Search results for Foundation Models

Found 568 documents, 5970 searched:

• 12 Deep Learning Researchers and Leaders">12 Deep Learning Researchers and Leaders

  Our list of deep learning researchers and industry leaders are the people you should follow to stay current with this wildly expanding field in AI. From early practitioners and established academics to entrepreneurs and today’s top corporate influencers, this diverse group of individuals is leading the way into tomorrow’s deep learning landscape.

  https://www.kdnuggets.com/2019/09/12-deep-learning-research-leaders.html

• Introducing IceCAPS: Microsoft’s Framework for Advanced Conversation Modeling

  The new open source framework that brings multi-task learning to conversational agents.

  https://www.kdnuggets.com/2019/09/introducing-icecaps-microsofts-framework-advanced-conversation-modeling.html

• Applying Data Science to Cybersecurity Network Attacks & Events

  Check out this detailed tutorial on applying data science to the cybersecurity domain, written by an individual with backgrounds in both fields.

  https://www.kdnuggets.com/2019/09/applying-data-science-cybersecurity-network-attacks-events.html

• Beyond Neurons: Five Cognitive Functions of the Human Brain that we are Trying to Recreate with Artificial Intelligence

  The quest for recreating cognitive capabilities of the brain in deep neural networks remains one of the elusive goals of AI. Let’s explore some human cognitive skills that are serving as inspiration to a new generation of AI techniques.

  https://www.kdnuggets.com/2019/09/beyond-neurons-five-cognitive-functions-human-brain-recreate-artificial-intelligence.html

• Jobs in Data Science, Machine Learning, AI & Analytics

  To add a free short entry here for a job related to Data Science, Machine Learning, AI or Analytics, email the following 5 items to Read more »

  https://www.kdnuggets.com/jobs/index.html

• Statistical Modelling vs Machine Learning">Statistical Modelling vs Machine Learning

  At times it may seem Machine Learning can be done these days without a sound statistical background but those people are not really understanding the different nuances. Code written to make it easier does not negate the need for an in-depth understanding of the problem.

  https://www.kdnuggets.com/2019/08/statistical-modelling-vs-machine-learning.html

• 6 Key Concepts in Andrew Ng’s “Machine Learning Yearning”">6 Key Concepts in Andrew Ng’s “Machine Learning Yearning”

  If you are diving into AI and machine learning, Andrew Ng's book is a great place to start. Learn about six important concepts covered to better understand how to use these tools from one of the field's best practitioners and teachers.

  https://www.kdnuggets.com/2019/08/key-concepts-andrew-ng-machine-learning-yearning.html

• This New Google Technique Help Us Understand How Neural Networks are Thinking">This New Google Technique Help Us Understand How Neural Networks are Thinking

  Recently, researchers from the Google Brain team published a paper proposing a new method called Concept Activation Vectors (CAVs) that takes a new angle to the interpretability of deep learning models.

  https://www.kdnuggets.com/2019/07/google-technique-understand-neural-networks-thinking.html

• 12 Things I Learned During My First Year as a Machine Learning Engineer

  Learn about the day-in-the-life of one machine learning engineer and the important lessons learned for being successful in that role.

  https://www.kdnuggets.com/2019/07/12-things-learned-machine-learning-engineer.html

• Pre-training, Transformers, and Bi-directionality

  Bidirectional Encoder Representations from Transformers BERT (Devlin et al., 2018) is a language representation model that combines the power of pre-training with the bi-directionality of the Transformer’s encoder (Vaswani et al., 2017). BERT improves the state-of-the-art performance on a wide array of downstream NLP tasks with minimal additional task-specific training.

  https://www.kdnuggets.com/2019/07/pre-training-transformers-bi-directionality.html

• The Death of Big Data and the Emergence of the Multi-Cloud Era">The Death of Big Data and the Emergence of the Multi-Cloud Era

  The Era of Big Data is coming to an end as the focus shifts from how we collect data to processing that data in real-time. Big Data is now a business asset supporting the next eras of multi-cloud support, machine learning, and real-time analytics.

  https://www.kdnuggets.com/2019/07/death-big-data-multi-cloud-era.html

• Why you’re not a job-ready data scientist (yet)">Why you’re not a job-ready data scientist (yet)

  Trying to snag a dream Data Science job, but can't seem to land one? Check out these four skills that companies really want and be prepared for your next interview.

  https://www.kdnuggets.com/2019/07/not-job-ready-data-scientist-yet.html

• A Data Scientist’s Path to Understanding Market Simulation

  Made possible by recent advances in computing power and machine learning, market simulation employs agent-based modeling, behavioral science and network science to recreate the complex dynamics and rules of how a population of people in a given market behave, influence each other and make decisions.

  https://www.kdnuggets.com/2019/07/data-scientist-understanding-market-simulation.html

• PySyft and the Emergence of Private Deep Learning

  PySyft is an open-source framework that enables secured, private computations in deep learning, by combining federated learning and differential privacy in a single programming model integrated into different deep learning frameworks such as PyTorch, Keras or TensorFlow.

