Sep
2019 Oct Tutorials, Overviews
All (93) | Courses, Education (2) | Meetings (5) | News (4) | Opinions (23) | Top Stories, Tweets (10) | Tutorials, Overviews (48) | Webcasts & Webinars (1)
- How to Build Your Own Logistic Regression Model in Python
- Oct 31, 2019.
A hands on guide to Logistic Regression for aspiring data scientist and machine learning engineer.
- AutoML for Temporal Relational Data: A New Frontier
- Oct 30, 2019.
While AutoML started out as an automation approach to develop optimal machine learning pipelines, extensions of AutoML to Data Science embedded products can now enable the processing of much more, including temporal relational data.
- Research Guide for Transformers
- Oct 30, 2019.
The problem with RNNs and CNNs is that they aren’t able to keep up with context and content when sentences are too long. This limitation has been solved by paying attention to the word that is currently being operated on. This guide will focus on how this problem can be addressed by Transformers with the help of deep learning.
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5 Statistical Traps Data Scientists Should Avoid
- Oct 30, 2019.
Here are five statistical fallacies — data traps — which data scientists should be aware of and definitely avoid. - How to Extend Scikit-learn and Bring Sanity to Your Machine Learning Workflow
- Oct 29, 2019.
In this post, learn how to extend Scikit-learn code to make your experiments easier to maintain and reproduce.
- Data Sources 101
- Oct 28, 2019.
Data collection is one of the first steps of the data lifecycle — you need to get all the data you require in the first place. To collect the right data, you need to know where to find it and determine the effort involved in collecting it. This article answers the most basic question: where does all the data you need (or might need) come from?
- How Bayes’ Theorem is Applied in Machine Learning
- Oct 28, 2019.
Learn how Bayes Theorem is in Machine Learning for classification and regression!
- DeepMind is Using This Old Technique to Evaluate Fairness in Machine Learning Models
- Oct 28, 2019.
Visualizing the datasets is an essential component to identify potential sources of bias and unfairness. DeepMind relied on a method called Causal Bayesian networks (CBNs) to represent and estimate unfairness in a dataset.
- 5 Advanced Features of Pandas and How to Use Them
- Oct 25, 2019.
The pandas library offers core functionality when preparing your data using Python. But, many don't go beyond the basics, so learn about these lesser-known advanced methods that will make handling your data easier and cleaner.
- Harnessing Semiotics and Discourse Communities to Understand User Intent
- Oct 25, 2019.
Semiotics helps us understand the importance of context to determining the meaning of a term and discourse communities provide us with the background context (mental model) by which to correctly interpret its meaning correctly.
- Introduction to Natural Language Processing (NLP)
- Oct 25, 2019.
Have you ever wondered how your personal assistant (e.g: Siri) is built? Do you want to build your own? Perfect! Let’s talk about Natural Language Processing.
- Feature Selection: Beyond feature importance?
- Oct 24, 2019.
In this post, you will see 3 different techniques of how to do Feature Selection to your datasets and how to build an effective predictive model.
- Convolutional Neural Network for Breast Cancer Classification
- Oct 24, 2019.
See how Deep Learning can help in solving one of the most commonly diagnosed cancer in women.
- Intro to Adversarial Machine Learning and Generative Adversarial Networks
- Oct 23, 2019.
In this crash course on GANs, we explore where they fit into the pantheon of generative models, how they've changed over time, and what the future has in store for this area of machine learning.
- How to Measure Foot Traffic Using Data Analytics
- Oct 23, 2019.
You need to know how many people visit your store now and what sort of audience you're acquiring. Foot traffic data is going to be invaluable to the success of your business.
- Time Series Analysis: A Simple Example with KNIME and Spark
- Oct 23, 2019.
The task: train and evaluate a simple time series model using a random forest of regression trees and the NYC Yellow taxi dataset.
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Everything a Data Scientist Should Know About Data Management
- Oct 22, 2019.
For full-stack data science mastery, you must understand data management along with all the bells and whistles of machine learning. This high-level overview is a road map for the history and current state of the expansive options for data storage and infrastructure solutions. - How to Write Web Apps Using Simple Python for Data Scientists
- Oct 22, 2019.
Convert your Data Science Projects into cool apps easily without knowing any web frameworks.
- Anomaly Detection, A Key Task for AI and Machine Learning, Explained
- Oct 21, 2019.
One way to process data faster and more efficiently is to detect abnormal events, changes or shifts in datasets. Anomaly detection refers to identification of items or events that do not conform to an expected pattern or to other items in a dataset that are usually undetectable by a human expert.
- How YouTube is Recommending Your Next Video
- Oct 21, 2019.
If you are interested in learning more about the latest Youtube recommendation algorithm paper, read this post for details on its approach and improvements.
- This Microsoft Neural Network can Answer Questions About Scenic Images with Minimum Training
- Oct 21, 2019.
Recently, a group of AI experts from Microsoft Research published a paper proposing a method for scene understanding that combines two key tasks: image captioning and visual question answering (VQA).
- Building an intelligent Digital Assistant
- Oct 18, 2019.
In this second part we want to outline our own experience building an AI application and reflect on why we chose not to utilise deep learning as the core technology used.
- Writing Your First Neural Net in Less Than 30 Lines of Code with Keras
- Oct 18, 2019.
Read this quick overview of neural networks and learn how to implement your first in very few lines using Keras.
- Data Anonymization – History and Key Ideas
- Oct 17, 2019.
