Search results for deep learning

    Found 4989 documents, 12960 searched:

  • The Economics and Benefits of Artificial Intelligence

    In this article, focus on current AI, which is mostly based on the algorithms that can do predictions, and discuss how the economics of AI works and how it may affect business.

  • Platinum BlogEssential Math for Data Science:  ‘Why’ and ‘How’">SilverPlatinum BlogEssential Math for Data Science:  ‘Why’ and ‘How’

    It always pays to know the machinery under the hood (even at a high level) than being just the guy behind the wheel with no knowledge about the car.

  • JPMorgan: Data Scientist – Treasury data+design team [New York, NY or Tampa, FL]

    JPMorgan is seeking a Data Scientist – Treasury data+design team in New York, NY or Tampa, FL, to perform at the leading edge inside the company driving change.

  • Comparison of the Most Useful Text Processing APIs">Silver BlogComparison of the Most Useful Text Processing APIs

    There is a need to compare different APIs to understand key pros and cons they have and when it is better to use one API instead of the other. Let us proceed with the comparison.

  • Top KDnuggets tweets, Aug 15-21: How to Set Up a Free Data Science Environment on Google Cloud

    Also: Unveiling Mathematics Behind XGBoost; Causation in a Nutshell; Introduction to Fraud Detection Systems.

  • Basic Statistics in Python: Probability

    At the most basic level, probability seeks to answer the question, "What is the chance of an event happening?" To calculate the chance of an event happening, we also need to consider all the other events that can occur.

  • Interpreting a data set, beginning to end

    Detailed knowledge of your data is key to understanding it! We review several important methods that to understand the data, including summary statistics with visualization, embedding methods like PCA and t-SNE, and Topological Data Analysis.

  • An Introduction to t-SNE with Python Example

    In this post we’ll give an introduction to the exploratory and visualization t-SNE algorithm. t-SNE is a powerful dimension reduction and visualization technique used on high dimensional data.

  • The Future of Data Affects the Whole Team – TDWI Orlando, Nov 11-16

    Eliminate Weak Links When You Bring Your Team to Orlando! Super Early Bird Deadline: September 14 - Save up to $915 with code KD20

  • Monash: Research Fellow Opportunities in Dialogue Research (Melbourne, Australia)

    We are inviting outstanding postdoctoral academics to join our world-class team to deliver high-quality research that will help shape the future of AI for conversational assistants, human-robot interaction, customer service, and other domains.

  • Seven Practical Ideas For Beginner Data Scientists

    As someone who has been there, I’d like to outline a few practical ideas to help junior data scientists get started at a small software company. The steps were drawn from my personal journey and that of others before me.

  • Programming Best Practices For Data Science">Silver BlogProgramming Best Practices For Data Science

    In this post, I'll go over the two mindsets most people switch between when doing programming work specifically for data science: the prototype mindset and the production mindset.

  • Weapons of Math Destruction, Ethical Matrix, Nate Silver and more Highlights from the Data Science Leaders Summit

    Domino Data Lab hosted its first ever Data Science Leaders Summit at the lovely Yerba Buena Center for the Arts in San Francisco on May 30-31, 2018.  Cathy O'Neil, Nate Silver, Cassie Kozyrkov and Eric Colson were some of the speakers at this event.

  • 5 reasons data analytics are falling short

    When it comes to big data, possession is not enough. Comprehensive intelligence is the key. But traditional data analytics paradigms simply cannot deliver on the promise of data-driven insights. Here’s why.

  • How to Build a Data Science Portfolio">Silver BlogHow to Build a Data Science Portfolio

    This post will include links to where various data science professionals (data science managers, data scientists, social media icons, or some combination thereof) and others talk about what to have in a portfolio and how to get noticed.

  • Ready your Skills for a Cloud-First World with Google

    The Machine Learning with TensorFlow on Google Cloud Platform Specialization on Coursera will help you jumpstart your career, includes hands-on labs, and takes you from a strategic overview to practical skills in building real-world, accurate ML models.

