Search results for deep learning

Found 2838 documents, 6021 searched:

• Cartoon: Thanksgiving, Big Data, and Turkey Data Science.

  A classic KDnuggets Thanksgiving cartoon examines the predicament of one group of fowl Data Scientists.

  https://www.kdnuggets.com/2018/11/cartoon-thanksgiving-turkey-data-science.html

• Using a Keras Long Short-Term Memory (LSTM) Model to Predict Stock Prices

  LSTMs are very powerful in sequence prediction problems because they’re able to store past information. This is important in our case because the previous price of a stock is crucial in predicting its future price.

  https://www.kdnuggets.com/2018/11/keras-long-short-term-memory-lstm-model-predict-stock-prices.html

• Word Morphing – an original idea

  In this post, we describe how to utilise word2vec's embeddings and A* search algorithm to morph between words.

  https://www.kdnuggets.com/2018/11/word-morphing-original-idea.html

• Anticipating the next move in data science – my interview with Thomson Reuters

  Like chess, Big Data is a combination of science, art and play; Gregory Piatetsky-Shapiro of KDnuggets helps data devotees discover winning moves - my Thomson Reuters interview.

  https://www.kdnuggets.com/2018/11/gps-anticipating-next-move-data-science.html

• Using Uncertainty to Interpret your Model

  We outline why you should care about uncertainty and discuss the different types, including model, data and measurement uncertainty and what different purposes these all serve.

  https://www.kdnuggets.com/2018/11/using-uncertainty-interpret-model.html

• [Download] Real-Life ML Examples + Notebooks

  In this eBook, we will walk you through four Machine Learning use cases on Databricks: Loan Risk Use Case; Advertising Analytics & Prediction Use Case; Market Basket Analysis Problem at Scale; Suspicious Behavior Identification in Video Use Case. Get your copy now!

  https://www.kdnuggets.com/2018/11/databricks-ebook-machine-learning-use-cases.html

• To get hired as a data scientist, don’t follow the herd">To get hired as a data scientist, don’t follow the herd

  Key tips, including advice on how to step out of your comfort zone and sometimes overlooked important skills that will impress employers. Check also the audio version with additional advice.

  https://www.kdnuggets.com/2018/11/get-hired-as-data-scientist.html

• The Long Tail of Medical Data

  This article discusses some issues related to medical data, relating specifically to power law distributions and computer aided diagnosis. Read on to see machine learning's place in the puzzle.

  https://www.kdnuggets.com/2018/11/long-tail-medical-data.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

• Building Surveillance System Using USB Camera and Wireless-Connected Raspberry Pi

  Read this post to learn how to build a surveillance system using a USB camera plugged into Raspberry Pi (RPi) which is connected a PC using its wireless interface.

  https://www.kdnuggets.com/2018/11/building-surveillance-system-usb-camera-wireless-connected-raspberry-pi.html

• Multi-Class Text Classification Model Comparison and Selection

  This is what we are going to do today: use everything that we have presented about text classification in the previous articles (and more) and comparing between the text classification models we trained in order to choose the most accurate one for our problem.

  https://www.kdnuggets.com/2018/11/multi-class-text-classification-model-comparison-selection.html

• Cartoon: Halloween Costume for Big Data.

  We revisit KDnuggets cartoon looking at the appropriate Halloween costume for Big Data and its companion, No Privacy.

  https://www.kdnuggets.com/2018/10/cartoon-halloween-big-data-no-privacy.html

• Naive Bayes from Scratch using Python only – No Fancy Frameworks

  We provide a complete step by step pythonic implementation of naive bayes, and by keeping in mind the mathematical & probabilistic difficulties we usually face when trying to dive deep in to the algorithmic insights of ML algorithms, this post should be ideal for beginners.

  https://www.kdnuggets.com/2018/10/naive-bayes-from-scratch-python.html

• Generative Adversarial Networks – Paper Reading Road Map

  To help the others who want to learn more about the technical sides of GANs, I wanted to share some papers I have read in the order that I read them.

  https://www.kdnuggets.com/2018/10/generative-adversarial-networks-paper-reading-road-map.html

• Apache Spark Introduction for Beginners">Apache Spark Introduction for Beginners

  An extensive introduction to Apache Spark, including a look at the evolution of the product, use cases, architecture, ecosystem components, core concepts and more.

  https://www.kdnuggets.com/2018/10/apache-spark-introduction-beginners.html

• The Main Approaches to Natural Language Processing Tasks">The Main Approaches to Natural Language Processing Tasks

