Jun2019 Jul
All (45) | Meetings (2) | News (3) | Opinions (11) | Top Stories, Tweets (6) | Tutorials, Overviews (22) | Webcasts & Webinars (1)
- First hand experience from Uber, Microsoft, Delivery Hero & more – at PAW London
- Jul 15, 2019.
Top practitioners are coming to PAW London, 16-17 Oct 2019, to describe the design, deployment and business impact of their machine learning projects. Register using code KDNUGGETS for a 15% discount on your Predictive Analytics World ticket.
- Scaling a Massive State-of-the-art Deep Learning Model in Production
- Jul 15, 2019.
A new NLP text writing app based on OpenAI's GPT-2 aims to write with you -- whenever you ask. Find out how the developers setup and deployed their model into production from an engineer working on the team.
- Secrets to a Successful Data Science Interview
- Jul 15, 2019.
Are you puzzled as to what to prepare for data science interviews? That you are reading this document is a reflection of your seriousness in being a successful data scientist.
- The Hackathon Guide for Aspiring Data Scientists
- Jul 15, 2019.
This article is an overview of how to prepare for a hackathon as an aspiring data scientist, highlighting the 4 reasons why you should take part in one, along with a series of tips for participation.
- Top Stories, Jul 8-14: The Death of Big Data and the Emergence of the Multi-Cloud Era; Training a Neural Network to Write Like Lovecraft
- Jul 15, 2019.
Also: What's wrong with the approach to Data Science?; Introducing Gen: MITs New Language That Wants to be the TensorFlow of Programmable Inference; Top 10 Data Science Leaders You Should Follow; 5 Probability Distributions Every Data Scientist Should Know; XGBoost and Random Forest with Bayesian Optimisation
- Introducing Gen: MIT’s New Language That Wants to be the TensorFlow of Programmable Inference
- Jul 12, 2019.
Researchers from MIT recently unveiled a new probabilistic programming language named Gen, a language which allow researchers to write models and algorithms from multiple fields where AI techniques are applied without having to deal with equations or manually write high-performance code.
- Pre-training, Transformers, and Bi-directionality
- Jul 12, 2019.
Bidirectional Encoder Representations from Transformers BERT (Devlin et al., 2018) is a language representation model that combines the power of pre-training with the bi-directionality of the Transformer’s encoder (Vaswani et al., 2017). BERT improves the state-of-the-art performance on a wide array of downstream NLP tasks with minimal additional task-specific training.
- Top June Stories: 5 Useful Statistics Data Scientists Need to Know; 7 Steps to Mastering Intermediate Machine Learning with Python – 2019 Edition
- Jul 12, 2019.
Also: Data Science Jobs Report 2019: Python Way Up, TensorFlow Growing Rapidly, R Use Double SAS; If you're a developer transitioning into data science, here are your best resources.
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Top 10 Data Science Leaders You Should Follow
- Jul 12, 2019.
If you’re in the data science field, I strongly encourage you to follow these giants— which I’ll list down in the section below — and be a part of our data science community to learn from the best and share your experience and knowledge. - Do more data science, do less ops with self-service infrastructure & tools
- Jul 11, 2019.
Do more data science, do less ops with self-service infrastructure & tools!
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The Death of Big Data and the Emergence of the Multi-Cloud Era
- Jul 11, 2019.
The Era of Big Data is coming to an end as the focus shifts from how we collect data to processing that data in real-time. Big Data is now a business asset supporting the next eras of multi-cloud support, machine learning, and real-time analytics. - Training a Neural Network to Write Like Lovecraft
- Jul 11, 2019.
In this post, the author attempts to train a neural network to generate Lovecraft-esque prose, known to be awkward and irregular at best. Did it end in success? If not, any suggestions on how it might have? Read on to find out.
- 10 Simple Hacks to Speed up Your Data Analysis in Python
- Jul 11, 2019.
This article lists some curated tips for working with Python and Jupyter Notebooks, covering topics such as easily profiling data, formatting code and output, debugging, and more. Hopefully you can find something useful within.
- Top KDnuggets tweets, Jul 03-09: How to choose a visualization; Data Science Jobs Report 2019
- Jul 10, 2019.