While effective anonymization technology remains elusive, understanding the history of this challenge can guide data science practitioners to address these important concerns through ethical and responsible use of sensitive information.
- How to Easily Deploy Machine Learning Models Using Flask
- Oct 17, 2019.
This post aims to make you get started with putting your trained machine learning models into production using Flask API.
- Probability Learning: Bayes’ Theorem
- Oct 16, 2019.
Learn about one of the fundamental theorems of probability with an easy everyday example.
- The 5 Classification Evaluation Metrics Every Data Scientist Must Know
- Oct 16, 2019.
This post is about various evaluation metrics and how and when to use them.
- Research Guide for Video Frame Interpolation with Deep Learning
- Oct 15, 2019.
In this research guide, we’ll look at deep learning papers aimed at synthesizing video frames within an existing video.
- Three Things to Know About Reinforcement Learning
- Oct 14, 2019.
As an engineer, scientist, or researcher, you may want to take advantage of this new and growing technology, but where do you start? The best place to begin is to understand what the concept is, how to implement it, and whether it’s the right approach for a given problem.
- Using Neural Networks to Design Neural Networks: The Definitive Guide to Understand Neural Architecture Search
- Oct 14, 2019.
A recent survey outlined the main neural architecture search methods used to automate the design of deep learning systems.
- An Overview of Density Estimation
- Oct 14, 2019.
Density estimation is estimating the probability density function of the population from the sample. This post examines and compares a number of approaches to density estimation.
- Beyond Word Embedding: Key Ideas in Document Embedding
- Oct 11, 2019.
This literature review on document embedding techniques thoroughly covers the many ways practitioners develop rich vector representations of text -- from single sentences to entire books.
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Activation maps for deep learning models in a few lines of code
- Oct 10, 2019.
We illustrate how to show the activation maps of various layers in a deep CNN model with just a couple of lines of code. - Contributing to PyTorch: By someone who doesn’t know a ton about PyTorch
- Oct 9, 2019.
By the end of my week with the team, I managed to proudly cut two PRs on GitHub. I decided that I would write a blog post to knowledge share, not just to show that YES, you can too.
- Introduction to Artificial Neural Networks
- Oct 8, 2019.
In this article, we’ll try to cover everything related to Artificial Neural Networks or ANN.
- Know Your Data: Part 2
- Oct 8, 2019.
To build an effective learning model, it is must to understand the quality issues exist in data & how to detect and deal with it. In general, data quality issues are categories in four major sets.
- The 4 Quadrants of Data Science Skills and 7 Principles for Creating a Viral Data Visualization
- Oct 7, 2019.
As a data scientist, your most important skill is creating meaningful visualizations to disseminate knowledge and impact your organization or client. These seven principals will guide you toward developing charts with clarity, as exemplified with data from a recent KDnuggets poll.
- OpenAI Tried to Train AI Agents to Play Hide-And-Seek but Instead They Were Shocked by What They Learned
- Oct 7, 2019.
OpenAI trained agents in a simple game of hide-and-seek and learned many other different skills in the process.
- 10 Free Top Notch Natural Language Processing Courses
- Oct 7, 2019.
Are you looking to learn natural language processing? This collection of 10 free top notch courses will allow you to do just that, with something for every approach to learning NLP and its varied topics.
- The Last SQL Guide for Data Analysis You’ll Ever Need
- Oct 4, 2019.
This is it: the last SQL guide for data analysis you'll ever need! OK, maybe it’s actually the first. But it’ll give you a solid head start.
- Research Guide for Neural Architecture Search
- Oct 4, 2019.
In this guide, we will explore a range of research papers that have sought to solve the challenging task of automating neural network design.
- 5 Fundamental AI Principles
- Oct 3, 2019.
While AI may appear magical at times, these five principles will help guide you to avoid pitfalls when leveraging this tech.
- Recreating Imagination: DeepMind Builds Neural Networks that Spontaneously Replay Past Experiences
- Oct 3, 2019.
DeepMind researchers created a model to be able to replay past experiences in a way that simulate the mechanisms in the hippocampus.
- Choosing the Right Clustering Algorithm for your Dataset
- Oct 2, 2019.
Applying a clustering algorithm is much easier than selecting the best one. Each type offers pros and cons that must be considered if you’re striving for a tidy cluster structure.
- Data Preparation for Machine learning 101: Why it’s important and how to do it
- Oct 2, 2019.
As data scientists who are the brains behind the AI-based innovations, you need to understand the significance of data preparation to achieve the desired level of cognitive capability for your models. Let’s begin.
- Multi-Task Learning – ERNIE 2.0: State-of-the-Art NLP Architecture Intuitively Explained
- Oct 2, 2019.
The tech giant Baidu unveiled its state-of-the-art NLP architecture ERNIE 2.0 earlier this year, which scored significantly higher than XLNet and BERT on all tasks in the GLUE benchmark. This major breakthrough in NLP takes advantage of a new innovation called “Continual Incremental Multi-Task Learning”.
- Sentiment and Emotion Analysis for Beginners: Types and Challenges
- Oct 1, 2019.
There are three types of emotion AI, and their combinations. In this article, I’ll briefly go through these three types and the challenges of their real-life applications.
- Clustering Metrics Better Than the Elbow Method
- Oct 1, 2019.
We show what metric to use for visualizing and determining an optimal number of clusters much better than the usual practice — elbow method.
Introduction to Time Series Analysis in Python