  • The Future of Map-Making is Open and Powered by Sensors and AI

    This article investigates the future of map-making and the role of Sensors, Artificial Intelligence and Machine Learning within that.

  • 9+ Rising Stars of Data Science

    Connect and learn from these 9 data science rock stars and over 231 more presenters at ODSC West 2018, Oct 31-Nov 3 in San Francisco. Get 60% off until Friday, July 13 - reserve your spot here .

  • AI Solutionism

    Machine learning has huge potential for the future of humanity — but it won’t solve all our problems.

  • Text Classification & Embeddings Visualization Using LSTMs, CNNs, and Pre-trained Word Vectors

    In this tutorial, I classify Yelp round-10 review datasets. After processing the review comments, I trained three model in three different ways and obtained three word embeddings.

  • Using Topological Data Analysis to Understand the Behavior of Convolutional Neural Networks

    Neural Networks are powerful but complex and opaque tools. Using Topological Data Analysis, we can describe the functioning and learning of a convolutional neural network in a compact and understandable way.

  • Analyzing Personalization Results

    The 4th part of this series will help answer the following questions: “Should I improve something or make changes to the system? Can it work more effectively? Can I squeeze the lion’s share of it?”

  • Novo Nordisk: Sr Data Scientist

    The Senior Data Scientist will build machine learning-based tools and processes within the company’s current big data infrastructure such as recommendation engines, automated propensity scoring systems, and A/B testing procedures.

  • Technical Content Personalization

    Part 3 of this series moves on from segmenting audiences to the technological side of the process.

  • How should I organize a larger data science team?

    VP of Data Science is asking opinions on how should he organize a larger Data Science team.

  • Command Line Tricks For Data Scientists

    Aspiring to master the command line should be on every developer’s list, especially data scientists. Learning the ins and outs of your terminal will undeniably make you more productive.

  • Audience Segmentation

    The process of audience segmentation is not about just statistics, it’s about finding your ideal clients and choosing the right way of interaction with them.

  • The Future of Artificial Intelligence: Is Your Job Under Threat?

    This article examines the rapid growth of artificial intelligence: how we got to this point, the future AI job market and what can be done.

  • University of Applied Sciences of Western Switzerland: Sr Research Data Scientist (Fraud Detection)

    Seeking a senior research data scientist to participate to the AFFUT (Advanced Analytics for Fraud Detection) project.

  • Using Linear Regression for Predictive Modeling in R

    In this post, we’ll use linear regression to build a model that predicts cherry tree volume from metrics that are much easier for folks who study trees to measure.

  • Introduction to Content Personalization

    The basics of user experience and content personalization. The way to target your audience more precisely and effectively.

  • Monash U: Lecturer/Sr Lecturer Digital Health

    Seeking up to 6 exceptional academics to take an active role in the Digital Health agenda. These will be continuing level B/C teaching and research positions (equivalent to tenure-track Assistant/Associate Professor).

  • Lumiata: Data Scientist

    Seeking data science candidates who are interested in using AI to predict impact of disease burden with respect to cost and clinical states.

  • Simple Derivatives with PyTorch

    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.

  • Top 7 Data Science Use Cases in Finance

    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.

    Also: #ApacheSpark: #Python vs. #Scala pros and cons for #DataScience; Loc2Vec: Learning location embeddings with triplet-loss networks; Skewness vs Kurtosis - The Robust Duo.

  • Disneyland Meets Data – Join TDWI this August

    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.

  • Jupyter Notebook for Beginners: A Tutorial

    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.

  • Pair Finance: Team Lead Data Scientist

    Seeking a Team Lead Data Scientist to work on a completely new and innovative product we are building, along with a small team of other experienced developers, and collaborate on an iterative design process from a basic prototype to the first production version.

  • Pair Finance: Python Developer

    Seeking a Python Developer to work on a completely new and innovative product we are building, along with a small team of other experienced developers, and to collaborate on an iterative design process from a basic prototype to the first production version.

