Feb
2019 Mar
All (107) | Courses, Education (10) | Meetings (15) | News (13) | Opinions (28) | Top Stories, Tweets (9) | Tutorials, Overviews (30) | Webcasts & Webinars (2)
- Datathon 2019: The International Data Science Hackathon, 12-14 April - Mar 29, 2019.
The Data Science Hackathon is open for the global community to participate from all around the world virtually.
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Explaining Random Forest® (with Python Implementation) - Mar 29, 2019.
We provide an in-depth introduction to Random Forest, with an explanation to how it works, its advantages and disadvantages, important hyperparameters and a full example Python implementation. - A Beginner’s Guide to Linear Regression in Python with Scikit-Learn, by Nagesh Singh Chauhan - Mar 29, 2019.
What linear regression is and how it can be implemented for both two variables and multiple variables using Scikit-Learn, which is one of the most popular machine learning libraries for Python.
- Interpolation in Autoencoders via an Adversarial Regularizer - Mar 29, 2019.
Adversarially Constrained Autoencoder Interpolation (ACAI; Berthelot et al., 2018) is a regularization procedure that uses an adversarial strategy to create high-quality interpolations of the learned representations in autoencoders.
- Gain the Skills You Need to Level-Up in Your Data-Driven Career - Mar 28, 2019.
If you work with data in your organization and want to expand your role, or you're considering a career move into a data analytics or data science role, the University of Delaware's 100% online Master of Science in Applied Statistics (ASTAT) can help you succeed.
- D3.js Graph Gallery for Data Visualization - Mar 28, 2019.
The d3 graph gallery is a collection of 200 simple charts made with d3.js, with reproducible, commented and editable code.
- 7 “Gotchas” for Data Engineers New to Google BigQuery - Mar 28, 2019.
Here are some things that might take some getting used to when new to Google BigQuery, along with mitigation strategies where I’ve found them.
- The Deep Learning Toolset — An Overview - Mar 28, 2019.
We are observing an increasing number of great tools that help facilitate the intricate process that is deep learning, making it both more accessible and more efficient.
- Top KDnuggets tweets, Mar 20-26: 10 More Free Must-Read Books for Machine Learning and Data Science - Mar 27, 2019.
Also - 7 Steps to Mastering Basic Machine Learning with Python - 2019 Edition; 10 Free Must-See Courses for Machine Learning and Data Science; How to Train a Keras Model 20x Faster with a TPU for Free.
- Network with Google, Intel, Facebook, LinkedIn & more - Mar 27, 2019.
The Predictive Analytics Innovation Summit takes place Apr 29 & 30 in San Diego. Secure your place at this must-attend event for data professionals today and deep-dive into a new era of AI and data strategy.
- [PDF] Python: The Programmer’s Lingua Franca - Mar 27, 2019.
This paper presents the case that Python is the language best suited to becoming a programmer’s lingua franca.
- Explainable AI or Halting Faulty Models ahead of Disaster - Mar 27, 2019.
A brief overview of a new method for explainable AI (XAI), called anchors, introduce its open-source implementation and show how to use it to explain models predicting the survival of Titanic passengers.
- How to Choose the Right Chart Type - Mar 27, 2019.
This article presents an infographic for choosing which chart type is most useful in a given scenario. The infographic and chart types are then explored for greater clarity.
- Data Pipelines, Luigi, Airflow: Everything you need to know - Mar 27, 2019.
This post focuses on the workflow management system (WMS) Airflow: what it is, what can you do with it, and how it differs from Luigi.
- Top Stories, Mar 18-24: Another 10 Free Must-Read Books for Machine Learning and Data Science; Artificial Neural Networks Optimization using Genetic Algorithm with Python - Mar 26, 2019.
Also: 8 Reasons Why You Should Get a Microsoft Azure Certification; How To Work In Data Science, AI, Big Data; How to Train a Keras Model 20x Faster with a TPU for Free; Who is a typical Data Scientist in 2019?; My Best Tips for Agile Data Science Research
- How to solve 4 big problems in data science – eBook. - Mar 26, 2019.
This eBook includes insights on how data scientists from 4 leading companies delivered impressive business results such as accelerating global inventory from 48 hours to 45 minutes and reducing operational cost of analytics infrastructure by 30%. Get the eBook now!
- The Four Levels of Analytics Maturity - Mar 26, 2019.
We outline our four-step model to categorize how successfully a company uses analytics by its ability to show the analytics, uncover underlying trends, and take action based on them.
