Common Data Cleaning Tasks in Everyday Work of a Data Scientist/Analyst in Python
Author(s): Rashida Nasrin Sucky Originally published on Towards AI. A Data Cleaning Cheat Sheet Photo by Brooks Rice on Unsplash Data cleaning is an essential part of your life if you are a data scientist, data analyst, or machine learning engineer. In …
Let’s Learn: Neural Nets #3 — Activation Functions
Author(s): Bradley Stephen Shaw Originally published on Towards AI. A beginner’s guide to activation functions in neural nets. Photo by Antoine Dautry on Unsplash Today we’ll be looking at activation functions in neural nets — the who, what, where, and why of …
Myers-Briggs 16 Personality Types
Author(s): Viridiana Romero Martinez Originally published on Towards AI. When I was in college, I used to watch Gilmore Girls, an American comedy-drama series created by Amy Sherman-Palladino, starring Lauren Graham and Alexis Bledel. I watched the 7 seasons, plus the sequel …
Oriented FAST and Rotated BRIEF (ORB)
Author(s): Garima Nishad Originally published on Towards AI. So first let’s get a general idea about what it does & how it works. Then we’ll see both of these algorithms separately i.e. FAST and BRIEF. Fast and brief, are a feature detection …
TensorFlow to CoreML Conversion and Model Inspection
Author(s): Mirek Stanek Originally published on Towards AI. Converting TF models to CoreML, an iOS-friendly format This post was originally published at thinkmobile.dev — a blog about implementing intelligent solutions in mobile apps (link to article). While TensorFlow Lite seems to be …
Prediction of Relative Locations of CT Slices in CT Images
Author(s): Avishek Nag Originally published on Towards AI. Predicting the relative location of CT slices on the axial axis of the human body using regression techniques on very high-dimensional data Regression is one of the most fundamental techniques in Machine Learning. In …
A few key points…
Author(s): Lawrence Alaso Krukrubo Originally published on Towards AI. Using matrix algebra… Hello World, ever wondered how your favourite Machine Learning algorithms actually work? Let’s see how a Multiple Linear Regression(MLR) model computes the ideal parameters, given the features matrix (X) and …
An Intuitive Introduction to Document Vector(Doc2Vec)
Author(s): Manish Nayak Originally published on Towards AI. Intro to Doc2Vec U+007C Towards AI Introduction Doc2Vec is an extension of Word2vec that encodes entire documents as opposed to individual words. You can read about Word2Vec in my previous post. Doc2Vec vectors represent …
Near-Optimal Representation Learning for Hierarchical Reinforcement Learning
Author(s): Sherwin Chen Originally published on Towards AI. Beyond Hierarchical Reinforcement learning with Off-policy correction(HIRO) This is the second post of the series, in which we will talk about a novel Hierarchical Reinforcement Learning built upon HIerarchical Reinforcement learning with Off-policy correction(HIRO) …
Understanding Multi-Label classification model and accuracy metrics
Author(s): Avishek Nag Originally published on Towards AI. The theory behind the multi-label/multi-tagging model, different umbrella classification schemes and accuracy metric analysis Classification techniques probably are the most fundamental in Machine Learning. The majority of all online ML/AI courses and curriculums start …
Multi-Label Text Classification Using Scikit-multilearn: a Case Study with StackOverflow Questions
Author(s): Avishek Nag Originally published on Towards AI. Designing a multi-label text classification model which helps to tag stackoverflow.com questions with different topics Everyday users of stackoverflow.com posts many technical questions and all those get tagged with different topics. In this article, …
HAC: Learning Multi-Level Hierarchies with Hindsight
Author(s): Sherwin Chen Originally published on Towards AI. Hindsight experience replay for multi-level hierarchies the 4-level HAC agent on the inverted pendulum We discuss a novel Hierarchical Reinforcement Learning(HRL) framework that can efficiently learn multiple levels of policies in parallel. Experiments shows, …
Demystifying the Architecture of Long Short Term Memory (LSTM) Networks
Author(s): Manish Nayak Originally published on Towards AI. Architecture of LSTMs U+007C Towards AI Introduction In my previous article, I explain RNNs’ Architecture. RNNs are not perfect and they mainly suffer from two major issues exploding gradients and vanishing gradients. Exploding gradients …
Build TensorFlow Lite Model with Firebase AutoML Vision Edge
Author(s): Mirek Stanek Originally published on Towards AI. Train first image classification model with Firebase ML Kit This post was originally published at thinkmobile.dev — a blog about implementing intelligent solutions in mobile apps (link to article). For more than a year …
How to Use scikit-learn ‘eli5’ Library to Compute Permutation Importance?
Author(s): Abhinav Prakash Originally published on Towards AI. Feature Permutation Importance with ‘eli5’ U+007C Towards AI Understanding the workings of scikit-learn’s ‘eli5’ library to compute feature importance on a sample housing dataset and interpreting its results cc: Forbes Most of the Data …
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