All (115) | Courses, Education (9) | Meetings (18) | News, Features (8) | Opinions, Interviews (30) | Top Stories, Tweets (10) | Tutorials, Overviews (33) | Webcasts & Webinars (7)
- The AI Conference returns to San Francisco, Sept 4–7, 2018 - May 31, 2018.
The AI Conference is coming to San Francisco Sept 4-7. It sold out last year, so make your plans soon. KDnuggets fans receive a 20% discount when you register (for most passes) using the code KDN20.
- Make Your Models a Competitive Advantage - May 31, 2018.
The Model Management white paper, based on our experience working with hundreds of model-driven organizations, describes the reasons most organizations have not yet unlocked the transformative potential of models and provides a framework for success.
- Overview of Dash Python Framework from Plotly for building dashboards - May 31, 2018.
Introduction to Dash framework from Plotly, reactive framework for building dashboards in Python. Tech talk covers basics and more advanced topics like custom component and scaling.
- On the contribution of neural networks and word embeddings in Natural Language Processing - May 31, 2018.
In this post I will try to explain, in a very simplified way, how to apply neural networks and integrate word embeddings in text-based applications, and some of the main implicit benefits of using neural networks and word embeddings in NLP.
- Top KDnuggets tweets, May 23-29: 10 More Free Must-Read Books for #MachineLearning and #DataScience - May 30, 2018.
Also: What is the Difference Between Deep Learning and “Regular” Machine Learning?; Top 20 R Libraries for Data Science in 2018; Understanding LSTM and its diagrams #NLProc
- Virtual Training Events Without Leaving Your Desk - May 30, 2018.
Check out our lineup of upcoming virtual seminars, online learning courses, and customized training in your office. Space is limited, so reserve your seat early and score the best savings!
- Interview: How Seagate Technology Makes Great Use of Deep Learning – Last Call to Register for Deep Learning World - May 30, 2018.
In anticipation of his upcoming conference co-presentation at Deep Learning World in Las Vegas, June 3-7, we asked Abbas Chokor, Staff Data Scientist at Seagate Technology, a few questions about his work in deep learning.
- Cartoon: GDPR first effect on Privacy - May 30, 2018.
New KDnuggets Cartoon examines the first unexpected effect of GDPR on Privacy.
- Introduction to Content Personalization - May 30, 2018.
The basics of user experience and content personalization. The way to target your audience more precisely and effectively.
- Improving the Performance of a Neural Network - May 30, 2018.
There are many techniques available that could help us achieve that. Follow along to get to know them and to build your own accurate neural network.
- Deep Learning Summit, Toronto featuring Geoff Hinton – save with KDnuggets - May 29, 2018.
Geoffrey Hinton, one of the fathers of Deep Learning, will be back to share his most recent and cutting-edge research progressions, and will be joined by other top researchers. Save 20% on Early Bird passes when you sign up before 15 June w. code KDNUGGETS. Also check Women in AI dinner series and get new white paper on Ethical implications of AI.
- AI Leaders Summit, Boston, June 21-22: Meet 100 AI innovators from top firms - May 29, 2018.
Discover game-changing data strategies, AI developments and how to lead their implementation. Save with code KDN200
- PAW Interview: Spectrum Cable Lays Fiber with Predictive Analytics – Last Call to Register - May 29, 2018.
In anticipation of his upcoming conference presentation at Predictive Analytics World for Business Las Vegas, June 3-7, we asked Nishant Sharma, Director, Predictive Analytics at Charter Communications, a few questions about his work in predictive analytics.
- 6 Tips for Effective Visualization with Tableau - May 29, 2018.
We analyse principles for effective data visualization in Tableau, including color gradients, avoiding crowded dashboards, Tableau marks and more.
- Descriptive analytics, machine learning, and deep learning viewed via the lens of CRISP-DM - May 29, 2018.
CRISP-DM methodology is a must teach to explain analytics project steps. This article purpose it to complement it with specific chart flow that explain as simply as possible how it is more likely used in descriptive analytics, classic machine learning or deep learning.
- A Beginner’s Guide to the Data Science Pipeline - May 29, 2018.