Also: How do you check the quality of your regression model in Python?; NLP vs. NLU: from Understanding a Language to Its Processing; 5 Probability Distributions Every Data Scientist Should Know; 10 More Free Must-Read Books for Machine Learning and Data Science
- How to Learn Python without First Needing to Learn Python
- Jul 10, 2019.
Learn how data scientists and anyone coding with Python can set up a made-to-order runtime in minutes - not days. Read the 3-minute blog post.
- How to Showcase the Impact of Your Data Science Work
- Jul 10, 2019.
You're a Data Scientist -- or preparing to land your first job -- and communicating your work to others, especially employers, so they understand your impact is essential. These five tips will help you help others appreciate your data science.
- A Gentle Guide to Starting Your NLP Project with AllenNLP
- Jul 10, 2019.
For those who aren’t familiar with AllenNLP, I will give a brief overview of the library and let you know the advantages of integrating it to your project.
- What’s wrong with the approach to Data Science?
- Jul 10, 2019.
The job ‘Data Scientist’ has been around for decades, it was just not called “Data Scientist”. Statisticians have used their knowledge and skills using machine learning techniques such as Logistic Regression and Random Forest for prediction and insights for longer than people actually realize.
- Math for Machine Learning
- Jul 9, 2019.
This ebook explains the math involved and introduces you directly to the foundational topics in machine learning.
- Why you’re not a job-ready data scientist (yet)
- Jul 9, 2019.
Trying to snag a dream Data Science job, but can't seem to land one? Check out these four skills that companies really want and be prepared for your next interview.
- Practical Speech Recognition with Python: The Basics
- Jul 9, 2019.
Do you fear implementing speech recognition in your Python apps? Read this tutorial for a simple approach to getting practical with speech recognition using open source Python libraries.
- Annotated Heatmaps of a Correlation Matrix in 5 Simple Steps
- Jul 9, 2019.
A heatmap is a graphical representation of data in which data values are represented as colors. That is, it uses color in order to communicate a value to the reader. This is a great tool to assist the audience towards the areas that matter the most when you have a large volume of data.
- Collaborative Evolutionary Reinforcement Learning
- Jul 8, 2019.
Intel Researchers created a new approach to RL via Collaborative Evolutionary Reinforcement Learning (CERL) that combines policy gradient and evolution methods to optimize, exploit, and explore challenges.
- XGBoost and Random Forest with Bayesian Optimisation
- Jul 8, 2019.
This article will explain how to use XGBoost and Random Forest with Bayesian Optimisation, and will discuss the main pros and cons of these methods.
- Top Stories, Jul 1-7: 5 Probability Distributions Every Data Scientist Should Know; NLP vs. NLU: from Understanding a Language to Its Processing
- Jul 8, 2019.
Also: XLNet Outperforms BERT on Several NLP Tasks; How do you check the quality of your regression model in Python?; How Data Science Is Used Within the Film Industry; Whats the Machine Learning Engineering Job Like; 7 Steps to Mastering Data Preparation for Machine Learning with Python — 2019 Edition
- DSGO19 Announces Speakers and Training Sessions
- Jul 8, 2019.
Tickets now on sale for DataScienceGO, the #1 career-focused data science conference, coming to San Diego, CA, Sep 27-29. Use promo code KD-Nuggets for 20% off.
- Classifying Heart Disease Using K-Nearest Neighbors
- Jul 8, 2019.
I have written this post for the developers and assumes no background in statistics or mathematics. The focus is mainly on how the k-NN algorithm works and how to use it for predictive modeling problems.
- How Data Science Is Used Within the Film Industry
- Jul 5, 2019.
As Data Science is becoming pervasive across so many industries, Hollywood is certainly not being left behind. Learn about how Big Data, analytics, and AI are now core drivers of the movies we watch and how we watch them.
- State of AI Report 2019
- Jul 5, 2019.
This year's "State of AI Report" has been released. Read it to find out about the latest in AI research, talent, industry, and politics form the past 12 months.
- Top 8 Data Science Use Cases in Construction
- Jul 5, 2019.
This article considers several of the most efficient and productive data science use cases in the construction industry.