- Pedestrian Detection in Aerial Images Using RetinaNet - Mar 26, 2019.
Object Detection in Aerial Images is a challenging and interesting problem. By using Keras to train a RetinaNet model for object detection in aerial images, we can use it to extract valuable information.
- Data Science for Decision Makers: A Discussion with Dr Stelios Kampakis - Mar 26, 2019.
This article contains an interview veteran data scientist, Dr Stylianos (Stelios) Kampakis, in which he discusses his career, and how he helps decision makers across a range of businesses understand how data science can benefit them.
- Earn an IBM Data Science Certificate - Mar 25, 2019.
IBM’s Data Science Professional Certificate program on Coursera brings you everything you need to plunge into an exciting career in data science—no prior experience required! Start learning today.
- Scaling Big Data and AI – Spark + AI Summit 2019 - Mar 25, 2019.
Data and AI are all about scale. Databricks is bringing the Spark + AI Summit to San Francisco Apr 23-25. Check out the full list of sessions at Summit to see more exciting talks. Use code KDNuggets200 and get $200 off registration.
- The AI Black Box Explanation Problem - Mar 25, 2019.
Introducing Black Box AI, a system for automated decision making often based on machine learning over big data, which maps a user’s features into a class predicting the behavioural traits of the individuals.
- R vs Python for Data Visualization - Mar 25, 2019.
This article demonstrates creating similar plots in R and Python using two of the most prominent data visualization packages on the market, namely ggplot2 and Seaborn.
- Feature Reduction using Genetic Algorithm with Python - Mar 25, 2019.
This tutorial discusses how to use the genetic algorithm (GA) for reducing the feature vector extracted from the Fruits360 dataset in Python mainly using NumPy and Sklearn.
- Make better decisions with data in every corner of your business - Mar 22, 2019.
Mode is the data science platform that helps you get data in every corner of your business and create a single source of truth. Free your data science team, automate everything, and create a single source of truth.
- Win KDnuggets Pass to Artificial Intelligence Conference, Apr 15-18, NYC - Mar 22, 2019.
KDnuggets readers have a chance to win a Bronze Pass to AI Conference 2019, a new must-attend conference for AI business and technology.
- The Problem With Self-Serve Analytics - Mar 22, 2019.
A hands-off approach would seem reckless for questions about things like security. And yet that approach is not just the norm for analytical questions in most organizations; it's often the ideal.
- Checklist for Debugging Neural Networks - Mar 22, 2019.
Check out these tangible steps you can take to identify and fix issues with training, generalization, and optimization for machine learning models.
- Make bold career moves - Mar 21, 2019.
At Vettery, we're flipping the job search on its head. Take a breather and consider an important question: is your job working just as hard as you are? If you're wondering what's out there or if you're ready to take a big step in your career, let your next job come to you.
- Predictive Analytics Innovation Summit, April 29-30, San Diego - Mar 21, 2019.
This summit is a must-attend event for data professionals, providing a deep-dive into a new era of AI and data strategy.
- How to Capture Data to Make Business Impact - Mar 21, 2019.
We take a look at the formula for calculating the efficiency of a data capturing method, before going onto explain the concept of Smart Data.
- Top 8 Data Science Use Cases in Manufacturing - Mar 21, 2019.
Data science is said to change the manufacturing industry dramatically. Let's take under consideration several data science use cases in manufacturing that have already become common and brought benefits to the manufacturers.
- My Best Tips for Agile Data Science Research - Mar 21, 2019.
This post demonstrates how to bring maximum value in minimal time using agile methods in data science research.
- Top KDnuggets tweets, Mar 13-19: Top R Packages for Data Cleaning; How to solve 90% of #NLP problems - Mar 20, 2019.
Also: Artificial Neural Networks Optimization using Genetic Algorithm with Python; How To Work In Data Science, AI, Big Data; Why #BERT has 3 Embedding Layers and Their Implementation Details #DeepLearning; How to Train a Keras Model 20x Faster with a TPU for Free
- Deep Compression: Optimization Techniques for Inference & Efficiency - Mar 20, 2019.
We explain deep compression for improved inference efficiency, mobile applications, and regularization as technology cozies up to the physical limits of Moore's law.
- Deploy your PyTorch model to Production - Mar 20, 2019.
This tutorial aims to teach you how to deploy your recently trained model in PyTorch as an API using Python.
- Just Announced, Keynotes and Major Global Events in Data Science - Mar 19, 2019.
