Search results for Recurrent Neural Network

Found 275 documents, 5946 searched:

• Multi-Task Learning – ERNIE 2.0: State-of-the-Art NLP Architecture Intuitively Explained

  The tech giant Baidu unveiled its state-of-the-art NLP architecture ERNIE 2.0 earlier this year, which scored significantly higher than XLNet and BERT on all tasks in the GLUE benchmark. This major breakthrough in NLP takes advantage of a new innovation called “Continual Incremental Multi-Task Learning”.

  https://www.kdnuggets.com/2019/10/multi-task-learning-ernie-sota-nlp-architecture.html

• How AI will transform healthcare (and can it fix the US healthcare system?)">How AI will transform healthcare (and can it fix the US healthcare system?)

  This thorough review focuses on the impact of AI, 5G, and edge computing on the healthcare sector in the 2020s as well as a look at quantum computing's potential impact on AI, healthcare, and financial services.

  https://www.kdnuggets.com/2019/09/ai-transform-healthcare.html

• A 2019 Guide for Automatic Speech Recognition

  In this article, we’ll look at a couple of papers aimed at solving the problem of automated speech recognition with machine and deep learning.

  https://www.kdnuggets.com/2019/09/2019-guide-automatic-speech-recognition.html

• A 2019 Guide to Speech Synthesis with Deep Learning

  In this article, we’ll look at research and model architectures that have been written and developed to do just that using deep learning.

  https://www.kdnuggets.com/2019/09/2019-guide-speech-synthesis-deep-learning.html

• TensorFlow vs PyTorch vs Keras for NLP">TensorFlow vs PyTorch vs Keras for NLP

  These three deep learning frameworks are your go-to tools for NLP, so which is the best? Check out this comparative analysis based on the needs of NLP, and find out where things are headed in the future.

  https://www.kdnuggets.com/2019/09/tensorflow-pytorch-keras-nlp.html

• TensorFlow 2.0: Dynamic, Readable, and Highly Extended

  With substantial changes coming with TensorFlow 2.0, and the release candidate version now available, learn more in this guide about the major updates and how to get started on the machine learning platform.

  https://www.kdnuggets.com/2019/08/tensorflow-20.html

• How to Become More Marketable as a Data Scientist">How to Become More Marketable as a Data Scientist

  As a data scientist, you are in high demand. So, how can you increase your marketability even more? Check out these current trends in skills most desired by employers in 2019.

  https://www.kdnuggets.com/2019/08/marketable-data-scientist.html

• A Gentle Guide to Starting Your NLP Project with AllenNLP

  For those who aren’t familiar with AllenNLP, I will give a brief overview of the library and let you know the advantages of integrating it to your project.

  https://www.kdnuggets.com/2019/07/gentle-guide-starting-nlp-project-allennlp.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

• Top Data Science and Machine Learning Methods Used in 2018, 2019">Top Data Science and Machine Learning Methods Used in 2018, 2019

  Once again, the most used methods are Regression, Clustering, Visualization, Decision Trees/Rules, and Random Forests. The greatest relative increases this year are overwhelmingly Deep Learning techniques, while SVD, SVMs and Association Rules show the greatest decline.

  https://www.kdnuggets.com/2019/04/top-data-science-machine-learning-methods-2018-2019.html

• An introduction to explainable AI, and why we need it

  We introduce explainable AI, why it is needed, and present the Reversed Time Attention Model, Local Interpretable Model-Agnostic Explanation and Layer-wise Relevance Propagation.

  https://www.kdnuggets.com/2019/04/introduction-explainable-ai.html

• Spatio-Temporal Statistics: A Primer

  Marketing scientist Kevin Gray asks University of Missouri Professor Chris Wikle about Spatio-Temporal Statistics and how it can be used in science and business.

  https://www.kdnuggets.com/2019/04/spatio-temporal-statistics-primer.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

• 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

• My favorite mind-blowing Machine Learning/AI breakthroughs">My favorite mind-blowing Machine Learning/AI breakthroughs

  We present some of our favorite breakthroughs in Machine Learning and AI in recent times, complete with papers, video links and brief summaries for each.

