A Gentle Introduction to Graph Embeddings
Author(s): Edward Ma Originally published on Towards AI. TransE Top highlight Photo by Edward Ma on Unsplash Instead of using traditional machine learning classification tasks, we can consider using graph neural network (GNN) to perform node classification problems. By providing an explicit …
How To Use Counterfactual Evaluation To Estimate Online AB Test Results
Author(s): ___ Originally published on Towards AI. Definition In this article, I will explain a principled approach to estimate the expected performance of a model in an online AB test using only offline data. This is very useful to help decide which …
Random Walk in Node Embeddings (DeepWalk, node2vec, LINE, and GraphSAGE)
Author(s): Edward Ma Originally published on Towards AI. Graph Embeddings Top highlight Photo by Steven Wei on Unsplash Instead of using traditional machine learning classification tasks, we can consider using graph neural network (GNN) to perform node classification problems. By providing an …
Generate Quotes with Web Scrapping, Glove Embeddings, and LSTM in Pytorch
Author(s): Lakshmi Narayana Santha Originally published on Towards AI. Introduction With the rise of advancement in research in NLP specially in Language Models, text generation – a classical machine learning task which solved using Recurrent Networks. In this article we walk through …
Building a Spam Detector Using Python’s NTLK Package
Author(s): Bindhu Balu Originally published on Towards AI. NTLK — Natural Language ToolKit In this part, we will go through an end to end walkthrough of building a very simple text classifier in Python 3. Our goal is to build a predictive …
4 Graph Neural Networks you Need to Know (WLG, GCN, GAT, GIN)
Author(s): Edward Ma Originally published on Towards AI. Top highlight Photo by Edward Ma on Unsplash We went through Knowledge Graph Embeddings and Random Walk in previous graph neural network stories. Knowledge graph embeddings train entity embeddings for downstream tasks. On the …
Why Batch Normalization Matters?
Author(s): Aman Sawarn Originally published on Towards AI. Understanding why batch normalization works, along with its intuitive elegance. Batch Normalization(BN) has become the-state-of-the-art right from its inception. It enables us to opt for higher learning rates and use sigmoid activation functions even …
Problem Framing: The Most Difficult Stage of a Machine Learning Project Workflow
Author(s): Benjamin Obi Tayo Ph.D. Originally published on Towards AI. Framing the right problem to tackle with data in a real-world project is not very straightforward Top highlight Photo by You X Ventures on Unsplash In this article, I will discuss why …
Attention! Beginners, Theory: Challenging terms and Methods for Facial Features Recognition
Author(s): Surya Govind Originally published on Towards AI. Theory Explained ( Computer Vision ): Pattern recognition ( Facial Recognition ) with Challenges and possible methods Image by newsroom.cisco.com from Laurence Cruz Energy-saving tip folk: Better start with OpenCV-C++/ Python, https://docs.opencv.org/2.4/modules/contrib/doc/facerec/facerec_tutorial.html is a …
EfficientDet: When Object Detection Meets Scalability and Efficiency
Author(s): Aniket Maurya Originally published on Towards AI. EfficientDet, a highly efficient and scalable state of the art object detection model developed by Google Research, Brain Team. It is not just a single model. It has a family of detectors which achieve …
Attention Beginners! Powerful Exposure of Eye Gaze Tracking Procedure
Author(s): Surya Govind Originally published on Towards AI. Explanation of the Existing system and steps required for eye gaze estimation Image from bdtechtalks.com by Ben Dickson. For your convenience, I have added a few importnt explanation links for the concepts that are …
Titanic Challenge — Machine Learning for Disaster Recovery — Part 2
Author(s): Bindhu Balu Originally published on Towards AI. Source: Pxfuel Part-2 — Predictive Model Building Code Location: https://github.com/BindhuVinodh/Titanic—Predictive-Model-Building II — Feature engineering In the previous part, we flirted with the data and spotted some interesting correlations. In this part, we’ll see how …
For The Win: An AI Agent Achieves Human-Level Performance in a 3D Video Game
Author(s): Sherwin Chen Originally published on Towards AI. Environment Observation Source: https://deepmind.com/blog/article/capture-the-flag-science In this article, we’ll discuss For The Win(FTW) agent, from DeepMind, that achieves human-level performance in a popular 3D team-based multiplayer first-person video game. The FTW agent utilizes a novel …
NLP News Cypher | 01.19.20
Author(s): Ricky Costa Originally published on Towards AI. Photo by Robert Murray on Unsplash NLP News Cypher U+007C 01.19.20 Towards AI and Infinity And…. we’re back! What a week! First, we are happy to announce we are now publishing our weekly blog …
Will Your Education Pay You Well?
Author(s): Harsh Darji Originally published on Towards AI. Wage analysis using Random Forest https://pixabay.com/photos/woman-adult-people-money-3261425/ Wage analysis is a process of comparing the salaries based on the attributes attached to the employee. Of course, there are several factors like the company, location which …
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