  • KDnuggets Recognized as a Top Data Science Influencer for 2018

    Check out Onalytica's Data Science Influencers Report for 2018, and see where KDnuggets (and others) were ranked.

  • GetYourGuide: Senior Data Scientist – Analytics

    As a Senior Data Scientist for Analytics you will play a key role in generating impactful insights from our huge data lake changing the way how we run aspects of our business. You will work on problems such as the identification of potential growth drivers or customer loyalty.

  • Apple: Commerce Data Scientist – Apple Media Products

    Seeking someone with a love for data. This position involves working on very large scale data mining, cleaning, analysis, deep level processing, machine learning or statistic modeling, metrics tracking and evaluation.

  • Top KDnuggets tweets, Apr 11-17: Boost your #datascience skills. Learn linear algebra.

    Also: Don’t learn #MachineLearning in 24 hours; Top 8 Free Must-Read Books on #DeepLearning; How Attractive Are You in the Eyes of Deep #NeuralNetwork?; Ten #MachineLearning Algorithms You Should Know to Become a #DataScientist

  • Role of IoT in Education

    In this article, I will discuss the significance of IoT and gain insights on why this technology is becoming an integral part of the daily learning and teaching methodologies.

  • Top KDnuggets tweets, Apr 04-10: Introduction to Markov Chains

    Also: 5 Things to Know About #MachineLearning; A "Weird" Intro to #DeepLearning; How Do I Get My First #DataScience Job?

  • Why Data Scientists Must Focus on Developing Product Sense

    Data Scientists should focus on developing product sense to move fast and systematically, create models that are relevant and to able to know when to stop.

  • Strata London, 21-24 May – KDnuggets Special Offer

    Meet the data industry's leading innovators, hear compelling data case studies, brush up on your technical skills, and get GDPR compliant.

  • Principles of Guided Analytics

    KNIME outline their guided analytics system and explain how this can assist data scientists to predict future outcomes.

  • 5 Things You Need to Know about Sentiment Analysis and Classification">Gold Blog5 Things You Need to Know about Sentiment Analysis and Classification

    We take a look at the important things you need to know about sentiment analysis, including social media, classification, evaluation metrics and how to visualise the results.

  • Data Skills: They’re Not Just for Data Scientists

    The continued growth of big data, both in terms of quality and accessibility, is disrupting a wide range of roles. The skills needed to analyse this data need to be learned by everyone - not just data scientists.

  • Top KDnuggets tweets, Mar 14-20: Introduction to Markov Chains “What are Markov chains, when to use them, and how they work”

    Also: Reinforcement Learning Cheat Sheet; How to do #MachineLearning Efficiently; 6 Interesting Things You Can Do with #Python on #Facebook Data; Demystifying #Docker for #DataScientists

  • National Grid: Dev Ops – Operations Engineer / Sr Ops Engineer – Advanced Analytics

    Seeking an Analytics Operations Engineer you will package, optimize, operationalize/productionize cloud based advanced analytical and big data software solutions.

  • Quick Feature Engineering with Dates Using

    The library is a collection of supplementary wrappers for a host of popular machine learning libraries, designed to remove the necessity of writing your own functions to take care of some repetitive tasks in a machine learning workflow.

  • Web Scraping with Python: Illustration with CIA World Factbook

    In this article, we show how to use Python libraries and HTML parsing to extract useful information from a website and answer some important analytics questions afterwards.

  • Apple: Commerce Data Scientist – Apple Media Products

    Seeking a talented, experienced Applied Researcher/Data Scientist to work on high visibility projects that affect millions of customers globally.

  • Introduction to Markov Chains">Silver BlogIntroduction to Markov Chains

    What are Markov chains, when to use them, and how they work

  • Yeshiva University: Faculty

    Seeking full and part time faculty, to teach both at our midtown Manhattan campus and online, where we offer data science, analytics, quantitative, risk, and related graduate programs. Are you the right candidate?

  • A Few Statistics Tips for Marketers

    Statistics can help good marketers become better marketers. Here are a few things they should know about stats.

