Using Scikit-Learn Pipelines and Converting Them To PMML Introduction Pipelining in machine learning involves chaining all the steps involved in training a model together. The pipeline allows to assemble several steps that can be cross-validated together while setting different parameter values. It is a step closer to automating the all...
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Neural Machine Translation
Application To English To Twi Language Translation
Neural Machine Translation Machine Translation (MT) is the task of translating a sentence x from one language (the source language ) to a sentence y in another language (the target language). Neural Machine Tranlaston(NMT) is a branch of natural language processing that uses deep learning models to predict the likelihood...
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Transfer Learning and Fine-Tuning with TensorFlow
Application To Pneumonia Classification from X_ray Images
Transfer learning involves taking layers/learned features from a model trained on a larger dataset and using those features to initialize training on another similar task. Training deep learning models especially for computer vision requires massive data to perform well. Transfer learning allows models to be trained on smaller dataset by...
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Returns on Exchange Traded Funds
performance of some popular ETF's
Exchange Traded Funds A collection of securities for example stocks which usually tracks an underlying index is known as an Exchange - Traded Fund(ETF). ETF’s share some similarity with mutual funds although they are listed on exchanges and their share is traded like stocks throughout the day. ETF’s can be...
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Cost-Sensitive Deep Learning For Imbalanced Dataset Classification
Applied to Fraud Detection
Introduction Data with imbalanced target class occurs frequently in several domians such as credit card Fraud Detection ,insurance claim prediction, email spam detection, anomaly detection, outlier detection etc. Financial instituions loose millions of dollars every year to fraudulent financial transactions. It is important that these institutions are able to identify...
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