On one end was a pipe with an entrance and at the other end an exit. The pipe was also labeled with five distinct letters: "O.S.E.M.N."
- 10 More Free Must-Read Books for Machine Learning and Data Science - May 28, 2018.
Summer, summer, summertime. Time to sit back and unwind. Or get your hands on some free machine learning and data science books and get your learn on. Check out this selection to get you started.
- NLP in Online Courses: an Overview - May 28, 2018.
This article examines several Natural Language Processing (NLP) courses across a variety of online sources and programming languages.
- Event Processing: Three Important Open Problems - May 28, 2018.
This article summarizes the three most important problems to be solved in event processing. The facts in this article are supported by a recent survey and an analysis conducted on the industry trends.
- Top Stories, May 21-27: Python eats away at R: Top Software for Analytics & Data Science; ETL vs ELT: Considering the Advancement of Data Warehouses - May 28, 2018.
Also: Top 20 R Libraries for Data Science in 2018; Frameworks for Approaching the Machine Learning Process; Machine Learning Breaking Bad – addressing Bias and Fairness in ML models
- Learn AI and Data Science rapidly based only on high school math – KDnuggets Offer - May 25, 2018.
This 3-month program, created by Ajit Jaokar, who teaches at Oxford, is interactive and delivered by video. Coding examples are in Python. Places limited - check special KDnuggets rate.
- Machine Learning Breaking Bad – addressing Bias and Fairness in ML models - May 25, 2018.
As the use of analytics proliferate, companies will need to be able to identify models that are breaking bad.
- Top 20 R Libraries for Data Science in 2018 - May 25, 2018.
We have prepared an infographic of Top 20 R packages for data science, which covers the libraries main features and GitHub activities, as all of the libraries are open-source.
- Modelling Time Series Processes using GARCH - May 25, 2018.
To go into the turbulent seas of volatile data and analyze it in a time changing setting, ARCH models were developed.
- Top-notch experts from Uber, PwC, HP & many more – Predictive Analytics World for Industry 4.0 - May 24, 2018.
Business users, decision-makers, and experts in predictive analytics will meet on 12-13 June 2018 in Munich to discover and discuss the latest trends and technologies in machine & deep learning for the era of Internet of Things and artificial intelligence.
- How to tackle common data cleaning issues in R - May 24, 2018.
R is a great choice for manipulating, cleaning, summarizing, producing probability statistics, and so on. In addition, it's not going away anytime soon, it is platform independent, so what you create will run almost anywhere, and it has awesome help resources.
- How Not to Regulate the Data Economy - May 24, 2018.
The GDPR will affect not just tech companies but any company that handles customer data — in other words, every company. And it will affect the use of data throughout the world, not just in Europe...
- Data Science: 4 Reasons Why Most Are Failing to Deliver - May 24, 2018.
Data Science: Some see billions in returns, but most are failing to deliver. This article explores some of the reasons why this is the case.
- Frequentists Fight Back - May 24, 2018.
Frequentist methods are sometimes described as “classical”, though most have only appeared in recent decades and new ones are under development as you read this. Whatever we call it, this branch of statistics is very much alive.
- Top KDnuggets tweets, May 16-22: Python eats away at R; Data Science Plan 2018 - May 23, 2018.
Also: AI is learning to see in the dark; Introducing state of the art text classification with universal language models; Top 100 Books for Data Scientists.
- Scientific debt – what does it mean for Data Science? - May 23, 2018.
This article analyses scientific debt - what it is and what it means for data science.
- Deep Learning With Apache Spark: Part 2 - May 23, 2018.
In this article I’ll continue the discussion on Deep Learning with Apache Spark. I will focus entirely on the DL pipelines library and how to use it from scratch.
- Mastering Advanced Analytics with Apache Spark - May 22, 2018.
Get ebook with a collection of the most popular technical blog posts that introduce you to machine learning on Apache Spark, and highlight many of the major developments around Spark MLlib and GraphX.
- Why Data and Infrastructure are key to determining Customer Intent,
May 31 Webinar - May 22, 2018.Join Yieldmo, an advertising technology company and learn how Snowflake and Looker unleashed the potential of their mobile ad engagement data and drove more impactful marketing for their clients.