ODSC is delighted to host an incredible lineup of leading experts in data science and AI from around the globe, with conferences in Boston, San Francisco, London, Bengaluru, São Paulo, and more on the way. Registration is now open!
- Xing, Heycar & Microsoft on stage at Deep Learning World in Munich - Mar 19, 2019.
Deep Learning World Munich is coming May 6-7 in Munich, Germany. You still have the chance to get your early bird pass until Apr 5. Secure your ticket for a lower price and access case studies and deep dives covering the commercial deployment of deep learning!
- Mastering Fast Gradient Boosting on Google Colaboratory with free GPU - Mar 19, 2019.
CatBoost is a fast implementation of GBDT with GPU support out-of-the-box. Google Colaboratory is a very useful tool with free GPU support.
- How to Train a Keras Model 20x Faster with a TPU for Free - Mar 19, 2019.
This post shows how to train an LSTM Model using Keras and Google CoLaboratory with TPUs to exponentially reduce training time compared to a GPU on your local machine.
- Top Stories, Mar 11-17: Who is a typical Data Scientist in 2019?; The Pareto Principle for Data Scientists - Mar 18, 2019.
Also: Another 10 Free Must-Read Books for Machine Learning and Data Science; Building NLP Classifiers Cheaply With Transfer Learning and Weak Supervision; My favorite mind-blowing Machine Learning/AI breakthroughs; The 7 Myths of Data Anonymisation
- Attend a PAW Workshop – Coming to a City Near You this Year - Mar 18, 2019.
PAW Workshops are coming to four cities in 2019. Along with top-tier keynotes and information-packed sessions, Predictive Analytics World brings you a wide selection of in-depth workshops this year in Munich, Las Vegas, London, and Berlin.
- 8 Reasons Why You Should Get a Microsoft Azure Certification - Mar 18, 2019.
With huge and growing popularity of Microsoft Azure, getting that certification will advance your career. Consider these 8 reasons for taking an Azure certification course
- Overcoming distrust on the path to productive analytics - Mar 18, 2019.
We outline the importance of overcoming distrust in data and analytics, with tips on how to align all stakeholders, being a data optimist, streamlining the process, and more.
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Artificial Neural Networks Optimization using Genetic Algorithm with Python - Mar 18, 2019.
This tutorial explains the usage of the genetic algorithm for optimizing the network weights of an Artificial Neural Network for improved performance. - How To Work In Data Science, AI, Big Data - Mar 18, 2019.
There are many facets to working in Data Science. Your role will depend greatly on the industry you pick and the area of Data Science you want to pursue. A Data Science career is very dynamic and requires a team effort to succeed.
- [eBook] Standardizing the Machine Learning Lifecycle - Mar 15, 2019.
We explore what makes the machine learning lifecycle so challenging compared to regular software, and share the Databricks approach.
- Top R Packages for Data Cleaning - Mar 15, 2019.
Data cleaning is one of the most important and time consuming task for data scientists. Here are the top R packages for data cleaning.
- Building NLP Classifiers Cheaply With Transfer Learning and Weak Supervision - Mar 15, 2019.
In this blog, I’ll walk you through a personal project in which I cheaply built a classifier to detect anti-semitic tweets, with no public dataset available, by combining weak supervision and transfer learning.
- Deep Learning Summit Boston – Meet RE•WORK’s Newest Speakers - Mar 14, 2019.
RE-WORK returns to Boston in May to showcase global experts in Deep Learning. Get up to 50% off passes with code KDNUGGETS if you register before March 22.
- Hone your coding and AI skills with a Computer Science online MSc - Mar 14, 2019.
Gain the necessary technical skills to further your career in an ever-growing sector with U. of Bath Computer Science online MSc. Apply until 1 April 2019.
- My favorite mind-blowing Machine Learning/AI breakthroughs - Mar 14, 2019.
We present some of our favorite breakthroughs in Machine Learning and AI in recent times, complete with papers, video links and brief summaries for each.
- Cartoon: AI and March Madness - Mar 14, 2019.
AI has mastered chess, Go, and other games, but can AI master March Madness? KDnuggets Cartoon imagines one scenario when this happens.
- Advanced Keras — Accurately Resuming a Training Process - Mar 14, 2019.
This article on practical advanced Keras use covers handling nontrivial cases where custom callbacks are used.
- Top KDnuggets tweets, Mar 06-12: Most impactful AI trends of 2018; Google open-sources GPipe for efficiently training large deep neural networks - Mar 13, 2019.