  https://www.kdnuggets.com/2019/06/pysyft-emergence-deep-learning.html

• How to Make a Success Story of your Data Science Team

  Today, data science is a crucial component for an organization's growth. Given how important data science has grown, it’s important to think about what data scientists add to an organization, how they fit in, and how to hire and build effective data science teams.

  https://www.kdnuggets.com/2019/06/success-story-data-science-team.html

• The Data Fabric for Machine Learning – Part 2: Building a Knowledge-Graph

  Before being able to develop a Data Fabric we need to build a Knowledge-Graph. In this article I’ll set up the basis on how to create it, in the next article we’ll go to the practice on how to do this.

  https://www.kdnuggets.com/2019/06/data-fabric-machine-learning-building-knowledge-graph.html

• The Emergence of Cooperative and Competitive AI Agents

  Without specific training in collaboration or competition, a recent AI model from DeepMind uses reinforcement learning to evolve these behaviors in game-playing agents. Learn how this emergent collective intelligence outperforms their human counterparts in 3D multiplayer games.

  https://www.kdnuggets.com/2019/06/emergence-cooperative-competitive-ai-agents.html

• The Machine Learning Puzzle, Explained">The Machine Learning Puzzle, Explained

  Lots of moving parts go into creating a machine learning model. Let's take a look at some of these core concepts and see how the machine learning puzzle comes together.

  https://www.kdnuggets.com/2019/06/machine-learning-puzzle-explained.html

• Why Machine Learning is vulnerable to adversarial attacks and how to fix it

  Machine learning can process data imperceptible to humans to produce expected results. These inconceivable patterns are inherent in the data but may make models vulnerable to adversarial attacks. How can developers harness these features to not lose control of AI?

  https://www.kdnuggets.com/2019/06/machine-learning-adversarial-attacks.html

• If you’re a developer transitioning into data science, here are your best resources"> If you’re a developer transitioning into data science, here are your best resources

  This article will provide a background on the data scientist role and why your background might be a good fit for data science, plus tangible stepwise actions that you, as a developer, can take to ramp up on data science.

  https://www.kdnuggets.com/2019/06/developer-transitioning-data-science-best-resources.html

• Choosing an Error Function

  The error function expresses how much we care about a deviation of a certain size. The choice of error function depends entirely on how our model will be used.

  https://www.kdnuggets.com/2019/06/choosing-error-function.html

• Understanding Backpropagation as Applied to LSTM

  Backpropagation is one of those topics that seem to confuse many once you move past feed-forward neural networks and progress to convolutional and recurrent neural networks. This article gives you and overall process to understanding back propagation by giving you the underlying principles of backpropagation.

  https://www.kdnuggets.com/2019/05/understanding-backpropagation-applied-lstm.html

• AI in the Family: how to teach machine learning to your kids

  AI is all the rage with today’s programmers, but what about the next generation? Machine learning can be introduced to young ones just now learning about code, and you can help spark their interest.

  https://www.kdnuggets.com/2019/05/ai-machine-learning-kids.html

• Best US/Canada Masters in Analytics, Business Analytics, Data Science

  In the final part of this series, we provide an updated list of our comprehensive, unbiased survey of graduate programs in Data Science and Analytics from across the US and Canada.

  https://www.kdnuggets.com/2019/05/best-masters-data-science-analytics-us-canada.html

• The Third Wave Data Scientist">The Third Wave Data Scientist

  An extensive look at what skills are needed to make up the portfolio of the third wave of data scientists.

  https://www.kdnuggets.com/2019/05/third-wave-data-scientist.html

• 2019 Best Masters in Data Science and Analytics – Online

  We provide an updated comprehensive and objective survey of online Masters in Analytics and Data Science, including rankings, tuition, and duration of the education program.

  https://www.kdnuggets.com/2019/04/best-masters-data-science-analytics-online.html

• Distributed Artificial Intelligence: A primer on Multi-Agent Systems, Agent-Based Modeling, and Swarm Intelligence

  Distributed Artificial Intelligence (DAI) is a class of technologies and methods that span from swarm intelligence to multi-agent technologies. It is one of the subsets of AI where simulation has greater importance that point-prediction.

  https://www.kdnuggets.com/2019/04/distributed-artificial-intelligence-multi-agent-systems-agent-based-modeling-swarm-intelligence.html

• 2019 Best Masters in Data Science and Analytics – Europe Edition">2019 Best Masters in Data Science and Analytics – Europe Edition

  We provide an updated list of our comprehensive, unbiased survey of graduate programs in Data Science and Analytics from across Europe.

  https://www.kdnuggets.com/2019/04/best-masters-data-science-analytics-europe.html

• Another 10 Free Must-See Courses for Machine Learning and Data Science">Another 10 Free Must-See Courses for Machine Learning and Data Science

  Check out another follow-up collection of free machine learning and data science courses to give you some spring study ideas.

  https://www.kdnuggets.com/2019/04/another-10-free-must-see-courses-machine-learning-data-science.html

• Predict Age and Gender Using Convolutional Neural Network and OpenCV">Predict Age and Gender Using Convolutional Neural Network and OpenCV