- Python eats away at R: Top Software for Analytics, Data Science, Machine Learning in 2018: Trends and Analysis - May 22, 2018.
Python continues to eat away at R, RapidMiner gains, SQL is steady, Tensorflow advances pulling along Keras, Hadoop drops, Data Science platforms consolidate, and more.
- If chatbots are to succeed, they need this - May 22, 2018.
Can logic be used to make chatbots intelligent? In the 1960s this was taken for granted. Now we have all but forgotten the logical approach. Is it time for a revival?
- Generative Adversarial Neural Networks: Infinite Monkeys and The Great British Bake Off - May 22, 2018.
Adversarial Neural Networks are oddly named since they actually cooperate to make things.
- ETL vs ELT: Considering the Advancement of Data Warehouses - May 22, 2018.
The traditional concept of ETL is changing towards ELT – when you’re running transformations right in the data warehouse. Let’s see why it’s happening, what it means to have ETL vs ELT, and what we can expect in the future.
- Learn By Doing: Hands-on Labs and Data Science Bootcamp - May 21, 2018.
Familiarize yourself with the tools, technologies, and techniques that help you derive value from data at TDWI Anaheim, Aug 5-10, with TDWI’s Hands-on Lab Series and the Data Science Bootcamp. Save up to $915 with code KD20.
- Customer-Driven Product Development for the Future, June 6 webinar - May 21, 2018.
Learn about the tools and strategies you need to develop products to meet the future needs of your customer and thrive in the future of insurance.
- 6 Proven Steps to Land a Job in Data Science - May 21, 2018.
What are the critical steps to get a job in data science? We share the proven formula that helped many data enthusiasts secure job offers as data scientist/analyst, data engineer and machine learning engineer.
- Top Stories, May 14-20: Data Science vs Machine Learning vs Data Analytics vs Business Analytics; Implement a YOLO Object Detector from Scratch in PyTorch - May 21, 2018.
Also: An Introduction to Deep Learning for Tabular Data; 9 Must-have skills you need to become a Data Scientist, updated; GANs in TensorFlow from the Command Line: Creating Your First GitHub Project; Complete Guide to Build ConvNet HTTP-Based Application
- Frameworks for Approaching the Machine Learning Process - May 21, 2018.
This post is a summary of 2 distinct frameworks for approaching machine learning tasks, followed by a distilled third. Do they differ considerably (or at all) from each other, or from other such processes available?
- Pursue a Stanford Data Science Certificate - May 18, 2018.
With our online graduate courses and certificates, you can earn a higher education credential from Stanford while still maintaining your career.
- Kernel Machine Learning (KernelML) - Generalized Machine Learning Algorithm - May 18, 2018.
This article introduces a pip Python package called KernelML, created to give analysts and data scientists a generalized machine learning algorithm for complex loss functions and non-linear coefficients.
- YouTube videos on database management, SQL, Datawarehousing, Business Intelligence, OLAP, Big Data, NoSQL databases, data quality, data governance and Analytics – free - May 18, 2018.
Watch over 20 hours of YouTube videos on databases and database design, Physical Data Storage, Transaction Management and Database Access, and Data Warehousing, Data Governance and (Big) Data Analytics - all free.
- Optimization Using R - May 18, 2018.
Optimization is a technique for finding out the best possible solution for a given problem for all the possible solutions. Optimization uses a rigorous mathematical model to find out the most efficient solution to the given problem.
- Find the Right Accelerator for Your Deep Learning Needs - May 17, 2018.
Download the report Find the Right Accelerator for your Deep Learning Needs to learn how I&O leaders must deliver effective machine learning infrastructures that effectively balance performance, cost, and functionality while minimizing complexity.
- 9 Must-have skills you need to become a Data Scientist, updated - May 17, 2018.
Check out this collection of 9 (plus some additional freebies) must-have skills for becoming a data scientist.
- How to build analytic products in an age of data privacy - May 17, 2018.
Privacy-preserving analytics is not only possible, but with GDPR about to come online, it will become necessary to incorporate privacy in your data products.