  Let's have a look at the main approaches to NLP tasks that we have at our disposal. We will then have a look at the concrete NLP tasks we can tackle with said approaches.

  https://www.kdnuggets.com/2018/10/main-approaches-natural-language-processing-tasks.html

• Adversarial Examples, Explained

  Deep neural networks—the kind of machine learning models that have recently led to dramatic performance improvements in a wide range of applications—are vulnerable to tiny perturbations of their inputs. We investigate how to deal with these vulnerabilities.

  https://www.kdnuggets.com/2018/10/adversarial-examples-explained.html

• Using Confusion Matrices to Quantify the Cost of Being Wrong

  The terms ‘true condition’ (‘positive outcome’) and ‘predicted condition’ (‘negative outcome’) are used when discussing Confusion Matrices. This means that you need to understand the differences (and eventually the costs associated) with Type I and Type II Errors.

  https://www.kdnuggets.com/2018/10/confusion-matrices-quantify-cost-being-wrong.html

• Semantic Interoperability: Are you training your AI by mixing data sources that look the same but aren’t?

  Semantic interoperability is a challenge in AI systems, especially since data has become increasingly more complex. The other issue is that semantic interoperability may be compromised when people use the same system differently.

  https://www.kdnuggets.com/2018/10/semantic-interoperability-training-ai-mixing-different-data-sources.html

• Building an Image Classifier Running on Raspberry Pi

  The tutorial starts by building the Physical network connecting Raspberry Pi to the PC via a router. After preparing their IPv4 addresses, SSH session is created for remotely accessing of the Raspberry Pi. After uploading the classification project using FTP, clients can access it using web browsers for classifying images.

  https://www.kdnuggets.com/2018/10/building-image-classifier-running-raspberry-pi.html

• BIG, small or Right Data: Which is the proper focus?">BIG, small or Right Data: Which is the proper focus?

  For most businesses, having and using big data is either impossible, impractical, costly to justify, or difficult to outsource due to the over demand of qualified resources. So, what are the benefits of using small data?

  https://www.kdnuggets.com/2018/10/big-small-right-data.html

• Semantic Segmentation: Wiki, Applications and Resources

  An extensive overview covering the features of Semantic Segmentation and possible uses for it, including GeoSensing, Autonomous Drive, Facial Recognition and more.

  https://www.kdnuggets.com/2018/10/semantic-segmentation-wiki-applications-resources.html

• Linear Regression in the Wild

  We take a look at how to use linear regression when the dependent variables have measurement errors.

  https://www.kdnuggets.com/2018/10/linear-regression-wild.html

• Sequence Modeling with Neural Networks – Part I

  In the context of this post, we will focus on modeling sequences as a well-known data structure and will study its specific learning framework.

  https://www.kdnuggets.com/2018/10/sequence-modeling-neural-networks-part-1.html

• How to Create a Simple Neural Network in Python">How to Create a Simple Neural Network in Python

  The best way to understand how neural networks work is to create one yourself. This article will demonstrate how to do just that.

  https://www.kdnuggets.com/2018/10/simple-neural-network-python.html

• Raspberry Pi IoT Projects for Fun and Profit

  In this post, I will explain how to run an IoT project from the command line, without graphical interface, using Ubuntu Core in a Raspberry Pi 3.

  https://www.kdnuggets.com/2018/09/raspberry-pi-iot-projects-fun-profit.html

• Introducing VisualData: A Search Engine for Computer Vision Datasets

  Instead of building your own dataset, there already exists a rich collection of computer vision datasets contributed by academic researchers, hobbyists and companies.

  https://www.kdnuggets.com/2018/09/introducing-visualdata-search-engine-computer-vision-datasets.html

• Unfolding Naive Bayes From Scratch

  Whether you are a beginner in Machine Learning or you have been trying hard to understand the Super Natural Machine Learning Algorithms and you still feel that the dots do not connect somehow, this post is definitely for you!

  https://www.kdnuggets.com/2018/09/unfolding-naive-bayes.html

• Cartoon: Where AI achieves excellence

  We examine what can happen when lawyers are replaced with Machine Learning.

  https://www.kdnuggets.com/2018/09/cartoon-ai-lawyer.html

• Free resources to learn Natural Language Processing

  An extensive list of free resources to help you learn Natural Language Processing, including explanations on Text Classification, Sequence Labeling, Machine Translation and more.

  https://www.kdnuggets.com/2018/09/free-resources-natural-language-processing.html

• Ultimate Guide to Getting Started with TensorFlow">Ultimate Guide to Getting Started with TensorFlow