  Age and gender estimation from a single face image are important tasks in intelligent applications. As such, let's build a simple age and gender detection model in this detailed article.

  https://www.kdnuggets.com/2019/04/predict-age-gender-using-convolutional-neural-network-opencv.html

• [eBook] Standardizing the Machine Learning Lifecycle

  We explore what makes the machine learning lifecycle so challenging compared to regular software, and share the Databricks approach.

  https://www.kdnuggets.com/2019/03/databrocks-ebook-machine-learning-lifecycle.html

• Neural Networks with Numpy for Absolute Beginners: Introduction

  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.

  https://www.kdnuggets.com/2019/03/neural-networks-numpy-absolute-beginners-introduction.html

• The Difference Between Data Scientists and Data Engineers

  ODSC East 2019 has multiple tracks for both Data Scientists and Data Engineers, including workshops, talks, and training sessions. Save 45% with code KDN45.

  https://www.kdnuggets.com/2019/03/odsc-difference-data-scientists-data-engineers.html

• On Building Effective Data Science Teams

  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.

  https://www.kdnuggets.com/2019/03/building-effective-data-science-teams.html

• Deconstructing BERT: Distilling 6 Patterns from 100 Million Parameters

  Google’s BERT algorithm has emerged as a sort of “one model to rule them all.” BERT builds on two key ideas that have been responsible for many of the recent advances in NLP: (1) the transformer architecture and (2) unsupervised pre-training.

  https://www.kdnuggets.com/2019/02/deconstructing-bert-distilling-patterns-100-million-parameters.html

• What are Some “Advanced” AI and Machine Learning Online Courses?

  Where can you find not-so-common, but high-quality online courses (Free) for ‘advanced’ machine learning and artificial intelligence?

  https://www.kdnuggets.com/2019/02/some-advanced-ai-machine-learning-online-courses.html

• Python Data Science for Beginners">Python Data Science for Beginners

  Python’s syntax is very clean and short in length. Python is open-source and a portable language which supports a large standard library. Buy why Python for data science? Read on to find out more.

  https://www.kdnuggets.com/2019/02/python-data-science-beginners.html

• How AI can help solve some of humanity’s greatest challenges – and why we might fail

  AI represents a step change in humanity’s ability to rise to its greatest challenges. We explore three areas in which AI can contribute to the UN’s Global Goals - and why we could fall short.

  https://www.kdnuggets.com/2019/02/ai-help-solve-humanity-challenges.html

• Five Ways Your Safety Depends on Machine Learning

  Eric Siegel tells you about five ways your safety depends on machine learning, which actively protects you from all sorts of dangers, including fires, explosions, collapses, crashes, workplace accidents, restaurant E. coli, and crime.

  https://www.kdnuggets.com/2019/02/dr-data-five-ways-safety-depends-machine-learning.html

• Cracking the Data Scientist Interview

  After interviewing with over 50 companies for Data Scientist/Machine Learning Engineer, I am going to frame my experiences in the Q&A format and try to debunk any myths that beginners may have in their quest for becoming a Data Scientist.

  https://www.kdnuggets.com/2019/01/cracking-data-scientist-interview.html

• Why Vegetarians Miss Fewer Flights – Five Bizarre Insights from Data

  A frenzy of number-crunching is churning out a heap of insights that are colorful, sometimes surprising, and often valuable. We explain how this works, and investigate five bizarre discoveries found in data.

  https://www.kdnuggets.com/2019/01/dr-data-five-bizarre-insights-from-data.html

• The Role of the Data Engineer is Changing

  The role of the data engineer in a startup data team is changing rapidly. Are you thinking about it the right way?

  https://www.kdnuggets.com/2019/01/role-data-engineer-changing.html

• Top 10 Books on NLP and Text Analysis">Top 10 Books on NLP and Text Analysis

  When it comes to choosing the right book, you become immediately overwhelmed with the abundance of possibilities. In this review, we have collected our Top 10 NLP and Text Analysis Books of all time, ranging from beginners to experts.

  https://www.kdnuggets.com/2019/01/top-10-books-nlp-text-analysis.html

• The Backpropagation Algorithm Demystified

  A crucial aspect of machine learning is its ability to recognize error margins and to interpret data more precisely as rising numbers of datasets are fed through its neural network. Commonly referred to as backpropagation, it is a process that isn’t as complex as you might think.

  https://www.kdnuggets.com/2019/01/backpropagation-algorithm-demystified.html

• Supervised Learning: Model Popularity from Past to Present

  An extensive look at the history of machine learning models, using historical data from the number of publications of each type to attempt to answer the question: what is the most popular model?

  https://www.kdnuggets.com/2018/12/supervised-learning-model-popularity-from-past-present.html

• Six Steps to Master Machine Learning with Data Preparation

  To prepare data for both analytics and machine learning initiatives teams can accelerate machine learning and data science projects to deliver an immersive business consumer experience that accelerates and automates the data-to-insight pipeline by following six critical steps.

  https://www.kdnuggets.com/2018/12/six-steps-master-machine-learning-data-preparation.html

• Why You Shouldn’t be a Data Science Generalist">Why You Shouldn’t be a Data Science Generalist

  But it’s hard to avoid becoming a generalist if you don’t know which common problem classes you could specialize in in the fist place. That’s why I put together a list of the five problem classes that are often lumped together under the “data science” heading.

  https://www.kdnuggets.com/2018/12/why-shouldnt-data-science-generalist.html

• Solve any Image Classification Problem Quickly and Easily

  This article teaches you how to use transfer learning to solve image classification problems. A practical example using Keras and its pre-trained models is given for demonstration purposes.

  https://www.kdnuggets.com/2018/12/solve-image-classification-problem-quickly-easily.html

• Should you become a data scientist?">Should you become a data scientist?