- An Introduction to Deep Learning for Tabular Data - May 17, 2018.
This post will discuss a technique that many people don’t even realize is possible: the use of deep learning for tabular data, and in particular, the creation of embeddings for categorical variables.
- How to Implement a YOLO (v3) Object Detector from Scratch in PyTorch: Part 1 - May 17, 2018.
The best way to go about learning object detection is to implement the algorithms by yourself, from scratch. This is exactly what we'll do in this tutorial.
- Modern Data Science Workflows, May 24 - May 16, 2018.
Learn how data scientists across have influenced and changed analysis behaviors across their companies, and get helpful tips for integrating data science findings into your organization decision making process.
- Top KDnuggets tweets, May 09-15: Top 100 Books for #DataScientists; 7 Books to Grasp Mathematical Foundations of #DataScience - May 16, 2018.
Also: 5 Reasons “Logistic Regression” should be the first thing you learn when becoming a Data Scientist; WTF is a Tensor?!?; 10 Free Must-Read Books for #MachineLearning and #DataScience; Annual KDnuggets Software Poll
- Learn Business Analytics at Clark University – affordable excellence - May 16, 2018.
Move your career forward in one of the fields with the largest demand. Business Analytics at Clark University will give you the skills employers demand by teaching you how to synthesize data into powerful information.
- AI Leaders Summit, Boston, June 21-22, KDnuggets Offer - May 16, 2018.
Join the AI research and industry leaders from top companies including eBay, Microsoft, GE, Intel, Facebook, Uber and learn about the latest AI topics. Use code KD20 to save.
- How to Organize Data Labeling for Machine Learning: Approaches and Tools - May 16, 2018.
The main challenge for a data science team is to decide who will be responsible for labeling, estimate how much time it will take, and what tools are better to use.
- GANs in TensorFlow from the Command Line: Creating Your First GitHub Project - May 16, 2018.
In this article I will present the steps to create your first GitHub Project. I will use as an example Generative Adversarial Networks.
- THE BOOK OF WHY: The New Science of Cause and Effect - May 15, 2018.
A Turing Prize-winning computer scientist and statistician shows how understanding causality has revolutionized science and will revolutionize AI.
- Get Hands-On with Deep Learning – New Workshop at Mega-PAW Vegas, June 3-7 - May 15, 2018.
A new full day training workshop has been announced for Predictive Analytics World's Mega-PAW in Las Vegas, Jun 4: Deep Learning in Practice: A Hands-On Introduction. Mega-PAW is Jun 3-7. Register now!
- Beyond Data Lakes and Data Warehousing - May 15, 2018.
We give a comprehensive review of data lakes and data warehouses, and look at what the future holds for total data integration.
- Complete Guide to Build ConvNet HTTP-Based Application using TensorFlow and Flask RESTful Python API - May 15, 2018.
In this tutorial, a CNN is to be built, and trained and tested against the CIFAR10 dataset. To make the model remotely accessible, a Flask Web application is created using Python to receive an uploaded image and return its classification label using HTTP.
- A Brief Introduction to Wikidata - May 15, 2018.
Like Wikipedia, there are all kinds of data stored in Wikidata. As such, when you are looking for a specific dataset or if you want to answer a curious question, it can be a good start looking for that data at Wikidata first.
- DSTI: Applied MSc in Data Engineering, Advanced MSc in AI – Learn in France - May 14, 2018.
DSTI launches 2 new programmes for October 2018 entry: Applied MSc in Data Engineering and Advanced MSc in AI - Paris, Nice, and online.
- PAW for Industry 4.0 – Take a Look at the Agenda - May 14, 2018.
The agenda for Predictive Analytics World for Industry 4.0 is here! See the heavy-weights from companies like PWC, Uber, Siemens, Airbus and many more who will gather in Munich on 12-13 Jun for insightful sessions on the latest in industry trends and achievements.
- Data Engineer vs Data Scientist: the evolution of aggressive species - May 14, 2018.
This article looks at how the two "species" - data scientists and data engineers - harmonise and coexist.