  https://www.kdnuggets.com/2019/03/favorite-ml-ai-breakthroughs.html

• Deconstructing BERT, Part 2: Visualizing the Inner Workings of Attention

  In this post, the author shows how BERT can mimic a Bag-of-Words model. The visualization tool from Part 1 is extended to probe deeper into the mind of BERT, to expose the neurons that give BERT its shape-shifting superpowers.

  https://www.kdnuggets.com/2019/03/deconstructing-bert-part-2-visualizing-inner-workings-attention.html

• Trending Deep Learning Github Repositories

  Check these pair of resources for trending and top GitHub deep learning repositories for some new ideas on what to be looking out for.

  https://www.kdnuggets.com/2019/02/trending-top-deep-learning-github-repositories.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

• NLP Overview: Modern Deep Learning Techniques Applied to Natural Language Processing">NLP Overview: Modern Deep Learning Techniques Applied to Natural Language Processing

  Trying to keep up with advancements at the overlap of neural networks and natural language processing can be troublesome. That's where the today's spotlighted resource comes in.

  https://www.kdnuggets.com/2019/01/nlp-overview-modern-deep-learning-techniques.html

• Deep Learning for the Masses (… and The Semantic Layer)

  Deep learning is everywhere right now, in your watch, in your television, your phone, and in someway the platform you are using to read this article. Here I’ll talk about how can you start changing your business using Deep Learning in a very simple way. But first, you need to know about the Semantic Layer.

  https://www.kdnuggets.com/2018/11/deep-learning-masses-semantic-layer.html

• Deep Learning Cheat Sheets">Deep Learning Cheat Sheets

  Check out this collection of high-quality deep learning cheat sheets, filled with valuable, concise information on a variety of neural network-related topics.

  https://www.kdnuggets.com/2018/11/deep-learning-cheat-sheets.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

• Introduction to PyTorch for Deep Learning

  In this tutorial, you’ll get an introduction to deep learning using the PyTorch framework, and by its conclusion, you’ll be comfortable applying it to your deep learning models.

  https://www.kdnuggets.com/2018/11/introduction-pytorch-deep-learning.html

• Introduction to Deep Learning with Keras

  In this article, we’ll build a simple neural network using Keras. Now let’s proceed to solve a real business problem: an insurance company wants you to develop a model to help them predict which claims look fraudulent.

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

• 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 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

• 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

• Deep Quantile Regression

  Most Deep Learning frameworks currently focus on giving a best estimate as defined by a loss function. Occasionally something beyond a point estimate is required to make a decision. This is where a distribution would be useful. This article will purely focus on inferring quantiles.

  https://www.kdnuggets.com/2018/07/deep-quantile-regression.html

• Taming LSTMs: Variable-sized mini-batches and why PyTorch is good for your health

  After reading this, you’ll be back to fantasies of you + PyTorch eloping into the sunset while your Recurrent Networks achieve new accuracies you’ve only read about on Arxiv.

  https://www.kdnuggets.com/2018/06/taming-lstms-variable-sized-mini-batches-pytorch.html

• The Keras 4 Step Workflow">The Keras 4 Step Workflow

  In his book "Deep Learning with Python," Francois Chollet outlines a process for developing neural networks with Keras in 4 steps. Let's take a look at this process with a simple example.

  https://www.kdnuggets.com/2018/06/keras-4-step-workflow.html

• Top 16 Open Source Deep Learning Libraries and Platforms

  We bring to you the top 16 open source deep learning libraries and platforms. TensorFlow is out in front as the undisputed number one, with Keras and Caffe completing the top three.

  https://www.kdnuggets.com/2018/04/top-16-open-source-deep-learning-libraries.html

• Ten Machine Learning Algorithms You Should Know to Become a Data Scientist">Ten Machine Learning Algorithms You Should Know to Become a Data Scientist

  It's important for data scientists to have a broad range of knowledge, keeping themselves updated with the latest trends. With that being said, we take a look at the top 10 machine learning algorithms every data scientist should know.

  https://www.kdnuggets.com/2018/04/10-machine-learning-algorithms-data-scientist.html

• Machine Learning for Text

  This book covers machine learning techniques from text using both bag-of-words and sequence-centric methods. The scope of coverage is vast, and it includes traditional information retrieval methods and also recent methods from neural networks and deep learning.

  https://www.kdnuggets.com/2018/04/machine-learning-text.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 “Weird” Introduction to Deep Learning">A “Weird” Introduction to Deep Learning

