2019 Aug Tutorials, Overviews
All (87) | Courses, Education (1) | Meetings (5) | News (6) | Opinions (24) | Top Stories, Tweets (9) | Tutorials, Overviews (41) | Webcasts & Webinars (1)
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Object-oriented programming for data scientists: Build your ML estimator - Aug 30, 2019.
Implement some of the core OOP principles in a machine learning context by building your own Scikit-learn-like estimator, and making it better. -
Deep Learning Next Step: Transformers and Attention Mechanism - Aug 29, 2019.
With the pervasive importance of NLP in so many of today's applications of deep learning, find out how advanced translation techniques can be further enhanced by transformers and attention mechanisms. - 4 Tips for Advanced Feature Engineering and Preprocessing - Aug 29, 2019.
Techniques for creating new features, detecting outliers, handling imbalanced data, and impute missing values.
- A 2019 Guide to Human Pose Estimation - Aug 28, 2019.
Human pose estimation refers to the process of inferring poses in an image. Essentially, it entails predicting the positions of a person’s joints in an image or video. This problem is also sometimes referred to as the localization of human joints.
- TensorFlow 2.0: Dynamic, Readable, and Highly Extended - Aug 27, 2019.
With substantial changes coming with TensorFlow 2.0, and the release candidate version now available, learn more in this guide about the major updates and how to get started on the machine learning platform.
- Introducing AI Explainability 360: A New Toolkit to Help You Understand what Machine Learning Models are Doing - Aug 27, 2019.
Recently, AI researchers from IBM open sourced AI Explainability 360, a new toolkit of state-of-the-art algorithms that support the interpretability and explainability of machine learning models.
- How to count Big Data: Probabilistic data structures and algorithms - Aug 26, 2019.
Learn how probabilistic data structures and algorithms can be used for cardinality estimation in Big Data streams.
- Artificial Intelligence vs. Machine Learning vs. Deep Learning: What is the Difference? - Aug 26, 2019.
Over the past few years, artificial intelligence continues to be one of the hottest topics. And in order to work effectively with it, you need to understand its constituent parts.
- Nothing but NumPy: Understanding & Creating Neural Networks with Computational Graphs from Scratch - Aug 23, 2019.
Entirely implemented with NumPy, this extensive tutorial provides a detailed review of neural networks followed by guided code for creating one from scratch with computational graphs.
- Top Handy SQL Features for Data Scientists - Aug 23, 2019.
Whenever we hear "data," the first thing that comes to mind is SQL! SQL comes with easy and quick to learn features to organize and retrieve data, as well as perform actions on it in order to gain useful insights.
- Order Matters: Alibaba’s Transformer-based Recommender System - Aug 23, 2019.
Alibaba, the largest e-commerce platform in China, is a powerhouse not only when it comes to e-commerce, but also when it comes to recommender systems research. Their latest paper, Behaviour Sequence Transformer for E-commerce Recommendation in Alibaba, is yet another publication that pushes the state of the art in recommender systems.
- How LinkedIn, Uber, Lyft, Airbnb and Netflix are Solving Data Management and Discovery for Machine Learning Solutions - Aug 22, 2019.
As machine learning evolves, the need for tools and platforms that automate the lifecycle management of training and testing datasets is becoming increasingly important. Fast growing technology companies like Uber or LinkedIn have been forced to build their own in-house data lifecycle management solutions to power different groups of machine learning models.
- Understanding Decision Trees for Classification in Python - Aug 21, 2019.
This tutorial covers decision trees for classification also known as classification trees, including the anatomy of classification trees, how classification trees make predictions, using scikit-learn to make classification trees, and hyperparameter tuning.
- Automate Stacking In Python: How to Boost Your Performance While Saving Time - Aug 21, 2019.
Utilizing stacking (stacked generalizations) is a very hot topic when it comes to pushing your machine learning algorithm to new heights. For instance, most if not all winning Kaggle submissions nowadays make use of some form of stacking or a variation of it.
- Detecting stationarity in time series data - Aug 20, 2019.
Explore how to determine if your time series data is generated by a stationary process and how to handle the necessary assumptions and potential interpretations of your result.
- Is Kaggle Learn a “Faster Data Science Education?” - Aug 20, 2019.
Kaggle Learn is "Faster Data Science Education," featuring micro-courses covering an array of data skills for immediate application. Courses may be made with newcomers in mind, but the platform and its content is proving useful as a review for more seasoned practitioners as well.
- An Overview of Python’s Datatable package - Aug 20, 2019.
Modern machine learning applications need to process a humongous amount of data and generate multiple features. Python’s datatable module was created to address this issue. It is a toolkit for performing big data (up to 100GB) operations on a single-node machine, at the maximum possible speed.
- Deep Learning for NLP: Creating a Chatbot with Keras! - Aug 19, 2019.
Learn how to use Keras to build a Recurrent Neural Network and create a Chatbot! Who doesn’t like a friendly-robotic personal assistant?
- Understanding Cancer using Machine Learning - Aug 16, 2019.
Use of Machine Learning (ML) in Medicine is becoming more and more important. One application example can be Cancer Detection and Analysis.
- Pytorch Lightning vs PyTorch Ignite vs Fast.ai - Aug 16, 2019.
Here, I will attempt an objective comparison between all three frameworks. This comparison comes from laying out similarities and differences objectively found in tutorials and documentation of all three frameworks.