The rise of ML Engineering; Build your own Robust #DeepLearning Environment in Minutes; Another 10 Free Must-Read Books for Machine Learning and Data Science; Top 5 #MachineLearning Courses for 2019 - from @Coursera and @EdX.
- Advanced Analytics & Data Gov Training in Chicago: ML, DL, Self-Service, Strategy, and more - Mar 13, 2019.
Learn effective data governance practices and how to successfully implement advanced analytics by attending our industry leading training at TDWI Chicago, April 28 - May 3, and take your projects to the next level.
- [PDF] Executive Guide To Machine Learning - Mar 13, 2019.
The Executive Guide covers the benefits to your business, the build-or-buy process, and gives a practical overview for implementing ML in your organization.
- Towards Automatic Text Summarization: Extractive Methods - Mar 13, 2019.
The basic idea looks simple: find the gist, cut off all opinions and detail, and write a couple of perfect sentences, the task inevitably ended up in toil and turmoil. Here is a short overview of traditional approaches that have beaten a path to advanced deep learning techniques.
- Apply for a Mozilla Fellowship - Mar 13, 2019.
During their 10-month tenure, Mozilla fellows design products, run campaigns, and influence policy around the theme of “better machine decision making.” Fellows receive competitive funding + benefits, and a travel stipend. Apply by April 8.
- Object Detection with Luminoth - Mar 13, 2019.
In this article you will learn about Luminoth, an open source computer vision library which sits atop Sonnet and TensorFlow and provides object detection for images and video.
- Get the guidebook for tackling data privacy & compliance - Mar 12, 2019.
This guidebook walks through the myths & realities of pseudonymization and working with personal data, and suggests data team processes for compliance.
- Securing your future in big data - Mar 12, 2019.
With four highly-specialised data analytics modules, and the practical business knowledge provided by the core MBA modules, NTU online course can prepare you for a career in big data.
- Top Stories, Mar 4-10: Another 10 Free Must-Read Books for Machine Learning and Data Science; 19 Inspiring Women in AI, Big Data, Data Science, ML - Mar 12, 2019.
Also: Neural Networks seem to follow a puzzlingly simple strategy to classify images; Neural Networks with Numpy - Intro for Absolute Beginners.
- AI: Arms Race 2.0 - Mar 12, 2019.
An analysis of the current state of the competition between US, Europe, and China in AI, examining research, patent publications, global datasphere, devices and IoT, people, and more.
- The 7 Myths of Data Anonymisation - Mar 12, 2019.
Anonymisation has always been rather seen as a necessary evil instead of a helpful tool. That’s why plenty of myths have arisen around that technology over the years.
- People Tracking using Deep Learning - Mar 12, 2019.
Read this overview of people tracking and how deep learning-powered computer vision has allowed for phenomenal performance.
- Download your DATAx guide to AI in healthcare - Mar 11, 2019.
AI is expected to contribute $150bn value to business by 2026. How can AI succeed in your industry? Do you know how to set an ambitious AI vision within your organization? Download your free ebook to find these solutions.
- LiveVideo Courses on AI, Big Data, Machine Learning – only $25 through March 31 - Mar 11, 2019.
All Manning live video courses, includes courses on AI, Big Data, Deep Learning, Machine Learning, Reinforcement Learning, and more - are on sale until March 31 - only twenty five dollars.
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Who is a typical Data Scientist in 2019? - Mar 11, 2019.
We investigate what a typical data scientist looks like and see how this differs from this time last year, looking at skill set, programming languages, industry of employment, country of employment, and more. - The Pareto Principle for Data Scientists - Mar 11, 2019.
In this article, I’ll share a few ways in which we, as data scientists, can use the power of the Pareto Principle to guide our day-to-day activities.
- Automated Machine Learning 101: Is Your Company Ready? - Mar 8, 2019.
In this webinar from DataRobot, learn common automated machine learning use cases how automated machine learning enables more employees to take part in AI initiatives while making existing data science teams more productive, and more!
- Chief Analytics Officers & Influencers, Spring: May 15 – 16 2019, Crowne Plaza San Diego - Mar 8, 2019.
Join to connect with your peers, colleagues, and friends! Network, share and learn from the 60+ expert speakers across 2 immersive days. Register for your free pass!
- Beating the Bookies with Machine Learning - Mar 8, 2019.
We investigate how to use a custom loss function to identify fair odds, including a detailed example using machine learning to bet on the results of a darts match and how this can assist you in beating the bookmaker.