  An overview of the current situation for data scientists, from its origins and history, to the recent growth in job postings, and looking at what changes the future might bring.

  https://www.kdnuggets.com/2018/12/should-i-become-a-data-scientist.html

• A comprehensive list of Machine Learning Resources: Open Courses, Textbooks, Tutorials, Cheat Sheets and more

  A thorough collection of useful resources covering statistics, classic machine learning, deep learning, probability, reinforcement learning, and more.

  https://www.kdnuggets.com/2018/12/finlayson-machine-learning-resources.html

• An Introduction to AI">An Introduction to AI

  We provide an introduction to AI key terminologies and methodologies, covering both Machine Learning and Deep Learning, with an extensive list including Narrow AI, Super Intelligence, Classic Artificial Intelligence, and more.

  https://www.kdnuggets.com/2018/11/an-introduction-ai.html

• Text Preprocessing in Python: Steps, Tools, and Examples

  We outline the basic steps of text preprocessing, which are needed for transferring text from human language to machine-readable format for further processing. We will also discuss text preprocessing tools.

  https://www.kdnuggets.com/2018/11/text-preprocessing-python.html

• A Winning Game Plan For Building Your Data Science Team">A Winning Game Plan For Building Your Data Science Team

  We need to understand the responsibilities, capabilities, expectations and competencies of the Data Engineer, Data Scientist and Business Stakeholder.

  https://www.kdnuggets.com/2018/09/winning-game-plan-building-data-science-team.html

• Topic Modeling with LSA, PLSA, LDA & lda2Vec">Topic Modeling with LSA, PLSA, LDA & lda2Vec

  This article is a comprehensive overview of Topic Modeling and its associated techniques.

  https://www.kdnuggets.com/2018/08/topic-modeling-lsa-plsa-lda-lda2vec.html

• Data Scientist Interviews Demystified">Data Scientist Interviews Demystified

  We look at typical questions in a data science interview, examine the rationale for such questions, and hope to demystify the interview process for recent graduates and aspiring data scientists.

  https://www.kdnuggets.com/2018/08/data-scientist-interviews-demystified.html

• Building a Basic Keras Neural Network Sequential Model

  The approach basically coincides with Chollet's Keras 4 step workflow, which he outlines in his book "Deep Learning with Python," using the MNIST dataset, and the model built is a Sequential network of Dense layers. A building block for additional posts.

  https://www.kdnuggets.com/2018/06/basic-keras-neural-network-sequential-model.html

• Advice For Applying To Data Science Jobs

  A comprehensive guide to applying for a job in data science, covering the application, interview and offer stage.

  https://www.kdnuggets.com/2018/06/advice-applying-data-science-jobs.html

• Top Stories, May 7-13: 2018 KDnuggets Analytics, Data Mining, Data Science, Machine Learning Software Poll; WTF is a Tensor?!?

  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!

  https://www.kdnuggets.com/2018/05/top-news-week-0507-0513.html

• Torus for Docker-First Data Science

  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.

  https://www.kdnuggets.com/2018/05/torus-docker-first-data-science.html

• KDnuggets™ News 18:n18, May 2: Blockchain Explained in 7 Python Functions; Data Science Dirty Secret; Choosing the Right Evaluation Metric

  Also: Building Convolutional Neural Network using NumPy from Scratch; Data Science Interview Guide; Implementing Deep Learning Methods and Feature Engineering for Text Data: The GloVe Model; Jupyter Notebook for Beginners: A Tutorial

  https://www.kdnuggets.com/2018/n18.html

• Are High Level APIs Dumbing Down Machine Learning?

  Libraries like Keras simplify the construction of neural networks, but are they impeding on practitioners full understanding? Or are they simply useful (and inevitable) abstractions?

  https://www.kdnuggets.com/2018/04/high-level-apis-dumbing-down-machine-learning.html

• Top 20 Deep Learning Papers, 2018 Edition">Top 20 Deep Learning Papers, 2018 Edition

  Deep Learning is constantly evolving at a fast pace. New techniques, tools and implementations are changing the field of Machine Learning and bringing excellent results.

  https://www.kdnuggets.com/2018/03/top-20-deep-learning-papers-2018.html

• A Day in the Life of a Data Scientist: Part 4

  Interested in what a data scientist does on a typical day of work? Each data science role may be different, but these contributors have insight to help those interested in figuring out what a day in the life of a data scientist actually looks like.

  https://www.kdnuggets.com/2018/04/day-life-data-scientist-part-4.html

• Is ReLU After Sigmoid Bad?

