In this guide you'll learn how to scope a computer vision project, what kind of source data you need to make it successful, what kind of tools fit your project best, and a whole lot more.
This post is the first in a series whose aim is to shake up our intuitions about what machine learning is making possible in specific sectors — to look beyond the set of use cases that always come to mind.
The idea behind the dplyr package is to do one thing at a time. dplyr has separate functions for every task which make its implementation crisp and easy to understand.
Chris Albon has created and shared a way more cool way to reinforce your machine learning learning (not to be confused with learning reinforcement learning): the flashcard.
Also: Recommendation System Algorithms Overview; The Connection Between #DataScience, #MachineLearning and #AI; The Ultimate Guide to Basic Data Cleaning.
We are looking for strong proposals that have a novel and motivated goal, a broad business impact, a rigid and fair setup, a challenging yet manageable task, and domain accessibility to the general public.
We review JAMA article on “Unintended Consequences of Machine Learning in Medicine” and argue that a number of alarming opinions in this pieces are not supported by evidence.
The term Horn Clause Mining, similar to Rule Based Machine Learning or Inductive Logic Programming, is used to describe the inverse of this functionality. Given a large enough knowledge base, can we infer rules that describe the data accurately?
Improve your skills in every layer of the Data Science stack at ODSC West 2017 and test drive the leading open source tools. Save 60% with code KD60 until Sep 1.
Data science is growing, and will continue to grow for the foreseeable future. Whether you are a student or an expert, here are courses to help further your knowledge of this promising field.
PyTorch is better for rapid prototyping in research, for hobbyists and for small scale projects. TensorFlow is better for large-scale deployments, especially when cross-platform and embedded deployment is a consideration.
Data science educator Jose Portilla provides this definitive guide on becoming a data scientist, which includes everything from resources for acquiring specific skills, to searching for the first job, to mastering the interview.
This is a fast paced, vendor agnostic, technical overview of the Big Data landscape, targeted towards people who want to understand the emerging world of Big Data. Use code KDNUGGETS to save.
While Python did not "swallow" R, in 2017 Python ecosystem overtook R as the leading platform for Analytics, Data Science, and Machine Learning and is pulling users from other platforms.
Also: 37 Reasons why your Neural Network is not working; Machine Learning vs. Statistics: The Texas Death Match of Data Science; Understanding overfitting: an inaccurate meme in Machine Learning; Recommendation System Algorithms: An Overview; The Ultimate Guide to Basic Data Cleaning
In this post, we will try to gain a high-level understanding of how SVMs work. I’ll focus on developing intuition rather than rigor. What that essentially means is we will skip as much of the math as possible and develop a strong intuition of the working principle.
While Deep Learning had many impressive successes, it is only a small part of Machine Learning, which is a small part of AI. We argue that future AI should explore other ways beyond DL.
This post is a collection of 6 separate posts of 7 steps a piece, each for mastering and better understanding a particular data science topic, with topics ranging from data preparation, to machine learning, to SQL databases, to NoSQL and beyond.
Spark Summit will bring together more than 1,200 developers, data scientists, analysts, researchers, and business pros from around the world. Reg by Aug 25 to catch early bird rates and save extra 15% w. code KD824.
AI has moved beyond the Turing Test and is literally “moving” towards new directions. We argue that the new AI grand challenge is to allow intelligence to become more embodied and animal-like.
Most forget that SQL isn’t just about writing queries, which is just the first step down the road. Ensuring that queries are performant or that they fit the context that you’re working in is a whole other thing. This SQL tutorial will provide you with a small peek at some steps that you can go through to evaluate your query.
Data cleaning can seem intimidating, but it’s not hard if you know the basic steps. That’s why we’re excited to announce our newest ebook, “The Ultimate Guide to Basic Data Cleaning”!
Also: No surprise, Gartner ranks #AI as the top technology to watch in coming years; TOP 100 @medium articles on #AI / #MachineLearning / #DeepLearning.
Applying cross-validation prevents overfitting is a popular meme, but is not actually true – it more of an urban legend. We examine what is true and how overfitting is different from overtraining.
Most forget that SQL isn’t just about writing queries, which is just the first step down the road. Ensuring that queries are performant or that they fit the context that you’re working in is a whole other thing. This SQL tutorial will provide you with a small peek at some steps that you can go through to evaluate your query.