- 19 Inspiring Women in AI, Big Data, Data Science, Machine Learning - Mar 8, 2019.
For the 2019 international women's day, we profile a new set of 19 inspiring women who lead the field in AI, Big Data, Data Science, and Machine Learning fields.
- Designing Ethical Algorithms - Mar 8, 2019.
Ethical algorithm design is becoming a hot topic as machine learning becomes more widespread. But how do you make an algorithm ethical? Here are 5 suggestions to consider.
- Top February Stories: Data Scientists: Why are they so expensive to hire? Artificial Neural Network Implementation using NumPy and Image Classification - Mar 7, 2019.
Also: Gainers, Losers, and Trends in Gartner 2019 Magic Quadrant for Data Science and Machine Learning Platforms; The Essential Data Science Venn Diagram.
- New Data Science Bundle, or Build Your Own Bundle of Skills - Mar 7, 2019.
Good things come in threes, including Online Learning courses. Introducing Data Science & Build Your Own Bundles: bundles include three full courses, allowing data and analytics pros to both broaden and deepen skills across today's hottest topics and save 15% for maximum ROI.
- The history of Predictive Analytics World in 13 bullets - Mar 7, 2019.
Check out these historical Predictive Analytics World milestones, from spawning the Target-predicting-pregnancy publicity debacle, to getting dinged by the Hollywood action movie star Chuck Norris, to growing into the leading international event series it is today.
- KDnuggets offer: Save 20% on Strata in London - Mar 7, 2019.
Strata Data Conference is coming to London Apr 29-May 2. Discover what's coming in data and AI—and how to implement it for your business. Save 20% on Gold, Silver, and Bronze passes with code KDNU until Friday, Mar 15.
- Where Analytics, Data Science, Machine Learning Were Applied: Trends and Analysis - Mar 7, 2019.
CRM/Consumer analytics, health care, banking, finance, and science were the top sectors in 2018. The greatest increases were in mobile apps, investment, security, entertainment, and social policy, while fraud detection, retail, advertising, direct marketing, and social media saw the greatest declines.
- Breaking neural networks with adversarial attacks - Mar 7, 2019.
We develop an intuition behind "adversarial attacks" on deep neural networks, and understand why these attacks are so successful.
- Neural Networks with Numpy for Absolute Beginners — Part 2: Linear Regression - Mar 7, 2019.
In this tutorial, you will learn to implement Linear Regression for prediction using Numpy in detail and also visualize how the algorithm learns epoch by epoch. In addition to this, you will explore two layer Neural Networks.
- Beyond news contents: the role of social context for fake news detection - Mar 7, 2019.
Today we’re looking at a more general fake news problem: detecting fake news that is being spread on a social network. This is a summary of a recent paper which demonstrates why we should also look at the social context: the publishers and the users spreading the information!
- Top KDnuggets tweets, Feb 27 – Mar 05: How to Setup a Python Environment for Machine Learning; How to do Everything in Computer Vision - Mar 6, 2019.
Also Python Data Science for Beginners; Deep Learning for Natural Language Processing (NLP) - using RNNs and CNNs.
- Robust Quality – Powerful Integration of Data Science and Process Engineering. - Mar 6, 2019.
This book provides a strong connection between the concepts in data science and process engineering needed to ensure better quality and takes you through a systematic approach to measure holistic quality with several case studies.
- Get a free SF hotel room night for Strata San Francisco - Mar 6, 2019.
Strata Data Conference is coming to San Francisco Mar 25-28. Register by Friday, Mar 8, with the code FREEROOM, and you'll get a night at the Hilton Union Square on us (and 20% off Gold, Silver, and Bronze passes).
- REV 2: Next Data Science Leaders Summit, NYC, May 23-24 - Mar 6, 2019.
Come to New York City on May 23–24 for Rev 2, and learn from data science teams and leaders. This year’s focus is “What can teams learn from each other?”
- 3 Reasons Why AutoML Won’t Replace Data Scientists Yet - Mar 6, 2019.
We dispel the myth that AutoML is replacing Data Scientists jobs by highlighting three factors in Data Science development that AutoML can’t solve.
- Another 10 Free Must-Read Books for Machine Learning and Data Science - Mar 6, 2019.
Here's a third set of 10 free books for machine learning and data science. Have a look to see if something catches your eye, and don't forget to check the previous installments for reading material while you're here.
- Deconstructing BERT, Part 2: Visualizing the Inner Workings of Attention - Mar 6, 2019.