  There are amazing introductions, courses and blog posts on Deep Learning. But this is a different kind of introduction.

  https://www.kdnuggets.com/2018/03/weird-introduction-deep-learning.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

• Top 20 Python AI and Machine Learning Open Source Projects">Top 20 Python AI and Machine Learning Open Source Projects

  We update the top AI and Machine Learning projects in Python. Tensorflow has moved to the first place with triple-digit growth in contributors. Scikit-learn dropped to 2nd place, but still has a very large base of contributors.

  https://www.kdnuggets.com/2018/02/top-20-python-ai-machine-learning-open-source-projects.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

• 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

• Managing Machine Learning Workflows with Scikit-learn Pipelines Part 3: Multiple Models, Pipelines, and Grid Searches

  In this post, we will be using grid search to optimize models built from a number of different types estimators, which we will then compare and properly evaluate the best hyperparameters that each model has to offer.

  https://www.kdnuggets.com/2018/01/managing-machine-learning-workflows-scikit-learn-pipelines-part-3.html

• Deep Learning in H2O using R

  This article is about implementing Deep Learning (DL) using the H2O package in R. We start with a background on DL, followed by some features of H2O's DL framework, followed by an implementation using R.

  https://www.kdnuggets.com/2018/01/deep-learning-h2o-using-r.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

• 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

• A Guide for Customer Retention Analysis with SQL

  Customer retention curves are essential to any business looking to understand its clients, and will go a long way towards explaining other things like sales figures or the impact of marketing initiatives. They are an easy way to visualize a key interaction between customers and the business.

  https://www.kdnuggets.com/2017/12/guide-customer-retention-analysis-sql.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

• Data Science, Machine Learning: Main Developments in 2017 and Key Trends in 2018">Data Science, Machine Learning: Main Developments in 2017 and Key Trends in 2018

  The leading experts in the field on the main Data Science, Machine Learning, Predictive Analytics developments in 2017 and key trends in 2018.

  https://www.kdnuggets.com/2017/12/data-science-machine-learning-main-developments-trends.html

• TensorFlow for Short-Term Stocks Prediction

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

  https://www.kdnuggets.com/2017/12/tensorflow-short-term-stocks-prediction.html

• Using Deep Learning to Solve Real World Problems">Using Deep Learning to Solve Real World Problems

  Do you assume that deep learning is only being used for toy problems and in self-learning scenarios? This post includes several firsthand accounts of organizations using deep neural networks to solve real world problems.

  https://www.kdnuggets.com/2017/12/using-deep-learning-solve-real-world-problems.html

• Top 10 Videos on Deep Learning in Python">Top 10 Videos on Deep Learning in Python

  Playlists, individual tutorials (not part of a playlist) and online courses on Deep Learning (DL) in Python using the Keras, Theano, TensorFlow and PyTorch libraries. Assumes no prior knowledge. These videos cover all skill levels and time constraints!

  https://www.kdnuggets.com/2017/11/top-10-videos-deep-learning-python.html

• Machine Learning Algorithms: Which One to Choose for Your Problem">Machine Learning Algorithms: Which One to Choose for Your Problem

  This article will try to explain basic concepts and give some intuition of using different kinds of machine learning algorithms in different tasks. At the end of the article, you’ll find the structured overview of the main features of described algorithms.

  https://www.kdnuggets.com/2017/11/machine-learning-algorithms-choose-your-problem.html

• Want to know how Deep Learning works? Here’s a quick guide for everyone">Want to know how Deep Learning works? Here’s a quick guide for everyone

  Once you’ve read this article, you will understand the basics of AI and ML. More importantly, you will understand how Deep Learning, the most popular type of ML, works.

  https://www.kdnuggets.com/2017/11/deep-learning-works-quick-guide-everyone.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

• Ranking Popular Deep Learning Libraries for Data Science">Ranking Popular Deep Learning Libraries for Data Science

  We rank 23 open-source deep learning libraries that are useful for Data Science. The ranking is based on equally weighing its three components: Github and Stack Overflow activity, as well as Google search results.

  https://www.kdnuggets.com/2017/10/ranking-popular-deep-learning-libraries-data-science.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

• 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

• 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

• 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

• 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

• 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

• Deep Learning in Minutes with this Pre-configured Python VM Image">Deep Learning in Minutes with this Pre-configured Python VM Image

