Deep Learning for Tabular Data using PyTorch

On a multiclass classification problem Deep learning has proved to be groundbreaking in a lot of domains like Computer Vision, Natural Language Processing, Signal Processing, etc. However, when it comes to more structured, tabular data consisting of categorical or numerical variables, traditional machine learning approaches (such as Random Forests, XGBoost) are believed to perform better. […]

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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 […]

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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 […]

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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 […]

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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 […]

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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 […]

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Building a Flower Classifier using Fast.ai

Using ResNet-34 To build our flower classifier, we are going to use the Flowers Recognition dataset provided by Kaggle. The pictures are divided into five classes: daisy, tulip, rose, sunflower, dandelion. For each class there are about 800 photos. Dividing data into Training and Validation Sets Since our data is not already segregated into training […]

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Machine Learning Notes — Week 1

My notes for the Machine Learning Course on Coursera by Andrew NG for week 1 Definition of Machine Learning: The field of study that gives computers the ability to learn without being explicitly programmed. In other words, a Computer program is said to learn from experience E with respect to some class of tasks T and […]