- Command Line Basics Every Data Scientist Should Know - Aug 15, 2019.
Check out this introductory guide to completing simple tasks with the command line.
- Introducing the Plato Research Dialogue System: Building Conversational Applications at Uber’s Scale - Aug 15, 2019.
While the process of building simple, domain-specific chatbots has gotten way easier, building large scale, multi-agent conversational applications remains a massive challenge. Recently, the Uber engineering team open sourced the Plato Research Dialogue System, which is the framework powering conversational agents across Uber’s different applications.
- What is Poisson Distribution? - Aug 14, 2019.
An solid overview of the Poisson distribution, starting from why it is needed, how it stacks up to binomial distribution, deriving its formula mathematically, and more.
- Learn how to use PySpark in under 5 minutes (Installation + Tutorial) - Aug 13, 2019.
Apache Spark is one of the hottest and largest open source project in data processing framework with rich high-level APIs for the programming languages like Scala, Python, Java and R. It realizes the potential of bringing together both Big Data and machine learning.
- A 2019 Guide to Semantic Segmentation - Aug 12, 2019.
Semantic segmentation refers to the process of linking each pixel in an image to a class label. These labels could include a person, car, flower, piece of furniture, etc., just to mention a few. We’ll now look at a number of research papers on covering state-of-the-art approaches to building semantic segmentation models.
- Keras Callbacks Explained In Three Minutes - Aug 9, 2019.
A gentle introduction to callbacks in Keras. Learn about EarlyStopping, ModelCheckpoint, and other callback functions with code examples.
- Introduction to Image Segmentation with K-Means clustering, by Nagesh Singh Chauhan - Aug 9, 2019.
Image segmentation is the classification of an image into different groups. Many kinds of research have been done in the area of image segmentation using clustering. In this article, we will explore using the K-Means clustering algorithm to read an image and cluster different regions of the image.
- 9 Tips For Training Lightning-Fast Neural Networks In Pytorch - Aug 9, 2019.
Who is this guide for? Anyone working on non-trivial deep learning models in Pytorch such as industrial researchers, Ph.D. students, academics, etc. The models we're talking about here might be taking you multiple days to train or even weeks or months.
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Knowing Your Neighbours: Machine Learning on Graphs - Aug 8, 2019.
Graph Machine Learning uses the network structure of the underlying data to improve predictive outcomes. Learn how to use this modern machine learning method to solve challenges with connected data. - Inside Pluribus: Facebook’s New AI That Just Mastered the World’s Most Difficult Poker Game - Aug 8, 2019.
The reasons why Pluribus represents a major breakthrough in AI systems might result confusing to many readers. After all, in recent years AI researchers have made tremendous progress across different complex games. However, six-player, no-limit Texas Hold’em still remains one of the most elusive challenges for AI systems.
- What is Benford’s Law and why is it important for data science? - Aug 7, 2019.
Benford’s law is a little-known gem for data analytics. Learn about how this can be used for anomaly or fraud detection in scientific or technical publications.
- Deep Learning for NLP: ANNs, RNNs and LSTMs explained! - Aug 7, 2019.
Learn about Artificial Neural Networks, Deep Learning, Recurrent Neural Networks and LSTMs like never before and use NLP to build a Chatbot!
- Coding Random Forests® in 100 lines of code* - Aug 7, 2019.
There are dozens of machine learning algorithms out there. It is impossible to learn all their mechanics; however, many algorithms sprout from the most established algorithms, e.g. ordinary least squares, gradient boosting, support vector machines, tree-based algorithms and neural networks.
- Feature selection by random search in Python - Aug 6, 2019.
Feature selection is one of the most important tasks in machine learning. Learn how to use a simple random search in Python to get good results in less time.
- 25 Tricks for Pandas - Aug 6, 2019.
Check out this video (and Jupyter notebook) which outlines a number of Pandas tricks for working with and manipulating data, covering topics such as string manipulations, splitting and filtering DataFrames, combining and aggregating data, and more.
- Lagrange multipliers with visualizations and code - Aug 6, 2019.
In this story, we’re going to take an aerial tour of optimization with Lagrange multipliers. When do we need them? Whenever we have an optimization problem with constraints.
- [video] Introduction to Generative Adversarial Networks (for beginners and advanced Data Scientists) - Aug 5, 2019.
Generative Adversarial Networks are driving important new technologies in deep learning methods. With so much to learn, these two videos will help you jump into your exploration with GANs and the mathematics behind the modelling.
- Pytorch Cheat Sheet for Beginners and Udacity Deep Learning Nanodegree - Aug 2, 2019.
This cheatsheet should be easier to digest than the official documentation and should be a transitional tool to get students and beginners to get started reading documentations soon.
- GPU Accelerated Data Analytics & Machine Learning - Aug 2, 2019.
The future is here! Speed up your Machine Learning workflow using Python RAPIDS libraries support.
- Opening Black Boxes: How to leverage Explainable Machine Learning - Aug 1, 2019.
A machine learning model that predicts some outcome provides value. One that explains why it made the prediction creates even more value for your stakeholders. Learn how Interpretable and Explainable ML technologies can help while developing your model.
- A 2019 Guide to Object Detection - Aug 1, 2019.
Object detection has been applied widely in video surveillance, self-driving cars, and object/people tracking. In this piece, we’ll look at the basics of object detection and review some of the most commonly-used algorithms and a few brand new approaches, as well.
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