- Top Stories, May 7-13: 2018 KDnuggets Analytics, Data Mining, Data Science, Machine Learning Software Poll; WTF is a Tensor?!? - May 14, 2018.
5 Reasons "Logistic Regression" should be the first thing you learn when becoming a Data Scientist; PyTorch Tensor Basics; Top 7 Data Science Use Cases in Finance; Detecting Breast Cancer with Deep Learning; To SQL or not To SQL: that is the question!
- Simple Derivatives with PyTorch - May 14, 2018.
PyTorch includes an automatic differentiation package, autograd, which does the heavy lifting for finding derivatives. This post explores simple derivatives using autograd, outside of neural networks.
- Spark + AI Summit, Top Speakers – Andreessen, Karpathy, Zaharia and more – KDnuggets Offer - May 11, 2018.
Join 4,000 of the top developers, data scientists, and business executives who will be tuning into the sessions and training at this year's Spark+AI Summit. Use code KDnuggets to save 30% when you register by May 18.
- Top SAS Courses Online - May 11, 2018.
High quality SAS training for beginners is out there and I’ll help you find it.
- PyTorch Tensor Basics - May 11, 2018.
This is an introduction to PyTorch's Tensor class, which is reasonably analogous to Numpy's ndarray, and which forms the basis for building neural networks in PyTorch.
- Unleash a faster Python on Your Data. - May 10, 2018.
Get real performance results and download the free Intel(r) Distribution for Python that includes everything you need for blazing-fast computing, analytics, machine learning, and more.
- Is a Single Version of Truth Possible?
May 16 Webinar - May 10, 2018.The data warehouse promised to deliver a single version of truth. But skeptics abound, saying a single version of truth is a mirage and not necessary. Join this webinar and learn from experts debating this question.
- The Executive Guide to Data Science and Machine Learning - May 10, 2018.
This article provides a short introductory guide for executives curious about data science or commonly used terms they may encounter when working with their data team. It may also be of interest to other business professionals who are collaborating with data teams or trying to learn data science within their unit.
- Deep learning scaling is predictable, empirically - May 10, 2018.
This study starts with a simple question: “how can we improve the state of the art in deep learning?”
- Top 7 Data Science Use Cases in Finance - May 10, 2018.
We have prepared a list of data science use cases that have the highest impact on the finance sector. They cover very diverse business aspects from data management to trading strategies, but the common thing for them is the huge prospects to enhance financial solutions.
- Top KDnuggets tweets, May 02-08: Boost your data science skills. Learn linear algebra. - May 9, 2018.
Also: #ApacheSpark: #Python vs. #Scala pros and cons for #DataScience; Loc2Vec: Learning location embeddings with triplet-loss networks; Skewness vs Kurtosis - The Robust Duo.
- What’s Hot in Machine Learning? Just Ask PAW Founder Eric Siegel - May 9, 2018.
What will 2018's key trends for machine learning be? Read what Predictive Analytics World Founder Eric Siegel has to say on the subject. And don't forget to register for Mega-PAW in Las Vegas, Jun 3-7!
- Data Augmentation: How to use Deep Learning when you have Limited Data - May 9, 2018.
This article is a comprehensive review of Data Augmentation techniques for Deep Learning, specific to images.
- Detecting Breast Cancer with Deep Learning - May 9, 2018.
Breast cancer is the most common invasive cancer in women, and the second main cause of cancer death in women, after lung cancer. In this article I will build a WideResNet based neural network to categorize slide images into two classes, one that contains breast cancer and other that doesn’t using Deep Learning Studio.
- Las Vegas Data Innovation Festival, July 17-18 - May 8, 2018.
Why should be in Vegas? Network with other professionals, learn at 50+ technical sessions, talk to speakers and top experts, and enjoy the city!
- Can’t-Miss Keynotes at Deep Learning World – June 3-7 in Vegas - May 8, 2018.
Don't miss the opportunity to witness keynote sessions by industry heavyweights at the upcoming inaugural Deep Learning World conference in Las Vegas, Jun 3-7.
- Top April Stories: Why so many data scientists are leaving their jobs? 7 Books to Grasp Math Foundations of Data Science and Machine Learning - May 8, 2018.