  Including video and written tutorials, beginner code examples, useful tricks, helpful communities, books, jobs and more - this is the ultimate guide to getting started with TensorFlow.

  https://www.kdnuggets.com/2018/09/ultimate-guide-tensorflow.html

• Data Science Cheat Sheet">Data Science Cheat Sheet

  Check out this new data science cheat sheet, a relatively broad undertaking at a novice depth of understanding, which concisely packs a wide array of diverse data science goodness into a 9 page treatment.

  https://www.kdnuggets.com/2018/09/meverick-lin-data-science-cheat-sheet.html

• Don’t Use Dropout in Convolutional Networks

  If you are wondering how to implement dropout, here is your answer - including an explanation on when to use dropout, an implementation example with Keras, batch normalization, and more.

  https://www.kdnuggets.com/2018/09/dropout-convolutional-networks.html

• Top Stories, Aug 27-Sep 2: Data Visualization Cheat Sheet; Topic Modeling with LSA, PLSA, LDA & lda2Vec

  Also: AI Knowledge Map: How To Classify AI Technologies; How to Make Your Machine Learning Models Robust to Outliers; Linear Regression In Real Life; 5 Data Science Projects That Will Get You Hired in 2018

  https://www.kdnuggets.com/2018/09/top-news-week-0827-0902.html

• Cartoon: Labor Day in the year 2050

  KDnuggets cartoon looks at how Labor Day can change in the year 2050.

  https://www.kdnuggets.com/2018/09/cartoon-labor-day-2050.html

• AI Knowledge Map: How To Classify AI Technologies">AI Knowledge Map: How To Classify AI Technologies

  What follows is then an effort to draw an architecture to access knowledge on AI and follow emergent dynamics, a gateway of pre-existing knowledge on the topic that will allow you to scout around for additional information and eventually create new knowledge on AI.

  https://www.kdnuggets.com/2018/08/ai-knowledge-map-classify-ai-technologies.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

• Are Vectorized Random Number Generators Actually Useful?

  I reported that you can multiply the speed of common (fast) random number generators such as PCG and xorshift128+ by a factor of three or four by vectorizing them using SIMD instructions. Is this actually useful in practice?

  https://www.kdnuggets.com/2018/08/vectorized-random-number-generators-actually-useful.html

• Emotion and Sentiment Analysis: A Practitioner’s Guide to NLP

  Sentiment analysis is widely used, especially as a part of social media analysis for any domain, be it a business, a recent movie, or a product launch, to understand its reception by the people and what they think of it based on their opinions or, you guessed it, sentiment!

  https://www.kdnuggets.com/2018/08/emotion-sentiment-analysis-practitioners-guide-nlp-5.html

• 9 Things You Should Know About TensorFlow

  A summary of the key points from the Google Cloud Next in San Francisco, "What’s New with TensorFlow?", including neural networks, TensorFlow Lite, data pipelines and more.

  https://www.kdnuggets.com/2018/08/9-things-tensorflow.html

• Named Entity Recognition: A Practitioner’s Guide to NLP

  Named entity recognition (NER) , also known as entity chunking/extraction , is a popular technique used in information extraction to identify and segment the named entities and classify or categorize them under various predefined classes.

  https://www.kdnuggets.com/2018/08/named-entity-recognition-practitioners-guide-nlp-4.html

• Project Hydrogen, new initiative based on Apache Spark to support AI and Data Science

  An introduction to Project Hydrogen: how it can assist machine learning and AI frameworks on Apache Spark and what distinguishes it from other open source projects.

  https://www.kdnuggets.com/2018/08/databricks-project-hydrogen-apache-spark.html

• A Crash Course in MXNet Tensor Basics & Simple Automatic Differentiation

  This is an overview of some basic functionality of the MXNet ndarray package for creating tensor-like objects, and using the autograd package for performing automatic differentiation.

  https://www.kdnuggets.com/2018/08/mxnet-tensor-basics-simple-derivatives.html

• Understanding Language Syntax and Structure: A Practitioner’s Guide to NLP">Understanding Language Syntax and Structure: A Practitioner’s Guide to NLP

  Knowledge about the structure and syntax of language is helpful in many areas like text processing, annotation, and parsing for further operations such as text classification or summarization.

  https://www.kdnuggets.com/2018/08/understanding-language-syntax-and-structure-practitioners-guide-nlp-3.html

• Top 10 roles in AI and data science">Top 10 roles in AI and data science

  When you think of the perfect data science team, are you imagining 10 copies of the same professor of computer science and statistics, hands delicately stained with whiteboard marker? We hope not!

