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Basics of Pandas: Part 1

If you are even remotely interested in data science, this blog post will surely help you. In this post we are going to talk about Pandas. Not the cute animal, but Pandas stands for ‘Python Data Analysis’. Pandas is an open-source Python library that is built on top of NumPy. As the name suggests, it […]

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Basics of NumPy: Part 2

One simply can’t stress enough upon the importance of NumPy when it comes to programmers. Welcome to the second part of the blog where we shall cover some advanced but essential topics under NumPy. For those of you, who are joining us for the first time, it would be beneficial to check out the first […]

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Basics of NumPy: Part 1

The universe of machine learning and data science is a fascinating one. On the surface, it may seem as though one is inundated with data in various forms – be it text, image, or voice, however, if dealt with properly, it makes for not just a great learning experience, but a rather enjoyable one too! […]

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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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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 […]