Search results for Long Short Term Memory Network

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• NLP and Computer Vision Integrated">NLP and Computer Vision Integrated

  Computer vision and NLP developed as separate fields, and researchers are now combining these tasks to solve long-standing problems across multiple disciplines.

  https://www.kdnuggets.com/2019/06/nlp-computer-vision-integrated.html

• Clearing air around “Boosting”

  We explain the reasoning behind the massive success of boosting algorithms, how it came to be and what we can expect from them in the future.

  https://www.kdnuggets.com/2019/06/clearing-air-around-boosting.html

• The Hitchhiker’s Guide to Feature Extraction

  Check out this collection of tricks and code for Kaggle and everyday work.

  https://www.kdnuggets.com/2019/06/hitchhikers-guide-feature-extraction.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

• Customer Churn Prediction Using Machine Learning: Main Approaches and Models

  We reach out to experts from HubSpot and ScienceSoft to discuss how SaaS companies handle the problem of customer churn prediction using Machine Learning.

  https://www.kdnuggets.com/2019/05/churn-prediction-machine-learning.html

• How (not) to use Machine Learning for time series forecasting: Avoiding the pitfalls">How (not) to use Machine Learning for time series forecasting: Avoiding the pitfalls

  We outline some of the common pitfalls of machine learning for time series forecasting, with a look at time delayed predictions, autocorrelations, stationarity, accuracy metrics, and more.

  https://www.kdnuggets.com/2019/05/machine-learning-time-series-forecasting.html

• Getting started with NLP using the PyTorch framework

  We discuss the classes that PyTorch provides for helping with Natural Language Processing (NLP) and how they can be used for related tasks using recurrent layers.

  https://www.kdnuggets.com/2019/04/nlp-pytorch.html

• OpenAI’s GPT-2: the model, the hype, and the controversy

  OpenAI recently released a very large language model called GPT-2. Controversially, they decided not to release the data or the parameters of their biggest model, citing concerns about potential abuse. Read this researcher's take on the issue.

  https://www.kdnuggets.com/2019/03/openai-gpt-2-model-hype-controversy.html

• Comparing MobileNet Models in TensorFlow

  MobileNets are a family of mobile-first computer vision models for TensorFlow, designed to effectively maximize accuracy while being mindful of the restricted resources for an on-device or embedded application.

  https://www.kdnuggets.com/2019/03/comparing-mobilenet-models-tensorflow.html

• Data-science? Agile? Cycles? My method for managing data-science projects in the Hi-tech industry.

  The following is a method I developed, which is based on my personal experience managing a data-science-research team and was tested with multiple projects. In the next sections, I’ll review the different types of research from a time point-of-view, compare development and research workflow approaches and finally suggest my work methodology.

  https://www.kdnuggets.com/2019/02/data-science-agile-cycles-method-managing-projects-hi-tech-industry.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

• How to Engineer Your Way Out of Slow Models

  We describe how we handle performance issues with our deep learning models, including how to find subgraphs that take a lot of calculation time and how to extract these into a caching mechanism.

  https://www.kdnuggets.com/2018/11/engineer-slow-models.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

• Top 13 Python Deep Learning Libraries">Top 13 Python Deep Learning Libraries

  Part 2 of a new series investigating the top Python Libraries across Machine Learning, AI, Deep Learning and Data Science.

  https://www.kdnuggets.com/2018/11/top-python-deep-learning-libraries.html

• Introduction to Active Learning

  An extensive overview of Active Learning, with an explanation into how it works and can assist with data labeling, as well as its performance and potential limitations.

  https://www.kdnuggets.com/2018/10/introduction-active-learning.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

• Introduction to Deep Learning

  I decided to begin to put some structure in my understanding of Neural Networks through this series of articles.

  https://www.kdnuggets.com/2018/09/introduction-deep-learning.html

• Deep Learning on the Edge

  Detailed analysis into utilizing deep learning on the edge, covering both advantages and disadvantages and comparing this against more traditional cloud computing methods.

  https://www.kdnuggets.com/2018/09/deep-learning-edge.html

• Deep Learning for NLP: An Overview of Recent Trends">Deep Learning for NLP: An Overview of Recent Trends

  A new paper discusses some of the recent trends in deep learning based natural language processing (NLP) systems and applications. The focus is on the review and comparison of models and methods that have achieved state-of-the-art (SOTA) results on various NLP tasks and some of the current best practices for applying deep learning in NLP.

  https://www.kdnuggets.com/2018/09/deep-learning-nlp-overview-recent-trends.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

• DevOps for Data Scientists: Taming the Unicorn

  How do we version control the model and add it to an app? How will people interact with our website based on the outcome? How will it scale!?

  https://www.kdnuggets.com/2018/07/devops-data-scientists-taming-unicorn.html

• fast.ai Deep Learning Part 1 Complete Course Notes

  This posts is a collection of a set of fantastic notes on the fast.ai deep learning part 1 MOOC freely available online, as written and shared by a student. These notes are a valuable learning resource either as a supplement to the courseware or on their own.