  https://www.kdnuggets.com/2018/08/top-10-roles-ai-data-science.html

• Text Wrangling & Pre-processing: A Practitioner’s Guide to NLP

  I will highlight some of the most important steps which are used heavily in Natural Language Processing (NLP) pipelines and I frequently use them in my NLP projects.

  https://www.kdnuggets.com/2018/08/practitioners-guide-processing-understanding-text-2.html

• Remote Data Science: How to Send R and Python Execution to SQL Server from Jupyter Notebooks

  Did you know that you can execute R and Python code remotely in SQL Server from Jupyter Notebooks or any IDE? Machine Learning Services in SQL Server eliminates the need to move data around.

  https://www.kdnuggets.com/2018/07/r-python-execution-sql-server-jupyter.html

• Data Science For Business: 3 Reasons You Need To Learn The Expected Value Framework

  This article highlights the importance of learning the expected value framework in data science, covering classification, maximization and testing.

  https://www.kdnuggets.com/2018/07/data-science-business-expected-value-framework.html

• Data Retrieval with Web Scraping: A Practitioner’s Guide to NLP

  Proven and tested hands-on strategies to tackle NLP tasks.

  https://www.kdnuggets.com/2018/07/practitioners-guide-processing-understanding-text-1.html

• Genetic Algorithm Implementation in Python">Genetic Algorithm Implementation in Python

  This tutorial will implement the genetic algorithm optimization technique in Python based on a simple example in which we are trying to maximize the output of an equation.

  https://www.kdnuggets.com/2018/07/genetic-algorithm-implementation-python.html

• Comparison of Top 6 Python NLP Libraries">Comparison of Top 6 Python NLP Libraries

  Today, we want to outline and compare the most popular and helpful natural language processing libraries, based on our experience.

  https://www.kdnuggets.com/2018/07/comparison-top-6-python-nlp-libraries.html

• Basic Image Processing in Python, Part 2

  We explain how to easily access and manipulate the internal components of digital images using Python and give examples from satellite image processing.

  https://www.kdnuggets.com/2018/07/image-data-analysis-numpy-opencv-p2.html

• Beginners Ask “How Many Hidden Layers/Neurons to Use in Artificial Neural Networks?”">Beginners Ask “How Many Hidden Layers/Neurons to Use in Artificial Neural Networks?”

  By the end of this article, you could at least get the idea of how these questions are answered and be able to test yourself based on simple examples.

  https://www.kdnuggets.com/2018/07/beginners-ask-how-many-hidden-layers-neurons-neural-networks.html

• Cartoon: Data Scientist was the sexiest job of the 21st century until …">Cartoon: Data Scientist was the sexiest job of the 21st century until …

  This Data Scientist thought that he had the sexiest job of the 21st century until the arrival of the competition ...

  https://www.kdnuggets.com/2018/07/cartoon-data-scientist-sexiest-job-21st-century.html

• What is Minimum Viable (Data) Product?

  This post gives a personal insight into what Minimum Viable Product means for Machine Learning and the importance of starting small and iterating.

  https://www.kdnuggets.com/2018/07/minimum-viable-data-product.html

• Basic Image Data Analysis Using Numpy and OpenCV – Part 1

  Accessing the internal component of digital images using Python packages becomes more convenient to understand its properties as well as nature.

  https://www.kdnuggets.com/2018/07/basic-image-data-analysis-numpy-opencv-p1.html

• Introduction to Apache Spark

  This is the first blog in this series to analyze Big Data using Spark. It provides an introduction to Spark and its ecosystem.

  https://www.kdnuggets.com/2018/07/introduction-apache-spark.html

• Inside the Mind of a Neural Network with Interactive Code in Tensorflow

  Understand the inner workings of neural network models as this post covers three related topics: histogram of weights, visualizing the activation of neurons, and interior / integral gradients.

  https://www.kdnuggets.com/2018/06/inside-mind-neural-network-interactive-code-tensorflow.html

• What’s the Difference Between Data Integration and Data Engineering?