- Top KDnuggets tweets, Jun 26 – Jul 02: An End-to-End Project on Time Series Analysis and Forecasting with #Python; The biggest mistake while learning #Python for #datascience
- Jul 4, 2019.
Approaches to Text Summarization: An Overview; How to Learn Python for Data Science the Right Way; The biggest mistake you can make while learning #Python for #datascience; Data Science Jobs Report 2019: Python Way Up, TensorFlow Growing Rapidly,
- Cartoon: AI + Self-Driving + BBQ = ?
- Jul 4, 2019.
KDnuggets Cartoon looks at what happens when AI and self-driving technology collide with the traditional summer pastime of grilling.
- 5 Probability Distributions Every Data Scientist Should Know
- Jul 4, 2019.
Having an understanding of probability distributions should be a priority for data scientists. Make sure you know what you should by reviewing this post on the subject.
- NLP vs. NLU: from Understanding a Language to Its Processing
- Jul 3, 2019.
As AI progresses and the technology becomes more sophisticated, we expect existing techniques to evolve. With these changes, will the well-founded natural language processing give way to natural language understanding? Or, are the two concepts subtly distinct to hold their own niche in AI?
- Building a Recommender System, Part 2
- Jul 3, 2019.
This post explores an technique for collaborative filtering which uses latent factor models, a which naturally generalizes to deep learning approaches. Our approach will be implemented using Tensorflow and Keras.
- Nvidia’s New Data Science Workstation — a Review and Benchmark
- Jul 3, 2019.
Nvidia has recently released their Data Science Workstation, a PC that puts together all the Data Science hardware and software into one nice package. The workstation is a total powerhouse machine, packed with all the computing power — and software — that’s great for plowing through data.
- Build your own AutoML computer vision pipeline, July 16 webinar
- Jul 2, 2019.
This webinar will present a step-by-step use case so you can build your own AutoML computer vision pipelines, and will go through the essentials for research, deployment and training using Keras, PyTorch and TensorFlow.
- Examining the Transformer Architecture – Part 2: A Brief Description of How Transformers Work
- Jul 2, 2019.
As The Transformer may become the new NLP standard, this review explores its architecture along with a comparison to existing approaches by RNN.
- 4 Most Popular Alternative Data Sources Explained
- Jul 2, 2019.
Alternative data is the new game changer. To start with alternative data, people might even wonder from where you can get hold of alternative data that can give such a competitive advantage. This post details 4 alternative data sources that you can exploit to the fullest.
- Seven Key Dimensions to Help You Understand Artificial Intelligence Environments
- Jul 2, 2019.
Understanding an AI environment is an incredibly complex task but there are several key dimensions that provide clarity on that reasoning.
- How do you check the quality of your regression model in Python?
- Jul 2, 2019.
Linear regression is rooted strongly in the field of statistical learning and therefore the model must be checked for the ‘goodness of fit’. This article shows you the essential steps of this task in a Python ecosystem.
- What’s the Machine Learning Engineering Job Like
- Jul 1, 2019.
As a relatively new position, the day in the life of a machine learning engineer or data scientist is still a bit fluid. Find out what is like from people working today at Airbnb, SurveyMonkey, and Instagram.
- A Data Scientist’s Path to Understanding Market Simulation
- Jul 1, 2019.
Made possible by recent advances in computing power and machine learning, market simulation employs agent-based modeling, behavioral science and network science to recreate the complex dynamics and rules of how a population of people in a given market behave, influence each other and make decisions.
- XLNet Outperforms BERT on Several NLP Tasks
- Jul 1, 2019.
XLNet is a new pretraining method for NLP that achieves state-of-the-art results on several NLP tasks.
- Top Stories, Jun 24-30: Understanding Cloud Data Services; 7 Steps to Mastering Data Preparation for Machine Learning with Python — 2019 Edition
- Jul 1, 2019.
Also: How To Get Funding For AI Startups; Optimization with Python: How to make the most amount of money with the least amount of risk?; 5 Useful Statistics Data Scientists Need to Know; Data Science Jobs Report 2019: Python Way Up, TensorFlow Growing Rapidly, R Use Double SAS
