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[Download] Real-Life ML Examples + Notebooks
In this eBook, we will walk you through four Machine Learning use cases on Databricks: Loan Risk Use Case; Advertising Analytics & Prediction Use Case; Market Basket Analysis Problem at Scale; Suspicious Behavior Identification in Video Use Case. Get your copy now!
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The Long Tail of Medical Data
This article discusses some issues related to medical data, relating specifically to power law distributions and computer aided diagnosis. Read on to see machine learning's place in the puzzle.
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Multi-Class Text Classification with Doc2Vec & Logistic Regression
Doc2vec is an NLP tool for representing documents as a vector and is a generalizing of the word2vec method. In order to understand doc2vec, it is advisable to understand word2vec approach.
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Best Practices for Using Notebooks for Data Science
Are you interested in implementing notebooks for data science? Check out these 5 things to consider as you begin the process.
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Mastering the Learning Rate to Speed Up Deep Learning
Figuring out the optimal set of hyperparameters can be one of the most time consuming portions of creating a machine learning model, and that’s particularly true in deep learning.
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Multi-Class Text Classification Model Comparison and Selection
This is what we are going to do today: use everything that we have presented about text classification in the previous articles (and more) and comparing between the text classification models we trained in order to choose the most accurate one for our problem.
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 SQL, Python, & R in One Platform
No more jumping between applications. Mode Studio combines a SQL editor, Python and R notebooks, and a visualization builder in one platform.
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Named Entity Recognition and Classification with Scikit-Learn
Named Entity Recognition and Classification is a process of recognizing information units like names, including person, organization and location names, and numeric expressions from unstructured text. The goal is to develop practical and domain-independent techniques in order to detect named entities with high accuracy automatically.
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Generative Adversarial Networks – Paper Reading Road Map
To help the others who want to learn more about the technical sides of GANs, I wanted to share some papers I have read in the order that I read them.
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Building a Question-Answering System from Scratch
This part will focus on introducing Facebook sentence embeddings and how it can be used in building QA systems. In the future parts, we will try to implement deep learning techniques, specifically sequence modeling for this problem.
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SQL, Python, & R in One Platform
Named Entity Recognition and Classification with Scikit-Learn