Deep Learning, NLP, NMT, AI, ML
Python tutorials
Perform document classification into four defined categories (World, Sports, Business, Sci/Tech). Compare the classifier accuracy with different models ranging from Naïve Bayes to Convolutional Neural Network (CNN) and RCNN. By making use of different feature engineering techniques and extra Natural Language Processing (NLP) features create an accurate text classifier. Document/Text classification is an important task that
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Most modern web applications are powered by a REST API under the hood. That way, developers can separate the front-end code from the back-end logic, and users can interact with the interface dynamically. In this three-part tutorial series, you’re building a REST API with the Flask web framework. You’ve created a foundation with a basic Flask project and added endpoints, which you connected to a SQLite database. You’re also testing your API with Swagger UI API documentation that you’re building […]
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In this blog post, I will guide you through the convoluted nature of calculating CVA for an IRS step by step. Please find the accompanying Python Notebooks on my GitHub page. We will take a look at the 8 steps required to calculate the Credit Valuation Adjustment (CVA) for hedging against the risk of default of the counterparty when entering an Interest Rate Swap (IRS) using
Read moreIn this article, I am writing my implementation of a confusion matrix using python and NumPy. This below function can calculate a confusion matrix of any size or number of classes. If you don’t know what a confusion matrix is, check out this awesome video on it
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Welcome to the first article of the “Machine Learning Deep Dive” biweekly series. Each article will consist of a theoretical summary based on Ethem Alpaydın’s Machine Learning book. For each method, there will be a separate GitHub repository containing sample codes and examples prepared from the scratch and/or using the open-source Python libraries. Wish you pleasant reading!
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In this article, we will discuss how to implement sentiment analysis on a raw tweets dataset in python, find the polarity scores to classify the sentiments, and train a Long short-term memory (LSTM) and Convolutional Neural Network to predict the sentiment polarity and compare the results. Reference
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