  Why is this distinction important? Because it’s critical to understanding how leading-organizations are investing in new data engineering skills that exploit advanced analytics to create new sources of business and operational value.

  https://www.kdnuggets.com/2018/06/difference-between-data-integration-data-engineering.html

• Top 20 Python Libraries for Data Science in 2018">Top 20 Python Libraries for Data Science in 2018

  Our selection actually contains more than 20 libraries, as some of them are alternatives to each other and solve the same problem. Therefore we have grouped them as it's difficult to distinguish one particular leader at the moment.

  https://www.kdnuggets.com/2018/06/top-20-python-libraries-data-science-2018.html

• 5 Data Science Projects That Will Get You Hired in 2018">5 Data Science Projects That Will Get You Hired in 2018

  A portfolio of real-world projects is the best way to break into data science. This article highlights the 5 types of projects that will help land you a job and improve your career.

  https://www.kdnuggets.com/2018/06/5-data-science-projects-hired.html

• Why Data Scientists Love Gaussian

  Gaussian distribution model, often identified with its iconic bell shaped curve, also referred as Normal distribution, is so popular mainly because of three reasons.

  https://www.kdnuggets.com/2018/06/why-data-scientists-love-gaussian.html

• Batch Normalization in Neural Networks

  This article explains batch normalization in a simple way. I wrote this article after what I learned from Fast.ai and deeplearning.ai.

  https://www.kdnuggets.com/2018/06/batch-normalization-neural-networks.html

• An Intuitive Introduction to Gradient Descent

  This post provides a good introduction to Gradient Descent, covering the intuition, variants and choosing the learning rate.

  https://www.kdnuggets.com/2018/06/intuitive-introduction-gradient-descent.html

• Data Science Predicting The Future

  In this article we will expand on the knowledge learnt from the last article - The What, Where and How of Data for Data Science - and consider how data science is applied to predict the future.

  https://www.kdnuggets.com/2018/06/data-science-predicting-future.html

• Natural Language Processing Nuggets: Getting Started with NLP

  Check out this collection of NLP resources for beginners, starting from zero and slowly progressing to the point that readers should have an idea of where to go next.

  https://www.kdnuggets.com/2018/06/getting-started-natural-language-processing.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

• The What, Where and How of Data for Data Science">The What, Where and How of Data for Data Science

  Here we will take data science apart and build it back up to a coherent and manageable concept. Bear with us!

  https://www.kdnuggets.com/2018/06/what-where-how-data-science.html

• Packaging and Distributing Your Python Project to PyPI for Installation Using pip

  This tutorial will explain the steps required to package your Python projects, distribute them in distribution formats using steptools, upload them into the Python Package Index (PyPI) repository using twine, and finally installation using Python installers such as pip and conda.

  https://www.kdnuggets.com/2018/06/packaging-distributing-python-project-pypi-pip.html

• The Book of Why

  Judea Pearl has made noteworthy contributions to artificial intelligence, Bayesian networks, and causal analysis. These achievements notwithstanding, Pearl holds some views many statisticians may find odd or exaggerated.

  https://www.kdnuggets.com/2018/06/gray-pearl-book-of-why.html

• On the contribution of neural networks and word embeddings in Natural Language Processing

  In this post I will try to explain, in a very simplified way, how to apply neural networks and integrate word embeddings in text-based applications, and some of the main implicit benefits of using neural networks and word embeddings in NLP.

  https://www.kdnuggets.com/2018/05/contribution-neural-networks-word-embeddings-natural-language-processing.html

• Cartoon: GDPR first effect on Privacy

  New KDnuggets Cartoon examines the first unexpected effect of GDPR on Privacy.

  https://www.kdnuggets.com/2018/05/cartoon-gdpr-first-effect-privacy.html

• Improving the Performance of a Neural Network

  There are many techniques available that could help us achieve that. Follow along to get to know them and to build your own accurate neural network.

  https://www.kdnuggets.com/2018/05/improving-performance-neural-network.html

• 6 Tips for Effective Visualization with Tableau

  We analyse principles for effective data visualization in Tableau, including color gradients, avoiding crowded dashboards, Tableau marks and more.

  https://www.kdnuggets.com/2018/05/6-tips-effective-visualization-tableau.html

• Data Science: 4 Reasons Why Most Are Failing to Deliver

  Data Science: Some see billions in returns, but most are failing to deliver. This article explores some of the reasons why this is the case.

  https://www.kdnuggets.com/2018/05/data-science-4-reasons-failing-deliver.html

• If chatbots are to succeed, they need this

  Can logic be used to make chatbots intelligent? In the 1960s this was taken for granted. Now we have all but forgotten the logical approach. Is it time for a revival?

  https://www.kdnuggets.com/2018/05/chatbots-succeed-need-logic.html

• How to Implement a YOLO (v3) Object Detector from Scratch in PyTorch: Part 1">How to Implement a YOLO (v3) Object Detector from Scratch in PyTorch: Part 1

  The best way to go about learning object detection is to implement the algorithms by yourself, from scratch. This is exactly what we'll do in this tutorial.