  Recently [we] were analyzing how different activation functions interact among themselves, and we found that using relu after sigmoid in the last two layers worsens the performance of the model.

  https://www.kdnuggets.com/2018/03/relu-after-sigmoid-bad.html

• Predictive and Preventive Maintenance

  Analytics is becoming important part of maintenance, with applications to analyzing part failures, using failure distributions to simulate product life, and determining the root cause of failures. We provide an overview of predictive maintenance, its usage and key issues to be considered.

  https://www.kdnuggets.com/2018/03/predictive-preventive-maintenance.html

• How StockTwits Applies Social and Sentiment Data Science

  StockTwits is a social network for investors and traders, giving them a platform to share assertions and perceptions, analyses and predictions.

  https://www.kdnuggets.com/2018/03/stocktwits-social-sentiment-data-science.html

• Text Processing in R

  There are good reasons to want to use R for text processing, namely that we can do it, and that we can fit it in with the rest of our analyses. Furthermore, there is a lot of very active development going on in the R text analysis community right now.

  https://www.kdnuggets.com/2018/03/text-processing-r.html

• Blockchains and APIs

  Major technological advances are providing opportunities for new business models, based on blockchain, which will see an increase in the number of connected devices in our day-to-day lives.

  https://www.kdnuggets.com/2018/03/blockchains-apis.html

• The Current Hype Cycle in Artificial Intelligence

  Over the past decade, the field of artificial intelligence (AI) has seen striking developments. As surveyed in, there now exist over twenty domains in which AI programs are performing at least as well as (if not better than) humans.

  https://www.kdnuggets.com/2018/02/current-hype-cycle-artificial-intelligence.html

• A Guide to Hiring Data Scientists

  This article provides a short overview of emerging data scientist types and their unique skillsets, as well as a guide for HR professionals and analytics managers who are looking to hire their first data scientists or build a data science team. Included are an overview of skills for each type and specific questions that can be asked to assess candidates.

  https://www.kdnuggets.com/2018/02/guide-hiring-data-scientists.html

• 5 Fantastic Practical Natural Language Processing Resources

  This post presents 5 practical resources for getting a start in natural language processing, covering a wide array of topics and approaches.

  https://www.kdnuggets.com/2018/02/5-fantastic-practical-natural-language-processing-resources.html

• Where AI is already rivaling humans

  Since 2011, AI hit hypergrowth, and researchers have created several AI solutions that are almost as good as – or better than – humans in several domains, including games, healthcare, computer vision and object recognition, speech to text conversion, speaker recognition, and improved robots and chat-bots for solving specific problems.

  https://www.kdnuggets.com/2018/02/domains-ai-rivaling-humans.html

• Supercharging Visualization with Apache Arrow

  Interactive visualization of large datasets on the web has traditionally been impractical. Apache Arrow provides a new way to exchange and visualize data at unprecedented speed and scale.

  https://www.kdnuggets.com/2018/01/supercharging-visualization-apache-arrow.html

• How Nonprofits Can Benefit from the Power of Data Science

  Nonprofits can use analytics to boost their fundraising efforts, measure and monitor the impact of their activities, build predictive models, optimize allocation of funds, and more

  https://www.kdnuggets.com/2018/01/nonprofits-data-science.html

• Building an Audio Classifier using Deep Neural Networks

  Using a deep convolutional neural network architecture to classify audio and how to effectively use transfer learning and data-augmentation to improve model accuracy using small datasets.

  https://www.kdnuggets.com/2017/12/audio-classifier-deep-neural-networks.html

• Transitioning to Data Science: How to become a data scientist, and how to create a data science team">Transitioning to Data Science: How to become a data scientist, and how to create a data science team

  "A good data scientist in my mind is the person that takes the science part in data science very seriously; a person who is able to find problems and solve them using statistics, machine learning, and distributed computing."

  https://www.kdnuggets.com/2017/12/transitioning-data-science-become-data-scientist-data-science-team.html

• Best Masters in Data Science and Analytics – Asia and Australia Edition

  The fourth edition of our comprehensive, unbiased survey on graduate degrees in Data Science and Analytics from around the world.

  https://www.kdnuggets.com/2017/12/best-masters-data-science-analytics-asia-australia.html

• Best Masters in Data Science and Analytics – Europe Edition

  The third part of our comprehensive, unbiased survey of graduate programs in Data Science and Analytics, examining the programs from Europe.

  https://www.kdnuggets.com/2017/12/best-masters-data-science-analytics-europe.html

• Big Data: Main Developments in 2017 and Key Trends in 2018">Big Data: Main Developments in 2017 and Key Trends in 2018

  As we bid farewell to one year and look to ring in another, KDnuggets has solicited opinions from numerous Big Data experts as to the most important developments of 2017 and their 2018 key trend predictions.

  https://www.kdnuggets.com/2017/12/big-data-main-developments-2017-key-trends-2018.html

• Understanding Objective Functions in Neural Networks

  This blog post is targeted towards people who have experience with machine learning, and want to get a better intuition on the different objective functions used to train neural networks.