Throughout its history, Machine Learning (ML) has coexisted with Statistics uneasily, like an ex-boyfriend accidentally seated with the groom’s family at a wedding reception: both uncertain where to lead the conversation, but painfully aware of the potential for awkwardness.
An episode of Data Podcast, featuring Gregory Piatetsky-Shapiro, discussing KDnuggets, trends in Big Data and Machine Learning, Automation of Data Science, Bias in Algorithms and AI, and more.
Predictive Analytics World for Healthcare in NYC, Oct 29-Nov 2, brings together the leading experts on core analytical and machine learning techniques for healthcare.
Learn how to optimize your Amazon Redshift instance, critical metrics for smart investments in cloud infrastructure, and best practices to scale your AWS investment.
Over the course of many debugging sessions, I’ve compiled my experience along with the best ideas around in this handy list. I hope they would be useful to you.
In this blog, I explore three sets of APIs—RDDs, DataFrames, and Datasets—available in a pre-release preview of Apache Spark 2.0; why and when you should use each set; outline their performance and optimization benefits; and enumerate scenarios when to use DataFrames and Datasets instead of RDDs.
This post presents an overview of the main existing recommendation system algorithms, in order for data scientists to choose the best one according a business’s limitations and requirements.
The answer to questions of trust and bias in AI is largely seen in the focus on Explainable AI. Although traditionally viewed as "black boxes", AI and machine learning systems are not ontologically inscrutable.
Predictive Analytics World for Business New York’s (Oct. 29-Nov. 2) rich program of brand name case studies and industry leaders covers deployed machine learning — across these topics: business, tech, marketing, and case studies.
Global Data Science Conference will include sharing real world experiences, how to create a balanced big data science team, Panel Sessions, Keynote Sessions and workshop. Use code KDNUGGETS to save.
Neural Network algorithms are showing promising results for different complex problems. Here we discuss how these algorithms are used in image compression.
Also: Data Science Primer: Basic Concepts for Beginners; A Guide to Instagramming with Python for Data Analysis; New Poll: Python vs R vs rest; 4 Industries Being Transformed by Machine Learning and Robotics; A Guide to Understanding AI Toolkits
Over 200+ senior analytics executives will attend this largest C-Level, Analytics event in North America, and only 60+ places left. See who you could be meeting at the event.
In any machine learning project, business understanding is very important. But in practice, it does not get enough attention. Here we explain what questions should be asked.
A lot of marketing research is aimed at uncovering why consumers do what they do and not just predicting what they'll do next. Marketing scientist Kevin Gray asks Harvard Professor Tyler VanderWeele about causal analysis, arguably the next frontier in analytics.
This is a collection of introductory posts which present a basic overview of neural networks and deep learning. Start by learning some key terminology and gaining an understanding through some curated resources. Then look at summarized important research in the field before looking at a pair of concise case studies.
Big Squid offers a Predictive Analytics Platform that uses automated Machine Learning to take your Looker investment from real-time data and insights to forward-looking action and impact. Learn more on Aug 24.
This is a fast paced, vendor agnostic, technical overview of the Big Data landscape, targeted towards people who want to understand the emerging world of Big Data. Use code KDNUGGETS to save.
Read "Analyst of the Future" guidebook to discover 3 emerging analyst roles and what they encompass, 4 trends transforming the world of data, and more.
The recent but noticeable shift from CPUs to GPUs is mainly due to the unique benefits they bring to sectors like AdTech, finance, telco, retail, or security/IT . We examine where GPU databases shine.
I won't give you the clichéd line that it's never too late because that's not the point. It is actually because, a term that I loved as soon as I came across it- 'The AI Winter' - doesn't seem to ever be going to return again.
I am writing this article to show you the basics of using Instagram in a programmatic way. You can benefit from this if you want to use it in a data analysis, computer vision, or any other cool project you can think of.
This live webinar (Aug 22) will discuss the impact that the notebook experience has had on data science, and how JupyterLab - the next generation data science IDE - has evolved from the classic notebooks.
Boosted decision trees are responsible for more than half of the winning solutions in machine learning challenges hosted at Kaggle, and require minimal tuning. We evaluate two popular tree boosting software packages: XGBoost and LightGBM and draw 4 important lessons.