  https://www.kdnuggets.com/2018/07/fast-ai-deep-learning-part-1-notes.html

• Overview and benchmark of traditional and deep learning models in text classification

  In this post, traditional and deep learning models in text classification will be thoroughly investigated, including a discussion into both Recurrent and Convolutional neural networks.

  https://www.kdnuggets.com/2018/07/overview-benchmark-deep-learning-models-text-classification.html

• 9 Must-have skills you need to become a Data Scientist, updated">9 Must-have skills you need to become a Data Scientist, updated

  Check out this collection of 9 (plus some additional freebies) must-have skills for becoming a data scientist.

  https://www.kdnuggets.com/2018/05/simplilearn-9-must-have-skills-data-scientist.html

• 50+ Useful Machine Learning & Prediction APIs, 2018 Edition">50+ Useful Machine Learning & Prediction APIs, 2018 Edition

  Extensive list of 50+ APIs in Face and Image Recognition ,Text Analysis, NLP, Sentiment Analysis, Language Translation, Machine Learning and prediction.

  https://www.kdnuggets.com/2018/05/50-useful-machine-learning-prediction-apis-2018-edition.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

• Getting Started with PyTorch Part 1: Understanding How Automatic Differentiation Works

  PyTorch has emerged as a major contender in the race to be the king of deep learning frameworks. What makes it really luring is it’s dynamic computation graph paradigm.

  https://www.kdnuggets.com/2018/04/getting-started-pytorch-understanding-automatic-differentiation.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

• 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

• 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

• 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

• 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

• Getting Started with TensorFlow: A Machine Learning Tutorial

  A complete and rigorous introduction to Tensorflow. Code along with this tutorial to get started with hands-on examples.

  https://www.kdnuggets.com/2017/12/getting-started-tensorflow.html

• The 10 Deep Learning Methods AI Practitioners Need to Apply

  Deep learning emerged from that decade’s explosive computational growth as a serious contender in the field, winning many important machine learning competitions. The interest has not cooled as of 2017; today, we see deep learning mentioned in every corner of machine learning.

  https://www.kdnuggets.com/2017/12/10-deep-learning-methods-ai-practitioners-need-apply.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

• A Framework for Approaching Textual Data Science Tasks">A Framework for Approaching Textual Data Science Tasks

  Although NLP and text mining are not the same thing, they are closely related, deal with the same raw data type, and have some crossover in their uses. Let's discuss the steps in approaching these types of tasks.

  https://www.kdnuggets.com/2017/11/framework-approaching-textual-data-tasks.html

• 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.

  https://www.kdnuggets.com/2017/11/getting-started-text-mining-r-python.html

• 7 Steps to Mastering Deep Learning with Keras">7 Steps to Mastering Deep Learning with Keras

  Are you interested in learning how to use Keras? Do you already have an understanding of how neural networks work? Check out this lean, fat-free 7 step plan for going from Keras newbie to master of its basics as quickly as is possible.

  https://www.kdnuggets.com/2017/10/seven-steps-deep-learning-keras.html

• Visualizing High Dimensional Data In Augmented Reality

  When Data Scientists first get a data set, they oftne use a matrix of 2D scatter plots to quickly see the contents and relationships between pairs of attributes. But for data with lots of attributes, such analysis does not scale.

  https://www.kdnuggets.com/2017/09/ibm-visualizing-high-dimensional-data-augmented-reality.html

• Machine Learning Translation and the Google Translate Algorithm

  Today, we’ve decided to explore machine translators and explain how the Google Translate algorithm works.

  https://www.kdnuggets.com/2017/09/machine-learning-translation-google-translate-algorithm.html

• New-Age Machine Learning Algorithms in Retail Lending">New-Age Machine Learning Algorithms in Retail Lending

  We review the application of new age Machine Learning algorithms for better Customer Analytics in Lending and Credit Risk Assessment.

  https://www.kdnuggets.com/2017/09/machine-learning-algorithms-lending.html

• AI and Deep Learning, Explained Simply">AI and Deep Learning, Explained Simply

  AI can now see, hear, and even bluff better than most people. We look into what is new and real about AI and Deep Learning, and what is hype or misinformation.
   

  https://www.kdnuggets.com/2017/07/ai-deep-learning-explained-simply.html

• 5 Free Resources for Getting Started with Deep Learning for Natural Language Processing">5 Free Resources for Getting Started with Deep Learning for Natural Language Processing

  This is a collection of 5 deep learning for natural language processing resources for the uninitiated, intended to open eyes to what is possible and to the current state of the art at the intersection of NLP and deep learning. It should also provide some idea of where to go next.

  https://www.kdnuggets.com/2017/07/5-free-resources-getting-started-deep-learning-nlp.html

• What Are Artificial Intelligence, Machine Learning, and Deep Learning?">What Are Artificial Intelligence, Machine Learning, and Deep Learning?

  AI and Machine Learning have become mainstream, and people know shockingly little about it. Here is an explainer and useful references.

  https://www.kdnuggets.com/2017/07/rapidminer-ai-machine-learning-deep-learning.html

• Apache Flink: The Next Distributed Data Processing Revolution?">Apache Flink: The Next Distributed Data Processing Revolution?