  • How data science can improve retail">Silver BlogHow data science can improve retail

    We’re going to take a look at a few surprising ways that data science can increase your sales, both offline and online.

  • How to Survive Your Data Science Interview

    There are many wonderful things about data science. It’s extreme breadth is not one of them. The title of data scientist means something different at every company

  • Four Broken Systems & Four Tech Trends for 2018

    We may be well into 2018, but here are a set of tech trends for looking forward, along with a set of 4 systems that manifested how inappropriate, inaccurate or outright broken they are in 2017.

  • A powerful new IDE to build, test, and run Apache Spark applications on your desktop for free!

    Build enterprise-grade functionally rich Spark applications with the aid of an intuitive drag-and-drop user interface and a wide array of pre-built Spark operators.

  • A Comparative Analysis of Top 6 BI and Data Visualization Tools in 2018">Silver BlogA Comparative Analysis of Top 6 BI and Data Visualization Tools in 2018

    In this article, we will compare the most commonly used platforms and analyze their main features to help you choose one or several platforms that will provide indispensable aid for your work communication.

  • Calculating Customer Lifetime Value: SQL Example

    In order to understand how to estimate LTV, it is useful to first think about evaluating a customer’s lifetime value at the end of their relationship with us.

  • 2018 Predictions for the Analytics & Data Science Hiring Market

    What do you think of this year’s predictions? Do you see any new tools on the horizon, or do you believe data science popularity is due for a reckoning of sorts?

  • Web Scraping Tutorial with Python: Tips and Tricks">Gold BlogWeb Scraping Tutorial with Python: Tips and Tricks

    This post is intended for people who are interested to know about the common design patterns, pitfalls and rules related to the web scraping.

  • A Beginner’s Guide to Data Engineering  –  Part I">Silver BlogA Beginner’s Guide to Data Engineering  –  Part I

    Data Engineering: The Close Cousin of Data Science.

  • Want to Become a Data Scientist? Try Feynman Technique">Silver BlogWant to Become a Data Scientist? Try Feynman Technique

    Get over the impostor syndrome by developing a strong understanding about the various Data Science topics using the Feynman Technique

  • Using Excel with Pandas

    In this tutorial, we are going to show you how to work with Excel files in pandas, covering computer setup, reading in data from Excel files into pandas, data exploration in pandas, and more.

  • Using Genetic Algorithm for Optimizing Recurrent Neural Networks

    In this tutorial, we will see how to apply a Genetic Algorithm (GA) for finding an optimal window size and a number of units in Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN).

  • Topological Data Analysis for Data Professionals: Beyond Ayasdi

    We review recent developments and tools in topological data analysis, including applications of persistent homology to psychometrics and a recent extension of piecewise regression, called Morse-Smale regression.

  • After the “Meltdown,” How Can You Protect Your Database?

    What Data Scientists should know about Meltdown and Spectre viruses and how to protect the potentially affected databases. The most important thing is to prevent outside parties from executing local Javascript code on your machine.

  • The Convergence of AI and Blockchain: What’s the deal?

    This article wants to give a flavor of the potentialities realized at the intersection of AI and Blockchain and discuss standard definitions, challenges, and benefits of this alliance, as well as about some interesting player in this space.

  • How To Debug Your Approach To Data Analysis

    Seven common biases that influence how we understand, use, and interpret the world around us.

  • Data Science for Laymen: 5 Writers Who Speak Your Language

    Here are 5 excellent Data Scientists who are also very good at explaining concepts and interacting with you.

  • Top KDnuggets tweets, Dec 20-26: Harvard CS109 #DataScience Course Resources; Computer Vision by Andrew Ng: Lessons Learned

    Also: Ten years in, nobody has come up with a use for #blockchain - here is what happened; Can I Become a #DataScientist: Research into 1,001 #DataScience Profiles.

  • View from Google Assistant: Are we becoming reliant on AI?