  https://www.kdnuggets.com/2018/05/implement-yolo-v3-object-detector-pytorch-part-1.html

• GANs in TensorFlow from the Command Line: Creating Your First GitHub Project

  In this article I will present the steps to create your first GitHub Project. I will use as an example Generative Adversarial Networks.

  https://www.kdnuggets.com/2018/05/zimbres-first-github-project-gans.html

• THE BOOK OF WHY: The New Science of Cause and Effect

  A Turing Prize-winning computer scientist and statistician shows how understanding causality has revolutionized science and will revolutionize AI.

  https://www.kdnuggets.com/2018/05/pearl-book-science-cause-effect.html

• Complete Guide to Build ConvNet HTTP-Based Application using TensorFlow and Flask RESTful Python API">Complete Guide to Build ConvNet HTTP-Based Application using TensorFlow and Flask RESTful Python API

  In this tutorial, a CNN is to be built, and trained and tested against the CIFAR10 dataset. To make the model remotely accessible, a Flask Web application is created using Python to receive an uploaded image and return its classification label using HTTP.

  https://www.kdnuggets.com/2018/05/complete-guide-convnet-tensorflow-flask-restful-python-api.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

• Apache Spark : Python vs. Scala">Apache Spark : Python vs. Scala

  When it comes to using the Apache Spark framework, the data science community is divided in two camps; one which prefers Scala whereas the other preferring Python. This article compares the two, listing their pros and cons.

  https://www.kdnuggets.com/2018/05/apache-spark-python-scala.html

• To Kaggle Or Not

  Kaggle is the most well known competition platform for predictive modeling and analytics. This article looks into the different aspects of Kaggle and the benefits it can bring to data scientists.

  https://www.kdnuggets.com/2018/05/to-kaggle-or-not.html

• Getting Started with spaCy for Natural Language Processing

  spaCy is a Python natural language processing library specifically designed with the goal of being a useful library for implementing production-ready systems. It is particularly fast and intuitive, making it a top contender for NLP tasks.

  https://www.kdnuggets.com/2018/05/getting-started-spacy-natural-language-processing.html

• How to Make AI More Accessible

  I recently was a guest speaker at the Stanford AI Salon on the topic of accessiblity in AI, which included a free-ranging discussion among assembled members of the Stanford AI Lab. There were a number of interesting questions and topics, so I thought I would share a few of my answers here.

  https://www.kdnuggets.com/2018/04/make-ai-more-accessible.html

• Building Convolutional Neural Network using NumPy from Scratch">Building Convolutional Neural Network using NumPy from Scratch

  In this article, CNN is created using only NumPy library. Just three layers are created which are convolution (conv for short), ReLU, and max pooling.

  https://www.kdnuggets.com/2018/04/building-convolutional-neural-network-numpy-scratch.html

• Data Science Interview Guide

  Traditionally, Data Science would focus on mathematics, computer science and domain expertise. While I will briefly cover some computer science fundamentals, the bulk of this blog will mostly cover the mathematical basics one might either need to brush up on (or even take an entire course).

  https://www.kdnuggets.com/2018/04/data-science-interview-guide.html

• Python Regular Expressions Cheat Sheet">Python Regular Expressions Cheat Sheet

  The tough thing about learning data is remembering all the syntax. While at Dataquest we advocate getting used to consulting the Python documentation, sometimes it's nice to have a handy reference, so we've put together this cheat sheet to help you out!

  https://www.kdnuggets.com/2018/04/python-regular-expressions-cheat-sheet.html

• Derivation of Convolutional Neural Network from Fully Connected Network Step-By-Step

  What are the advantages of ConvNets over FC networks in image analysis? How is ConvNet derived from FC networks? Where the term convolution in CNNs came from? These questions are to be answered in this article.

  https://www.kdnuggets.com/2018/04/derivation-convolutional-neural-network-fully-connected-step-by-step.html

• Key Algorithms and Statistical Models for Aspiring Data Scientists">Key Algorithms and Statistical Models for Aspiring Data Scientists

  This article provides a summary of key algorithms and statistical techniques commonly used in industry, along with a short resource related to these techniques.

  https://www.kdnuggets.com/2018/04/key-algorithms-statistical-models-aspiring-data-scientists.html

• Top 10 Technology Trends of 2018">Top 10 Technology Trends of 2018

  In this article, we will focus on the modern trends that took off well on the market by the end of 2017 and discuss the major breakthroughs expected in 2018.

  https://www.kdnuggets.com/2018/04/top-10-technology-trends-2018.html

• Understanding What is Behind Sentiment Analysis – Part 1

  Build your first sentiment classifier in 3 steps.