  Will Apache Flink displace Apache Spark as the new champion of Big Data Processing? We compare Spark and Apache Flink performance for batch processing and stream processing.

  https://www.kdnuggets.com/2017/07/apache-flink-distributed-data-processing-revolution.html

• Time Series Analysis with Generalized Additive Models

  In this tutorial, we will see an example of how a Generative Additive Model (GAM) is used, learn how functions in a GAM are identified through backfitting, and learn how to validate a time series model.

  https://www.kdnuggets.com/2017/04/time-series-analysis-generalized-additive-models.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

• Deep Learning Research Review: Natural Language Processing">Deep Learning Research Review: Natural Language Processing

  This edition of Deep Learning Research Review explains recent research papers in Natural Language Processing (NLP). If you don't have the time to read the top papers yourself, or need an overview of NLP with Deep Learning, this post is for you.

  https://www.kdnuggets.com/2017/01/deep-learning-review-natural-language-processing.html

• Artificial Intelligence and Speech Recognition for Chatbots: A Primer

  Bot bots bots... Read this overview of how artificial intelligence and natural language processing are contributing to chatbot development, and where it all goes from here.

  https://www.kdnuggets.com/2017/01/artificial-intelligence-speech-recognition-chatbots-primer.html

• 6 areas of AI and Machine Learning to watch closely">6 areas of AI and Machine Learning to watch closely

  Artificial Intelligence is a generic term and many fields of science overlaps when comes to make an AI application. Here is an explanation of AI and its 6 major areas to be focused, going forward.

  https://www.kdnuggets.com/2017/01/6-areas-ai-machine-learning.html

• Why the Data Scientist and Data Engineer Need to Understand Virtualization in the Cloud

  This article covers the value of understanding the virtualization constructs for the data scientist and data engineer as they deploy their analysis onto all kinds of cloud platforms. Virtualization is a key enabling layer of software for these data workers to be aware of and to achieve optimal results from.

  https://www.kdnuggets.com/2017/01/data-scientist-engineer-understand-virtualization-cloud.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

• The Human Vector: Incorporate Speaker Embeddings to Make Your Bot More Powerful

  One of the many ways in which bots can fail is by their (lack of) persona. Learn how speaker embeddings can help with this problem, and can help improve the persona of your bot.

  https://www.kdnuggets.com/2016/09/human-vector-incorporate-speaker-embedding-powerful-bot.html

• 35 Open Source tools for Internet of Things

  If you have heard about the Internet of Things many times by now, its time to join the conversation. Explore the many open source tools & projects related to Internet of Things.

  https://www.kdnuggets.com/2016/07/open-source-tools-internet-things.html

• Deep Learning for Chatbots, Part 1 – Introduction

  The first in a series of tutorial posts on using Deep Learning for chatbots, this covers some of the techniques being used to build conversational agents, and goes from the current state of affairs through to what is and is not possible.

  https://www.kdnuggets.com/2016/04/deep-learning-chatbots-part-1.html

• Research Leaders on Data Mining, Data Science and Big Data key advances, top trends

  Research Leaders in Data Science and Big Data reflect on the most important research advances in 2015 and the key trends expected to dominate throughout 2016.

  https://www.kdnuggets.com/2016/01/research-leaders-data-science-big-data-top-trends.html

• A Statistical View of Deep Learning

  A statistical overview of deep learning, with a focus on testing wide-held beliefs, highlighting statistical connections, and the unseen implications of deep learning. The post links to 6 articles covering a number of related topics.

  https://www.kdnuggets.com/2015/11/statistical-view-deep-learning.html

• Spark SQL for Real-Time Analytics

  Apache Spark is the hottest topic in Big Data. This tutorial discusses why Spark SQL is becoming the preferred method for Real Time Analytics and for next frontier, IoT (Internet of Things).

  https://www.kdnuggets.com/2015/09/spark-sql-real-time-analytics.html

• Top 10 Data Mining Algorithms, Explained

  Top 10 data mining algorithms, selected by top researchers, are explained here, including what do they do, the intuition behind the algorithm, available implementations of the algorithms, why use them, and interesting applications.

  https://www.kdnuggets.com/2015/05/top-10-data-mining-algorithms-explained.html

• Interview: Vince Darley, King.com on the Serious Analytics behind Casual Gaming

  We discuss key characteristics of social gaming data, ML use cases at King, infrastructure challenges, major problems with A-B testing and recommendations to resolve them.

  https://www.kdnuggets.com/2015/03/interview-vince-darley-king-analytics-gaming.html

• Data Science’s Most Used, Confused, and Abused Jargon

  As data science has spread through the mainstream, so too has a dense vocabulary of ill-defined jargon. In a split-personality post, we offer several perspectives on many of data science's most confused terms.

  https://www.kdnuggets.com/2015/02/data-science-confusing-jargon-abused.html

• KDnuggets™ News 14:n30, Nov 19

  Features | Software | Opinions | Interviews | Reports | News | Webcasts | Courses | Meetings | Jobs | Academic | Publications | Tweets Read more »

  https://www.kdnuggets.com/2014/n30.html