    AI is powering a paradigm shift in human machine interaction and conversational UIs like Alexa, Cortana, Google Assistant, and Siri, have the potential to break free from some key limitations of mobile app.

  • Demystifying Data Science

    Marketing scientist Kevin Gray asks Dr. Randy Bartlett of Blue Sigma Analytics what Data Science really is and how it can help decision-makers.

  • An Introduction to Monte Carlo Tree Search

    A great explanation of the concept behind Monte Carlo Tree Search algorithm and a brief example of how it was used at the European Space Agency for planning interplanetary flights.

  • How Much Mathematics Does an IT Engineer Need to Learn to Get Into Data Science?">Gold BlogHow Much Mathematics Does an IT Engineer Need to Learn to Get Into Data Science?

    When I started diving deep into these exciting subjects (by self-study), I discovered quickly that I don’t know/only have a rudimentary idea about/ forgot mostly what I studied in my undergraduate study some essential mathematics.

  • athenahealth: Data Scientists

    Seeking experienced data scientists who love machine learning and complex data and who care about making a positive impact on the world by fielding real ML-driven systems. Positions are available at multiple levels of seniority.

  • Ingram Micro: Data Architect

    Seeking a talented and highly motivated Architect with 7+ years of experience for our Global Data Infrastructure team, with responsibilities ranging from sustaining current systems to developing the future state of the Enterprise Global Data Infrastructure Architecture framework.

  • Get Network insights in Excel with NodeXL

    NodeXL, the network overview discovery and exploration add-in for the familiar Microsoft Office Excel (TM) spreadsheet brings network functions within the reach of people who are more comfortable making pie charts than writing code. See what NodeXL finds in KDnuggets network and download NodeXL Pro for your analyses.

  • How to Generate FiveThirtyEight Graphs in Python

    In this post, we'll help you. Using Python's matplotlib and pandas, we'll see that it's rather easy to replicate the core parts of any FiveThirtyEight (FTE) visualization.

  • TensorFlow for Short-Term Stocks Prediction

    In this post you will see an application of Convolutional Neural Networks to stock market prediction, using a combination of stock prices with sentiment analysis.

  • SlashData: Technology Analyst & Author

    Seeking a Technology Analyst & Author to support our developer research efforts. The Technology Analyst & Author will be responsible for analysing our Developer Economics survey data to deliver cutting-edge insights and reports on the future of software.

  • Web Scraping for Data Science with Python

    We take a quick look at how web scraping can be useful in the context of data science projects, eg to construct a social graph based of S&P 500 companies, using Python and Gephi.

  • InfoGAN - Generative Adversarial Networks Part III

    In this third part of this series of posts the contributions of InfoGAN will be explored, which apply concepts from Information Theory to transform some of the noise terms into latent codes that have systematic, predictable effects on the outcome.

  • Using TensorFlow for Predictive Analytics with Linear Regression

    This post presents a powerful and simple example of how to use TensorFlow to perform a Linear Regression. check out the code for your own experiments!

  • American Family Insurance: Director, Data Science & Analytics

    Seeking a Director, Data Science & Analytics, to be responsible for oversight and strategic direction of advanced analytics, including predictive modeling, that drive business performance consistent with company goals and objectives.

  • Best Online Masters in Data Science and Analytics – a comprehensive, unbiased survey">Gold BlogBest Online Masters in Data Science and Analytics – a comprehensive, unbiased survey

    The first comprehensive and objective survey of online Masters in Analytics / Data Science, including rankings, tuition, and duration of the education program.

  • The amazing predictive power of conditional probability in Bayes Nets

    This article explains how Bayes Nets gain remarkable predictive power by their use of conditional probability. This adds to several other salient strengths, making them a preeminent method for prediction and understanding variables’ effects.

  • Top KDnuggets tweets, Nov 01-07: Airbnb develops an #AI which converts design into source code

    Also: One LEGO at a time: Explaining the #Math of How #NeuralNetworks Learn; 6 Books Every #DataScientist Should Keep Nearby; Direct from Sebastian Raschka #Python #MachineLearning book, new edition.