Whether you want to start learning deep learning for you career, to have a nice adventure (e.g. with detecting huggable objects) or to get insight into machines before they take over, this post is for you!
This post surveys today’s foremost options for AI in the form of deep learning, examining each toolkit’s primary advantages as well as their respective industry supporters.
Until recently, deep learning alluded to the big names in tech such as Amazon, Facebook, and Google as having a clear use for these tools. Whilst these are some of the key players in AI and DL implementation, there are also huge advantages for their applications in businesses and everyday enterprises.
Every time DeepMind publishes a new paper, there is frenzied media coverage around it. We examine what is and is not real in recent work described as “DeepMind Neural Network Can Make Sense of Objects Around It”.
Python vs R vs Other - What did you use for Analytics, Data Science, Machine Learning work in 2016-17? Vote and we will analyze and report results and trends.
When used in combination with big data and machine learning, both AI and robotics can actively improve over time as they collect more information. You don’t have to look far to see how these technologies have revolutionized the world, and continue to do so.
This post introduces five perfectly valid ways of measuring distances between data points. We will also perform simple demonstration and comparison with Python and the SciPy library.
Deep learning expands boundaries of the possible. Detecting fraud. Predicting claims. Diagnosing cancer. Deep learning solves these problems and many others. Find out more with Cloudera, Aug 24.
Global Big Data Conference, a leading vendor agnostic conference for the Big Data community, will hold 5th conference in Santa Clara. Use code KDnuggets to save.
DevOps and DVC tools can help reduce time data scientists spend on mundane data preparation and achieve their dream of focusing on cool machine learning algorithms and interesting data analysis.
Also: How I Used Deep Learning To Train A Chatbot To Talk Like Me; Making Predictive Models Robust: Holdout vs Cross-Validation; How Convolutional Neural Networks Accomplish Image Recognition?; Top Influencers for Data Science
Learn how two data scientists quickly transformed from mere mortals into data science superheroes, now able to tackle more projects with better results - faster than a speeding bullet!
The validation step helps you find the best parameters for your predictive model and prevent overfitting. We examine pros and cons of two popular validation strategies: the hold-out strategy and k-fold.
This blog introduces the basics of reinforcement learning. We are going to see how reinforcement learning might help us to address these challenges; to work smarter at the edge when brute force technology advances will not suffice.
This collection of concise introductory data science tutorials cover topics including the difference between data mining and statistics, supervised vs. unsupervised learning, and the types of patterns we can mine from data.
I have seen situations where AI (or at least machine learning) had an incredible impact on a business—I also have seen situations where this was not the case. So, what was the difference?
While earlier entrants in this series covered elementary classification algorithms, another (more advanced) machine learning algorithm which can be used for classification is Support Vector Machines (SVM).
Why can't you guys comment your f*cking code?; Train Chrome's Trex character to play independently; How to make a racist AI without really trying; Is training a NN to mimic a closed-source library legal?; 37 Reasons why your NN is not working
Also: What is the most important step in a #MachineLearning project? #MachineLearning Algorithms: a concise technical overview; McKinsey state of #MachineLearning and #AI.
PAW Financial is the leading cross-vendor event covering the deployment of machine learning and predictive analytics for financial services. Register Now!
Until recently, deep learning alluded to the big names in tech such as Amazon, Facebook, and Google as having a clear use for these tools. Whilst these are some of the key players in AI and DL implementation, there are also huge advantages for their applications in businesses and everyday enterprises.
Image recognition is very interesting and challenging field of study. Here we explain concepts, applications and techniques of image recognition using Convolutional Neural Networks.
In this post, a Google Analytics & Google AdWords expert shares his tips and tools of intelligent Google Analytics auditing. Read on for some practical insight.
This post outlines the approach taken at a recent deep learning hackathon, hosted by YCombinator-backed startup DeepGram. The dataset: EEG readings from a Stanford research project that predicted which category of images their test subjects were viewing using linear discriminant analysis.
Join IAPA on 18 October in Melbourne to hear international experts share insights behind machine learning, data science and analytics; listen to local experts explain how they’ve used data to change their business. Get super early bird rates or become IAPA member and save even more.