  https://www.kdnuggets.com/2017/11/understanding-objective-functions-neural-networks.html

• Best Masters in Data Science and Analytics in US/Canada

  Second comprehensive list of master's degrees in the US and Canada with tuition information and duration.

  https://www.kdnuggets.com/2017/11/best-masters-data-science-analytics-us-canada.html

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

  https://www.kdnuggets.com/2017/11/best-online-masters-analytics-data-science.html

• Overview of GANs (Generative Adversarial Networks) – Part I

  A great introductory and high-level summary of Generative Adversarial Networks.

  https://www.kdnuggets.com/2017/11/overview-gans-generative-adversarial-networks-part1.html

• Neural Networks, Step 1: Where to Begin with Neural Nets & Deep Learning

  This is a short post for beginners learning neural networks, covering several essential neural networks concepts.

  https://www.kdnuggets.com/2017/10/neural-networks-step-1.html

• An Overview of 3 Popular Courses on Deep Learning">An Overview of 3 Popular Courses on Deep Learning

  After completing the 3 most popular MOOCS in deep learning from Fast.ai, deeplearning.ai/Coursera (which is not completely released) and Udacity, I believe a post about what you can expect from these 3 courses will be useful for future Deep learning enthusiasts.

  https://www.kdnuggets.com/2017/10/3-popular-courses-deep-learning.html

• Understanding Machine Learning Algorithms">Understanding Machine Learning Algorithms

  Machine learning algorithms aren’t difficult to grasp if you understand the basic concepts. Here, a SAS data scientist describes the foundations for some of today’s popular algorithms.

  https://www.kdnuggets.com/2017/10/understanding-machine-learning-algorithms.html

• Ensemble Learning to Improve Machine Learning Results

  Ensemble methods are meta-algorithms that combine several machine learning techniques into one predictive model in order to decrease variance (bagging), bias (boosting), or improve predictions (stacking).

  https://www.kdnuggets.com/2017/09/ensemble-learning-improve-machine-learning-results.html

• Machine Learning vs. Statistics: The Texas Death Match of Data Science">Machine Learning vs. Statistics: The Texas Death Match of Data Science

  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.

  https://www.kdnuggets.com/2017/08/machine-learning-vs-statistics.html

• 37 Reasons why your Neural Network is not working">37 Reasons why your Neural Network is not working

  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.

  https://www.kdnuggets.com/2017/08/37-reasons-neural-network-not-working.html

• Deep Learning and Neural Networks Primer: Basic Concepts for Beginners

  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.

  https://www.kdnuggets.com/2017/08/deep-learning-neural-networks-primer-basic-concepts-beginners.html

• First Steps of Learning Deep Learning: Image Classification in Keras

  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!

  https://www.kdnuggets.com/2017/08/first-steps-learning-deep-learning-image-classification-keras.html

• What Is Optimization And How Does It Benefit Business?

  Here we explain what Mathematical Optimisation is, and discuss how it can be applied in business and finance to make decisions.

  https://www.kdnuggets.com/2017/08/optimization-benefit-business.html

• Visualizing Convolutional Neural Networks with Open-source Picasso

  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?

  https://www.kdnuggets.com/2017/08/visualizing-convolutional-neural-networks-open-source-picasso.html

• How GDPR Affects Data Science">How GDPR Affects Data Science

  Coming European GDPR directive affects data science practice mainly in 3 areas: limits on data processing and consumer profiling, a “right to an explanation” for automated decision-making, and accountability for bias and discrimination in automated decisions.

  https://www.kdnuggets.com/2017/07/gdpr-affects-data-science.html

• Applying Deep Learning to Real-world Problems">Applying Deep Learning to Real-world Problems

  In this blog post I shared three learnings that are important to us at Merantix when applying deep learning to real-world problems. I hope that these ideas are helpful for other people who plan to use deep learning in their business.

  https://www.kdnuggets.com/2017/06/applying-deep-learning-real-world-problems.html

• Deep Learning Papers Reading Roadmap">Deep Learning Papers Reading Roadmap

  The roadmap is constructed in accordance with the following four guidelines: from outline to detail; from old to state-of-the-art; from generic to specific areas; focus on state-of-the-art.

  https://www.kdnuggets.com/2017/06/deep-learning-papers-reading-roadmap.html

• Is Regression Analysis Really Machine Learning?">Is Regression Analysis Really Machine Learning?

  What separates "traditional" applied statistics from machine learning? Is statistics the foundation on top of which machine learning is built? Is machine learning a superset of "traditional" statistics? Do these 2 concepts have a third unifying concept in common? So, in that vein... is regression analysis actually a form of machine learning?

  https://www.kdnuggets.com/2017/06/regression-analysis-really-machine-learning.html

• Deep Learning 101: Demystifying Tensors">Deep Learning 101: Demystifying Tensors

  Many deep-learning systems available today are based on tensor algebra, but tensor algebra isn’t tied to deep-learning. It isn’t hard to get started with tensor abuse but can be hard to stop.

  https://www.kdnuggets.com/2017/06/deep-learning-demystifying-tensors.html

• The Guerrilla Guide to Machine Learning with R">The Guerrilla Guide to Machine Learning with R