  • Tips for Getting Started with Text Mining in R and Python

    This article opens up the world of text mining in a simple and intuitive way and provides great tips to get started with text mining.

  • How to Job Interview a Data Scientist

    Data Scientist is a very broad term and hiring a good fit data scientist for your project is challenging task. Here we discuss this important topic in details.

  • More than the Hype: Beyond Gartner’s Hype Cycle

    Gartner publishes hype cycles across different technologies and sectors. Here we conduct detailed analysis of Gartner’s Hype Cycles.

  • Cybersecurity: Managing Risk in the Information age, Harvard online short course

    Learn how to identify and manage operational risk, litigation risk and reputational risk. This course is brought to you by HarvardX in collaboration with GetSmarter, experts in online education for working professionals.

  • Apple: Manager, Data Science – Apple Media Products Commerce Engineering

    Seeking an individual who will create their own data models around the wealth set of commerce data. They will also be working in one of the world's largest and most complex data warehouse environments, and be working with the metrics, analytics, and anti-fraud teams.

  • Machine Ethics and Artificial Moral Agents

    This article is simply a stream of consciousness on questions and problems I have been thinking and asking myself, and hopefully, it will stimulate some discussion.

  • Jefferies: Data Scientist

    Seeking a Data Scientist with deep experience in Statistical and Machine Learning to help us build and integrate data-driven intelligent solution into our business processes.

  • Monash: Research Fellow – Blockchain

    Seeking research fellows to work on a project about post-quantum cryptographic algorithms in blockchain. Selected candidates will have a unique opportunity to work closely with an international team of key researchers located in Hong Kong and Shanghai.

  • XGBoost: A Concise Technical Overview">Silver BlogXGBoost: A Concise Technical Overview

    Interested in learning the concepts behind XGBoost, rather than just using it as a black box? Or, are you looking for a concise introduction to XGBoost? Then, this article is for you. Includes a Python implementation and links to other basic Python and R codes as well.

  • Density Based Spatial Clustering of Applications with Noise (DBSCAN)

    DBSCAN clustering can identify outliers, observations which won’t belong to any cluster. Since DBSCAN clustering identifies the number of clusters as well, it is very useful with unsupervised learning of the data when we don’t know how many clusters could be there in the data.

  • Top KDnuggets tweets, Oct 18-24: Chihuahua or muffin? The #DataScience Project Playbook

    Chihuahua or muffin? My search for the best computer vision API; Could #AI Be the Future of #FakeNews and Product Reviews? 7 Types of Artificial #NeuralNetworks for NLP.

  • Business intuition in data science

    Data Science projects are not just use of algorithms & building models; there are other steps of the project which are equally important. Here we explain them in detail.

  • Your Complete Guide to Predictive Analytics World – Oct 29-Nov 2 in New York City

    Predictive Analytics World for Business is slated for Oct 29-Nov 2 in New York City. See for yourself precisely how Fortune 500 analytics competitors and other top practitioners deploy predictive modeling and machine learning, and the kind of business results they achieve.

  • Apple: Manager, Data Science – Apple Media Products Commerce Engineering

    Seeking a Manager, Data Science to build our data science and machine learning team and develop a long-term roadmap where modeling will have a huge material impact in savings in financial cost while maintaining the Apple brand of trust, using cutting edge technology.

  • It Only Takes One Line of Code to Run Regression

    I learned how important to understand data before running algorithms, how important it is to know the context and the industry before jumping on getting insights, how it is very easy to make models but tough to get them to work for you, and finally, how it only takes one line of code to run linear regression on your dataset.

  • The Fast Path to Success with AI, DataRobot Webinar, Oct 26

    Learn The Fast Path to Success with AI and see how industry leaders are generating ROI with AI Webinar Details Register today Thursday, October 26, 2017.

  • An opinionated Data Science Toolbox in R from Hadley Wickham, tidyverse

    Get your productivity boosted with Hadley Wickham's powerful R package, tidyverse. It has all you need to start developing your own data science workflows.