Also: Key Algorithms and Statistical Models for Aspiring Data Scientists; Top 20 Deep Learning Papers, 2018 Edition.
- Predictive Analytics World for Industry 4.0 is Back – Munich, 12-13 June - May 8, 2018.
Predictive Analytics World for Industry 4.0, the leading vendor-independent conference for applied predictive analytics is back, in Munich, 12-13 Jun. Discover and discuss the latest trends and technologies in machine & deep learning for the era of Internet of Things and artificial intelligence.
- Torus for Docker-First Data Science - May 8, 2018.
To help data science teams adopt Docker and apply DevOps best practices to streamline machine learning delivery pipelines, we open-sourced a toolkit based on the popular cookiecutter project structure.
- 7 Useful Suggestions from Andrew Ng “Machine Learning Yearning” - May 8, 2018.
Machine Learning Yearning is a book by AI and Deep Learning guru Andrew Ng, focusing on how to make machine learning algorithms work and how to structure machine learning projects. Here we present 7 very useful suggestions from the book.
- How I Used CNNs and Tensorflow and Lost a Silver Medal in Kaggle Challenge - May 8, 2018.
I joined the competition a month before it ended, eager to explore how to use Deep Natural Language Processing (NLP) techniques for this problem. Then came the deception. And I will tell you how I lost my silver medal in that competition.
- Top Data Science, Machine Learning Courses from Udemy – May 2018 - May 8, 2018.
Learn Machine Learning, Data Science, Python, Azure Machine Learning, and more with Udemy Mother's Day $9.99 sale - get top courses from leading instructors.
- 5 Reasons Logistic Regression should be the first thing you learn when becoming a Data Scientist - May 8, 2018.
Learn Logistic Regression first to become familiar with the pipeline and not being overwhelmed with fancy algorithms.
- Best Practices for Scaling Data Science Across the Organization - May 7, 2018.
Join Forrester and Anaconda for a webinar on Thursday, May 17, at 2:00 PM CT, to learn best practices for scaling data science across your entire organization. Learn how to tackle five key challenges facing organizations today!
- To SQL or not To SQL: that is the question! - May 7, 2018.
This article looks at the emergence of the NoSQL movement and compares it to a traditional relational database.
- Top Stories, Apr 30 – May 6: Boost your data science skills. Learn linear algebra.; Operational Machine Learning: Seven Considerations for Successful MLOps - May 7, 2018.
Also: 50+ Useful Machine Learning & Prediction APIs, 2018 Edition; 8 Useful Advices for Aspiring Data Scientists; Apache Spark : Python vs. Scala; 8 Useful Advices for Aspiring Data Scientists; Data Science vs Machine Learning vs Data Analytics vs Business Analytics
- 2018 KDnuggets Poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months? - May 7, 2018.
Vote in KDnuggets 19th Annual Poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months?
- Disneyland Meets Data – Join TDWI this August - May 4, 2018.
Check out the 55+ full and half-day courses in four core learning tracks plus five accelerated learning fast tracks, Aug 5-10 at TDWI Anaheim, and buckle up for a week of in-depth training in sunny SoCal! Save up to $915 with priority code KD20 before Jun 15.
- Apache Spark : Python vs. Scala - May 4, 2018.
When it comes to using the Apache Spark framework, the data science community is divided in two camps; one which prefers Scala whereas the other preferring Python. This article compares the two, listing their pros and cons.
- Skewness vs Kurtosis – The Robust Duo - May 4, 2018.
Kurtosis and Skewness are very close relatives of the “data normalized statistical moment” family – Kurtosis being the fourth and Skewness the third moment, and yet they are often used to detect very different phenomena in data. At the same time, it is typically recommendable to analyse the outputs of both together to gather more insight and understand the nature of the data better.
- 8 Useful Advices for Aspiring Data Scientists - May 4, 2018.
I recently read Sebastian Gutierrez’s “Data Scientists at Work”, in which he interviewed 16 data scientists. I want to share the best answers that these data scientists gave for the question: "What advice would you give to someone starting out in data science?"
- JupyterCon – Exclusive KDnuggets Offer - May 3, 2018.