  This post is a lean look at learning machine learning with R. It is a complete, if very short, course for the quick study hacker with no time (or patience) to spare.

  https://www.kdnuggets.com/2017/05/guerrilla-guide-machine-learning-r.html

• Data Science & Machine Learning Platforms for the Enterprise

  A resilient Data Science Platform is a necessity to every centralized data science team within a large corporation. It helps them centralize, reuse, and productionize their models at peta scale.

  https://www.kdnuggets.com/2017/05/data-science-machine-learning-platforms-enterprise.html

• 42 Essential Quotes by Data Science Thought Leaders

  42 illuminating quotes you need to read if you’re a data scientist or considering a career in the field – insights from industry experts tackling the tough questions that every data scientist faces.

  https://www.kdnuggets.com/2017/05/42-essential-quotes-data-science-thought-leaders.html

• Awesome Deep Learning: Most Cited Deep Learning Papers">Awesome Deep Learning: Most Cited Deep Learning Papers

  This post introduces a curated list of the most cited deep learning papers (since 2012), provides the inclusion criteria, shares a few entry examples, and points to the full listing for those interested in investigating further.

  https://www.kdnuggets.com/2017/04/awesome-deep-learning-most-cited-papers.html

• What Makes a Good Analyst?

  Without doubt, critical thinking is necessary in order to be a good analyst but particular skills and experience are also required. What are some of these skills?

  https://www.kdnuggets.com/2017/04/gray-makes-good-analyst.html

• 10 Free Must-Read Books for Machine Learning and Data Science">10 Free Must-Read Books for Machine Learning and Data Science

  Spring. Rejuvenation. Rebirth. Everything’s blooming. And, of course, people want free ebooks. With that in mind, here's a list of 10 free machine learning and data science titles to get your spring reading started right.

  https://www.kdnuggets.com/2017/04/10-free-must-read-books-machine-learning-data-science.html

• Top 20 Recent Research Papers on Machine Learning and Deep Learning">Top 20 Recent Research Papers on Machine Learning and Deep Learning

  Machine learning and Deep Learning research advances are transforming our technology. Here are the 20 most important (most-cited) scientific papers that have been published since 2014, starting with "Dropout: a simple way to prevent neural networks from overfitting".

  https://www.kdnuggets.com/2017/04/top-20-papers-machine-learning.html

• What is Structural Equation Modeling?">What is Structural Equation Modeling?

  Structural Equation Modeling (SEM) is an extremely broad and flexible framework for data analysis, perhaps better thought of as a family of related methods rather than as a single technique. What is its relevance to Marketing Research?

  https://www.kdnuggets.com/2017/03/structural-equation-modeling.html

• How to think like a data scientist to become one

  The author went from securities analyst to Head of Data Science at Amazon. He describes what he learned in his journey and gives 4 useful rules based on his experience.

  https://www.kdnuggets.com/2017/03/think-like-data-scientist-become-one.html

• What Is Data Science, and What Does a Data Scientist Do?">What Is Data Science, and What Does a Data Scientist Do?

  This article is intended to help define the data scientist role, including typical skills, qualifications, education, experience, and responsibilities. This definition is somewhat loose, and given that the ideal experience and skill set is relatively rare to find in one individual.

  https://www.kdnuggets.com/2017/03/data-science-data-scientist-do.html

• Gartner Data Science Platforms – A Deeper Look

  Thomas Dinsmore critical examination of Gartner 2017 MQ of Data Science Platforms, including vendors who out, in, have big changes, Hadoop and Spark integration, open source software, and what Data Scientists actually use.

  https://www.kdnuggets.com/2017/03/thomaswdinsmore-gartner-data-science-platforms.html

• The Data Science Project Playbook">The Data Science Project Playbook

  Keep your development team from getting mired in high-complexity, low-return projects by following this practical playbook.

  https://www.kdnuggets.com/2017/03/data-science-project-playbook.html

• 7 More Steps to Mastering Machine Learning With Python">7 More Steps to Mastering Machine Learning With Python

  This post is a follow-up to last year's introductory Python machine learning post, which includes a series of tutorials for extending your knowledge beyond the original.
   
   

  https://www.kdnuggets.com/2017/03/seven-more-steps-machine-learning-python.html

• Introduction to Correlation

  Correlation is one of the most widely used (and widely misunderstood) statistical concepts. We provide the definitions and intuition behind several types of correlation and illustrate how to calculate correlation using the Python pandas library.

  https://www.kdnuggets.com/2017/02/datascience-introduction-correlation.html

• Getting Real World Results From Agile Data Science Teams

  In this post, I’ll look at the practical ingredients of managing agile data science. By using agile data science methods, we help data teams do fast and directed work, and manage the inherent uncertainty of data science and application development.

  https://www.kdnuggets.com/2017/02/real-world-results-agile-data-science-teams.html

• Regression Analysis: A Primer

  Despite the popularity of Regression, it is also misunderstood. Why? The answer might surprise you: There is no such thing as Regression. Rather, there are a large number of statistical methods that are called Regression, all of which are based on a shared statistical foundation.