  • What makes a data visualization successful?

    Data visualisation gives very important insights about the data. But it is subjective to the goal of analysis & area of application. Let’s see how.

  • Top KDnuggets tweets, Sep 13-19: Top Books on NLP; What Else Can AI Guess From Your Face?

    Also: The Ten Fallacies of Data Science; #Python #Pandas tips and tricks; Geoff Hinton says we need to start all over.

  • Advancing Analytics, Melbourne, October 18 – Early bird extended

    IAPA National Conference in Melbourne on 18 October will be a fantastic day with another five speakers just announced. Early bird rates have been extended to Sep 20 or become IAPA member and save even more.

  • Top KDnuggets tweets, Sep 06-12: Visualizing Cross-validation Code; Intro to #Blockchain and #BigData

    Also: WTF #Python - A collection of interesting and tricky Python examples; Thoughts after taking @AndrewYNg #Deeplearning #ai course; Another #Keras Tutorial For #NeuralNetwork Beginners.

  • Accelerating Your Algorithms in Production with Python and Intel MKL, Sep 21

    We will provide tips for data scientists to speed up Python algorithms, including a discussion on algorithm choice, and how effective package tool can make large differences in performance.

  • Closing the Insights-to-Action Gap">Silver Blog, Sep 2017Closing the Insights-to-Action Gap

    There are many types of analytics for getting insight out of data, but the bigger and more difficult challenge is turning that insight into action. What should we do differently based on your findings?

  • How To Write Better SQL Queries: The Definitive Guide – Part 2

    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.

  • Willis Towers Watson: Data Scientist – Financial Services

    Seeking a Data Scientist with a passion for delivering innovative analytical solutions. You will deliver high quality work for our broad set of UK clients, working on projects including behavioural modelling, price optimisation, financial risk modelling and big data analytics.

  • Top KDnuggets tweets, Aug 09-15: #Tensorflow tutorials and best practices; Top Influencers for #DataScience

    Also 37 Reasons why your #NeuralNetwork is not working; Making Predictive Model Robust: Holdout vs Cross-Validation.

  • Jefferies: Sr Data Scientist

    Seeking a hands-on-data scientist with deep experience in Statistical and Machine Learning to help us build and integrate data-driven intelligent solution into our business processes.

  • Accenture: Data Science Consultant

    Seeking a Data Science Consultant to strategically handle the massive amounts of information our clients collect today so that it may become their most valuable new asset, and to gain actionable insights from that data is critical to produce tangible results.

  • Accenture: Artificial Intelligence Analytics Sr Manager

    Seeking an Artificial Intelligence Analytics Senior Manager to build and drive Artificial Intelligence opportunities in the UK and Ireland. This person will be responsible for helping shape Accenture’s point of view on Artificial Intelligence.

  • Accenture: Financial Services Analytics Senior Manager

    Seeking a Financial Services Analytics Senior Manager to deliver analytically-informed, issue-based solutions that help clients make faster, smarter decisions, play a critical role in helping them tackle complex business issues.

  • Top Influencers for Data Science

    We report on top influencers in Data science in 2017 and compare with similar reports reports from 2016.

  • Top 3 Breakthroughs in Combating Financial Crime

    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.

  • How will Big Data companies monetize data in 2018?

    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.

  • How to squeeze the most from your training data

    In many cases, getting enough well-labelled training data is a huge hurdle for developing accurate prediction systems. Here is an innovative approach which uses SVM to get the most from training data.

  • ML Engineer/Data Scientist

    Seeking engineers with a passion for technology and a thirst for solving unique and hard problems. Forge is solving one of hardest challenges in AI - how to capture and transform the world’s unstructured information.

  • SIGKDD Elects Jian Pei as Chair, Michael Zeller Treasurer, New Executive Committee

    New SIGKDD chair will be Dr. Jian Pei. He says SIGKDD must continue serving academia and industry in a balanced and innovative manner.

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