  https://www.kdnuggets.com/2018/04/understanding-behind-sentiment-analysis-part-1.html

• Why You Should Start Using .npy Files More Often

  In this article, we demonstrate the utility of using native NumPy file format .npy over CSV for reading large numerical data set. It may be an useful trick if the same CSV data file needs to be read many times.

  https://www.kdnuggets.com/2018/04/start-using-npy-files-more-often.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

• Semantic Segmentation Models for Autonomous Vehicles

  State-of-the-art Semantic Segmentation models need to be tuned for efficient memory consumption and fps output to be used in time-sensitive domains like autonomous vehicles.

  https://www.kdnuggets.com/2018/03/semantic-segmentation-models-autonomous-vehicles.html

• Using Tensorflow Object Detection to do Pixel Wise Classification

  Tensorflow recently added new functionality and now we can extend the API to determine pixel by pixel location of objects of interest. So when would we need this extra granularity?

  https://www.kdnuggets.com/2018/03/tensorflow-object-detection-pixel-wise-classification.html

• Cartoon: AI Masters the March Madness

  AI has mastered chess, Go, and other games, but can AI master March Madness? KDnuggets Cartoon imagines one scenario when this happens.

  https://www.kdnuggets.com/2018/03/cartoon-ai-march-madness.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

• Ranking Popular Distributed Computing Packages for Data Science

  We examined 140 frameworks and distributed programing packages and came up with a list of top 20 distributed computing packages useful for Data Science, based on a combination of Github, Stack Overflow, and Google results.

  https://www.kdnuggets.com/2018/03/top-distributed-computing-packages-data-science.html

• Creating a simple text classifier using Google CoLaboratory

  Google CoLaboratory is Google’s latest contribution to AI, wherein users can code in Python using a Chrome browser in a Jupyter-like environment. In this article I have shared a method, and code, to create a simple binary text classifier using Scikit Learn within Google CoLaboratory environment.

  https://www.kdnuggets.com/2018/03/simple-text-classifier-google-colaboratory.html

• Introduction to Optimization with Genetic Algorithm">Introduction to Optimization with Genetic Algorithm

  This article gives a brief introduction about evolutionary algorithms (EAs) and describes genetic algorithm (GA) which is one of the simplest random-based EAs.

  https://www.kdnuggets.com/2018/03/introduction-optimization-with-genetic-algorithm.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

• Great Data Scientists Don’t Just Think Outside the Box, They Redefine the Box

  The best data scientists have strong imaginative skills for not just “thinking outside the box” – but actually redefining the box – in trying to find variables and metrics that might be better predictors of performance.

  https://www.kdnuggets.com/2018/03/great-data-scientists-think-outside-redefine-box.html

• Is Google Tensorflow Object Detection API the Easiest Way to Implement Image Recognition?

  There are many different ways to do image recognition. Google recently released a new Tensorflow Object Detection API to give computer vision everywhere a boost.

  https://www.kdnuggets.com/2018/03/google-tensorflow-object-detection-api-the-easiest-way-implement-image-recognition.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

• 5 Things You Need To Know About Data Science

  Here are 5 useful things to know about Data Science, including its relationship to BI, Data Mining, Predictive Analytics, and Machine Learning; Data Scientist job prospects; where to learn Data Science; and which algorithms/methods are used by Data Scientists

  https://www.kdnuggets.com/2018/02/5-things-about-data-science.html

• Resurgence of AI During 1983-2010

  We discuss supervised learning, unsupervised learning and reinforcement learning, neural networks, and 6 reasons that helped AI Research and Development to move ahead.

  https://www.kdnuggets.com/2018/02/resurgence-ai-1983-2010.html

• Neural network AI is simple. So… Stop pretending you are a genius">Neural network AI is simple. So… Stop pretending you are a genius

  This post may come off as a rant, but that’s not so much its intent, as it is to point out why we went from having very few AI experts, to having so many in so little time.

  https://www.kdnuggets.com/2018/02/neural-network-ai-simple-genius.html

• The Birth of AI and The First AI Hype Cycle

  A dazzling review of AI History, from Alan Turing and Turing Test, to Simon and Newell and Logic Theorist, to Marvin Minsky and Perceptron, birth of Rule-based systems and Machine Learning, Eliza - first chatbot, Robotics, and the bust which led to first AI Winter.

  https://www.kdnuggets.com/2018/02/birth-ai-first-hype-cycle.html

• Top 15 Scala Libraries for Data Science in 2018

  For your convenience, we have prepared a comprehensive overview of the most important libraries used to perform machine learning and Data Science tasks in Scala.