Deep learning makes it possible to convert unstructured text to computable formats, incorporating semantic knowledge to train machine learning models. These digital data troves help us understand people on a new level.
Though it doesn’t get a lot of buzz, sampling is fundamental to any field of science. Marketing scientist Kevin Gray asks Dr. Stas Kolenikov, Senior Scientist at Abt Associates, what marketing researchers and data scientists most need to know about it.
In this post, we’ll be looking at how we can use a deep learning model to train a chatbot on my past social media conversations in hope of getting the chatbot to respond to messages the way that I would.
Find out how to utilize Big Data to make informed, data driven decisions and stay one-step ahead of the competition. Classes start Aug 21 - signup and get a lifetime access to analytics experts.
Download Best Practices Report: Data Science and Big Data - Enterprise Paths to Success, where our research team takes a look at experiences with and plans for big data and data science.
Apache Arrow is a de-facto standard for columnar in-memory analytics. In the coming years we can expect all the big data platforms adopting Apache Arrow as its columnar in-memory layer.
Also: Beautiful Python Visualizations: An Interview with Bokeh Core Developer; How will Big Data companies monetize data in 2018?; What is hardcore data science – in practice?; DeepSense: Time-series mobile sensing data processing; The Machine Learning Abstracts: Decision Trees
Back-to-school sale on best courses from Udemy, including Data Science, Machine Learning, Python, Spark, Tableau, and Hadoop - only $10 or $12 until Aug 10, 2017.
Get the free kit, which includes webcast with text analytics expert on how he helps clients make sense of text data, book chapter on text mining, and more.
Springboard is a leading provider of data science training. Apply to the Data Science Career Track, the first online bootcamp to guarantee you a job in data science or your money back.
EDSF provides a conceptual basis for the Data Science Profession definition, targeted education and training, professional certification, organizational and individual skills management and career transferability.
AirBnB has 2 million listings and operates in 65,000 cities. Here we look at insights related to vacation rental space in the sharing economy using the property listings data for Texas, US.
These short and to-the-point tutorials may provide the assistance you are looking for. Each of these posts concisely covers a single, specific machine learning concept.
Cutting-edge science and new business fundamentals intersect and merge at Strata Data Conference. Win KDnuggets Pass - submit your entry by Aug 17, 2017.
We explain another novel method for much faster training of Deep Learning models by freezing the intermediate layers, and show that it has little or no effect on accuracy.
Decision trees are a classic machine learning technique. The basic intuition behind a decision tree is to map out all possible decision paths in the form of a tree.
AI and Analytics driven solutions have been widely adopted across different industries for various purposes. However, only a handful of banks around the world are working with advanced analytics and artificial intelligence technologies to improve their risk and compliance activities.
Learn how to deploy your Data Science work in production, both in batch and real-time environments, where people and programs can use them simply and confidently.
Coming soon: KDD-2017 Halifax, JupyterCon NYC, Big Data Innovation Summit Boston, O'Reilly AI NYC, Strata NYC, Rework Deep Learning London, and many more.
Compared to the state-of-art, DeepSense provides an estimator with far smaller tracking error on the car tracking problem, and outperforms state-of-the-art algorithms on the HHAR and biometric user identification tasks by a large margin.
Data Science expert Mikio Braun on the anatomy of an architecture to bring data science into production. Learn more at his talk at Strata NYC - Use code KDNU for additional 20% off (best price ends Aug 11).
In today’s data driven economy, Data is a strategic asset to a company and data monetization is prime focus of many companies. Let’s see how data monetization will be achieved in 2018.
Toolkits for standard neural network visualizations exist, along with tools for monitoring the training process, but are often tied to the deep learning framework. Could a general, easy-to-setup tool for generating standard visualizations provide a sanity check on the learning process?
Read this insightful interview with Bokeh's core developer, Bryan Van de Ven, and gain an understanding of what Bokeh is, when and why you should use it, and what makes Bryan a great fit for helming this project.
Top 10 Machine Learning Use Cases: Part 1
Data Scientists: What They Do and How to Become One
Python overtakes R, becomes the leader in Data Science, Machine Learning platforms
Deep Learning is not the AI future
42 Steps to Mastering Data Science
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