  https://www.kdnuggets.com/2017/02/regression-analysis-primer.html

• An ode to the analytics grease monkeys

  Analytics is not one time job. It needs to be automated, deployed and improved for future business analytics requirements. Here an IBM expert discusses about development & deployment of analytics assets and capabilities of it.

  https://www.kdnuggets.com/2017/02/analytics-grease-monkeys.html

• Introduction to Forecasting with ARIMA in R

  ARIMA models are a popular and flexible class of forecasting model that utilize historical information to make predictions. In this tutorial, we walk through an example of examining time series for demand at a bike-sharing service, fitting an ARIMA model, and creating a basic forecast.

  https://www.kdnuggets.com/2017/01/datascience-introduction-forecasting-arima-r.html

• The Five Capability Levels of Deep Learning Intelligence

  Deep learning writer Carlos Perez gives his own classification for deep learning-based AI, which is aimed at practitioners. This classification gives us a sense of where we currently are and where we might be heading.

  https://www.kdnuggets.com/2016/12/5-capability-levels-deep-learning-intelligence.html

• Evaluating HTAP Databases for Machine Learning Applications

  Businesses are producing a greater number of intelligent applications; which traditional databases are unable to support. A new class of databases, Hybrid Transactional and Analytical Processing (HTAP) databases, offers a variety of capabilities with specific strengths and weaknesses to consider. This article aims to give application developers and data scientists a better understanding of the HTAP database ecosystem so they can make the right choice for their intelligent application.

  https://www.kdnuggets.com/2016/11/evaluating-htap-databases-machine-learning-applications.html

• 5 EBooks to Read Before Getting into A Machine Learning Career">5 EBooks to Read Before Getting into A Machine Learning Career

  A carefully-curated list of 5 free ebooks to help you better understand the various aspects of what machine learning, and skills necessary for a career in the field.

  https://www.kdnuggets.com/2016/10/5-free-ebooks-machine-learning-career.html

• 9 Key Deep Learning Papers, Explained">9 Key Deep Learning Papers, Explained

  If you are interested in understanding the current state of deep learning, this post outlines and thoroughly summarizes 9 of the most influential contemporary papers in the field.

  https://www.kdnuggets.com/2016/09/9-key-deep-learning-papers-explained.html

• A Beginner’s Guide To Understanding Convolutional Neural Networks Part 2

  This is the second part of a thorough introductory treatment of convolutional neural networks. Have a look after reading the first part.

  https://www.kdnuggets.com/2016/09/beginners-guide-understanding-convolutional-neural-networks-part-2.html

• Introducing Dask for Parallel Programming: An Interview with Project Lead Developer

  Introducing Dask, a flexible parallel computing library for analytics. Learn more about this project built with interactive data science in mind in an interview with its lead developer.

  https://www.kdnuggets.com/2016/09/introducing-dask-parallel-programming.html

• How to Become a Data Scientist – Part 1">How to Become a Data Scientist – Part 1

  Check out this excellent (and exhaustive) article on becoming a data scientist, written by someone who spends their day recruiting data scientists. Do yourself a favor and read the whole way through. You won't regret it!

  https://www.kdnuggets.com/2016/08/become-data-scientist-part-1.html

• Big Data Key Terms, Explained

  Just getting started with Big Data, or looking to iron out the wrinkles in your current understanding? Check out these 20 Big Data-related terms and their concise definitions.

  https://www.kdnuggets.com/2016/08/big-data-key-terms-explained.html

• And the Winner is… Stepwise Regression

  This post evaluates several methods for automating the feature selection process in large-scale linear regression models and show that for marketing applications the winner is Stepwise regression.

  https://www.kdnuggets.com/2016/08/winner-stepwise-regression.html

• 5 More Machine Learning Projects You Can No Longer Overlook

  There are a lot of popular machine learning projects out there, but many more that are not. Which of these are actively developed and worth checking out? Here is an offering of 5 such projects.

  https://www.kdnuggets.com/2016/06/five-more-machine-learning-projects-cant-overlook.html

• History of Data Mining

  Data mining is a subfield of computer science which blends many techniques from statistics, data science, database theory and machine learning. Here are the major milestones and “firsts” in the history of data mining plus how it’s evolved and blended with data science and big data.

  https://www.kdnuggets.com/2016/06/rayli-history-data-mining.html

• Apache Spark Key Terms, Explained

  An overview of 13 core Apache Spark concepts, presented with focus and clarity in mind. A great beginner's overview of essential Spark terminology.

  https://www.kdnuggets.com/2016/06/spark-key-terms-explained.html

• Are Deep Neural Networks Creative?

  Deep neural networks routinely generate images and synthesize text. But does this amount to creativity? Can we reasonably claim that deep learning produces art?

  https://www.kdnuggets.com/2016/05/deep-neural-networks-creative-deep-learning-art.html

• New Deep Learning Book Finished, Finalized Online Version Available

  What will likely become known as the seminal book on deep learning is finally finished, with the online version finalized and freely-accessible to all those interested in mastering deep neural networks.

  https://www.kdnuggets.com/2016/04/deep-learning-book-finished.html