  https://www.kdnuggets.com/2018/02/top-15-scala-libraries-data-science-2018.html

• Fast.ai Lesson 1 on Google Colab (Free GPU)

  In this post, I will demonstrate how to use Google Colab for fastai. You can use GPU as a backend for free for 12 hours at a time. GPU compute for free? Are you kidding me?

  https://www.kdnuggets.com/2018/02/fast-ai-lesson-1-google-colab-free-gpu.html

• A Simple Starter Guide to Build a Neural Network">A Simple Starter Guide to Build a Neural Network

  This guide serves as a basic hands-on work to lead you through building a neural network from scratch. Most of the mathematical concepts and scientific decisions are left out.

  https://www.kdnuggets.com/2018/02/simple-starter-guide-build-neural-network.html

• Error Analysis to your Rescue – Lessons from Andrew Ng, part 3

  The last entry in a series of posts about Andrew Ng's lessons on strategies to follow when fixing errors in your algorithm

  https://www.kdnuggets.com/2018/01/error-analysis-your-rescue.html

• Four Big Data Trends for 2018

  Curious about the future of Big Data and AI? Here’s what the trends have it in 2018 for innovations.

  https://www.kdnuggets.com/2018/01/four-big-data-trends-2018.html

• Training and Visualising Word Vectors

  In this tutorial I want to show how you can implement a skip gram model in tensorflow to generate word vectors for any text you are working with and then use tensorboard to visualize them.

  https://www.kdnuggets.com/2018/01/training-visualising-word-vectors.html

• Gradient Boosting in TensorFlow vs XGBoost

  For many Kaggle-style data mining problems, XGBoost has been the go-to solution since its release in 2016. It's probably as close to an out-of-the-box machine learning algorithm as you can get today.

  https://www.kdnuggets.com/2018/01/gradient-boosting-tensorflow-vs-xgboost.html

• A Day in the Life of an AI Developer">A Day in the Life of an AI Developer

  This is the narrative of a typical AI Sunday, where I decided to look at building a sequence to sequence (seq2seq) model based chatbot using some already available sample code and data from the Cornell movie database.

  https://www.kdnuggets.com/2018/01/day-life-ai-developer.html

• Becoming a Data Scientist">Becoming a Data Scientist

  This article contains a lot of links to resources that I think are very helpful in getting you started to "think like a data scientist" which in my opinion is the most important step of the transition. I hope that you find this useful.

  https://www.kdnuggets.com/2018/01/feizpour-becoming-data-scientist.html

• Custom Optimizer in TensorFlow

  How to customize the optimizers to speed-up and improve the process of finding a (local) minimum of the loss function using TensorFlow.

  https://www.kdnuggets.com/2018/01/custom-optimizer-tensorflow.html

• Cartoon: AI at Home: How Far Can A Smart Device Go?

  New KDnuggets cartoon looks at AI at Home technology and considers how a novel way how a smart device can help its owner to lose weight.

  https://www.kdnuggets.com/2018/01/cartoon-ai-at-home.html

• Artificial General Intelligence (AGI) in less than 50 years, say KDnuggets readers

  Artificial General Intelligence (AGI) will likely be achieved in less than 50 years, according to latest KDnuggets Poll. The median estimate from all regions was 21-50 years, except in Asia where AGI is expected in 11-20 years.

  https://www.kdnuggets.com/2018/01/poll-agi-50-years.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 to build a Successful Advanced Analytics Department">How to build a Successful Advanced Analytics Department

  This article presents our opinions and suggestions on how an Advanced Analytics department should operate. We hope this will be useful for those who want to implement analytics work in their company, as well as for existing departments.

  https://www.kdnuggets.com/2018/01/build-successful-advanced-analytics-department.html

• Docker for Data Science">Docker for Data Science

  Coming from a statistics background I used to care very little about how to install software and would occasionally spend a few days trying to resolve system configuration issues. Enter the god-send Docker almighty.

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

• NIPS 2017 Key Points & Summary Notes

  Third year Ph.D student David Abel, of Brown University, was in attendance at NIP 2017, and he labouriously compiled and formatted a fantastic 43-page set of notes for the rest of us. Get them here.

  https://www.kdnuggets.com/2017/12/nips-2017-key-points-summary-notes.html

• Cartoon: AI and Technology Transforming Christmas?

  New KDnuggets cartoon looks at how AI and the new technology can transform Christmas.

  https://www.kdnuggets.com/2017/12/cartoon-ai-transforming-christmas.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