JupyterCon returns to New York August 21-24. Save an additional 20% on individual Gold, Silver and Bronze passes with the code KDN20 before May 18.
- Deep Conversations: Lisha Li, Principal at Amplify Partners - May 3, 2018.
Mathematician Lisha Li expounds on how she thrives as a Venture Capitalist at Amplify Partners to identify, invest and nurture the right startups in Machine Learning and Distributed Systems.
- AI is not set and forget - May 3, 2018.
Just like a car, AI-based system can tick along in decent shape for a while. But neglect it too long and you’re in trouble. Unfortunately, failing to maintain your AI will destroy the project.
Studies have shown that only 1% or less of total users click on privacy policies, and those that do rarely actually read them. The GDPR requires clear succinct explanations and explicit consent, but that’s not the situation on the ground right now, and it’s hard to see that changing overnight on May 25th.
- Boost your data science skills. Learn linear algebra. - May 3, 2018.
The aim of these notebooks is to help beginners/advanced beginners to grasp linear algebra concepts underlying deep learning and machine learning. Acquiring these skills can boost your ability to understand and apply various data science algorithms.
- Top KDnuggets tweets, Apr 25 – May 1: Detecting Sarcasm with Deep Convolutional #NeuralNetworks - May 2, 2018.
Also: Building Convolutional #NeuralNetwork using NumPy from Scratch; Top 16 Open Source #DeepLearning Libraries and Platforms; #Python Regular Expressions Cheat Sheet
- Best Practices in Data Visualization - May 2, 2018.
Do your data visualizations need a reboot? Though data visualizations may be designed to facilitate understanding, not all graphs are effective. In this webcast, viewers will learn how to use best practices to give a graph a makeover.
- Hands-on: Intro to Python for Data Analysis - May 2, 2018.
Learn one of the top languages used in data science and machine learning with this new hands-on course by TDWI Online Learning.
- Upcoming Meetings in AI, Analytics, Big Data, Data Science, Deep Learning, Machine Learning: May and Beyond - May 2, 2018.
Coming soon: Train AI San Francisco, Deep Learning Boston, Mega-PAW Las Vegas, Spark + AI Summit San Francisco, PAKDD Melbourne, CogX London, and more.
- To Kaggle Or Not - May 2, 2018.
Kaggle is the most well known competition platform for predictive modeling and analytics. This article looks into the different aspects of Kaggle and the benefits it can bring to data scientists.
- Getting Started with spaCy for Natural Language Processing - May 2, 2018.
spaCy is a Python natural language processing library specifically designed with the goal of being a useful library for implementing production-ready systems. It is particularly fast and intuitive, making it a top contender for NLP tasks.
- GraphDB for DevOps, Semantic Technology Proof-of-Concept – Online Training - May 1, 2018.
Advance your career and business with live online training: GraphDB for DevOps, Designing Semantic Technology Proof-of-Concept - special KDnuggets Offers.
- Actionable Insights with Predictive Analytics for Marketers, May 9 - May 1, 2018.
Learn how your predictions can only be as good as your data, how to fix imperfect data, how to structure your customer data for optimal predictive power, and more.
- 50+ Useful Machine Learning & Prediction APIs, 2018 Edition - May 1, 2018.
Extensive list of 50+ APIs in Face and Image Recognition ,Text Analysis, NLP, Sentiment Analysis, Language Translation, Machine Learning and prediction.
- Data Science vs Machine Learning vs Data Analytics vs Business Analytics - May 1, 2018.
This article gives a broad overview of data science and the various fields within it, including business analytics, data analytics, business intelligence, advanced analytics, machine learning, and AI.
- Jupyter Notebook for Beginners: A Tutorial - May 1, 2018.
The Jupyter Notebook is an incredibly powerful tool for interactively developing and presenting data science projects. Although it is possible to use many different programming languages within Jupyter Notebooks, this article will focus on Python as it is the most common use case.
- Implementing Deep Learning Methods and Feature Engineering for Text Data: FastText - May 1, 2018.
Overall, FastText is a framework for learning word representations and also performing robust, fast and accurate text classification. The framework is open-sourced by Facebook on GitHub.