In this post, the author shows how BERT can mimic a Bag-of-Words model. The visualization tool from Part 1 is extended to probe deeper into the mind of BERT, to expose the neurons that give BERT its shape-shifting superpowers.
- Webinar: Supercharging Search: AI in Information Discovery for the Life Sciences - Mar 5, 2019.
Learn how to cut through the complexity of scientific language; hear how SciBite puts AI techniques in action, and find how to supercharge search at your organization.
- Earn your MS in Business Analytics Online from Drexel University - Mar 5, 2019.
With Drexel University's online MS in Business Analytics program, you'll be able to effectively analyze this data to give your company and yourself a competitive edge. Learn more today!
- Neural Networks seem to follow a puzzlingly simple strategy to classify images - Mar 5, 2019.
We explain why state-of-the-art Deep Neural Networks can still recognize scrambled images perfectly well and how this helps to uncover a puzzlingly simple strategy that DNNs seem to use to classify natural images.
- Neural Networks with Numpy for Absolute Beginners: Introduction - Mar 5, 2019.
In this tutorial, you will get a brief understanding of what Neural Networks are and how they have been developed. In the end, you will gain a brief intuition as to how the network learns.
- GANs Need Some Attention, Too - Mar 5, 2019.
Self-Attention Generative Adversarial Networks (SAGAN; Zhang et al., 2018) are convolutional neural networks that use the self-attention paradigm to capture long-range spatial relationships in existing images to better synthesize new images.
- Top Stories, Feb 25 – Mar 3: Asking Great Questions as a Data Scientist; 4 Reasons Why Your Machine Learning Code is Probably Bad - Mar 5, 2019.
Also: Deconstructing BERT: Distilling 6 Patterns from 100 Million Parameters; How to do Everything in Computer Vision; What no one will tell you about data science job applications; Python Data Science for Beginners
- Interested in Datamining.net domain? - Mar 4, 2019.
Interested in datamining dot net domain? Send me your best offer by March 12.
- PAW Healthcare’s Agenda Highlights, plus 3 other PAWS in Vegas – Save ’til Friday - Mar 4, 2019.
Start planning for PAW Healthcare 2019 in Las Vegas Jun 16-20 and get ready to hear excellent sessions and case studies across healthcare business operations and clinical applications. Save til Friday!
- The Difference Between Data Scientists and Data Engineers - Mar 4, 2019.
ODSC East 2019 has multiple tracks for both Data Scientists and Data Engineers, including workshops, talks, and training sessions. Save 45% with code KDN45.
- Penn State online MS in Data Analytics - Mar 4, 2019.
Teaches students to examine the entire life cycle of analytics problem solving and play a major role in key business decisions.
- On Building Effective Data Science Teams - Mar 4, 2019.
We take a look at the qualities that make a successful data team in order to help business leaders and executives create better AI strategies.
- OpenAI’s GPT-2: the model, the hype, and the controversy - Mar 4, 2019.
OpenAI recently released a very large language model called GPT-2. Controversially, they decided not to release the data or the parameters of their biggest model, citing concerns about potential abuse. Read this researcher's take on the issue.
- Predictive Analytics World – Why It’s the Leading Machine Learning Event in 2019 - Mar 1, 2019.
The founder of PAW gives you an overview of PAW events in 2019 and breaks down what makes PAW the leading machine learning event to attend this year.
- Rapidly Build and Run Apache Spark Applications in the Cloud with StreamAnalytix on AWS Marketplace - Mar 1, 2019.
StreamAnalytix is an Apache Spark based big data analytics and machine learning platform. It offers an intuitive visual development environment to rapidly build and operationalize batch + streaming applications, across industries, data formats, and use cases.
- What no one will tell you about data science job applications - Mar 1, 2019.
For every person who has a question relating to a data science job application, and asks it, there are ten people who have the same question, but don’t ask it. If you’re one of those ten, then this post is for you.
- Comparing MobileNet Models in TensorFlow - Mar 1, 2019.
MobileNets are a family of mobile-first computer vision models for TensorFlow, designed to effectively maximize accuracy while being mindful of the restricted resources for an on-device or embedded application.
- Most impactful AI trends of 2018: The rise of ML Engineering - Mar 1, 2019.
As both research and applied teams are doubling down on their engineering and infrastructure needs, the nascent field of ML Engineering will build upon 2018’s foundation and truly blossom in 2019.
Explaining Random Forest® (with Python Implementation)
R vs Python for Data Visualization
Artificial Neural Networks Optimization using Genetic Algorithm with Python