  Check out this Python deep learning virtual machine image, built on top of Ubuntu, which includes a number of machine learning tools and libraries, along with several projects to get up and running with right away.

  https://www.kdnuggets.com/2017/05/deep-learning-pre-configured-python-vm-image.html

• How Not To Program the TensorFlow Graph

  Using TensorFlow from Python is like using Python to program another computer. Being thoughtful about the graphs you construct can help you avoid confusion and costly performance problems.

  https://www.kdnuggets.com/2017/05/how-not-program-tensorflow-graph.html

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

  Here is a bare bones take on learning machine learning with Python, a complete course for the quick study hacker with no time (or patience) to spare.

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

• 5 Machine Learning Projects You Can No Longer Overlook, April">5 Machine Learning Projects You Can No Longer Overlook, April

  It's about that time again... 5 more machine learning or machine learning-related projects you may not yet have heard of, but may want to consider checking out. Find tools for data exploration, topic modeling, high-level APIs, and feature selection herein.

  https://www.kdnuggets.com/2017/04/five-machine-learning-projects-cant-overlook-april.html

• Getting Started with Deep Learning

  This post approaches getting started with deep learning from a framework perspective. Gain a quick overview and comparison of available tools for implementing neural networks to help choose what's right for you.

  https://www.kdnuggets.com/2017/03/getting-started-deep-learning.html

• Open Source Toolkits for Speech Recognition

  This article reviews the main options for free speech recognition toolkits that use traditional Hidden Markov Models and n-gram language models.

  https://www.kdnuggets.com/2017/03/open-source-toolkits-speech-recognition.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

• An Overview of Python Deep Learning Frameworks">An Overview of Python Deep Learning Frameworks

  Read this concise overview of leading Python deep learning frameworks, including Theano, Lasagne, Blocks, TensorFlow, Keras, MXNet, and PyTorch.

  https://www.kdnuggets.com/2017/02/python-deep-learning-frameworks-overview.html

• Learning to Learn by Gradient Descent by Gradient Descent

  What if instead of hand designing an optimising algorithm (function) we learn it instead? That way, by training on the class of problems we’re interested in solving, we can learn an optimum optimiser for the class!

  https://www.kdnuggets.com/2017/02/learning-learn-gradient-descent.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

• 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

• Top 20 Python Machine Learning Open Source Projects, updated">Top 20 Python Machine Learning Open Source Projects, updated

  Open Source is the heart of innovation and rapid evolution of technologies, these days. This article presents you Top 20 Python Machine Learning Open Source Projects of 2016 along with very interesting insights and trends found during the analysis.

  https://www.kdnuggets.com/2016/11/top-20-python-machine-learning-open-source-updated.html

• Deep Learning Reading Group: Skip-Thought Vectors

  Skip-thought vectors take inspiration from Word2Vec skip-gram and attempt to extend it to sentences, and are created using an encoder-decoder model. Read on for an overview of the paper.

  https://www.kdnuggets.com/2016/11/deep-learning-group-skip-thought-vectors.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

• 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

• Contest Winner: Winning the AutoML Challenge with Auto-sklearn

  This post is the first place prize recipient in the recent KDnuggets blog contest. Auto-sklearn is an open-source Python tool that automatically determines effective machine learning pipelines for classification and regression datasets. It is built around the successful scikit-learn library and won the recent AutoML challenge.

  https://www.kdnuggets.com/2016/08/winning-automl-challenge-auto-sklearn.html

• In Deep Learning, Architecture Engineering is the New Feature Engineering

  A discussion of architecture engineering in deep neural networks, and its relationship with feature engineering.

  https://www.kdnuggets.com/2016/07/deep-learning-architecture-engineering-feature-engineering.html

• How to Start Learning Deep Learning

  Want to get started learning deep learning? Sure you do! Check out this great overview, advice, and list of resources.

  https://www.kdnuggets.com/2016/07/start-learning-deep-learning.html

• Machine Learning for Artists – Video lectures and notes

  Art has always been deep for those who appreciate it... but now, more than ever, deep learning is making a real impact on the art world. Check out this graduate course, and its freely-available resources, focusing on this very topic.

  https://www.kdnuggets.com/2016/04/machine-learning-artists-video-lectures-notes.html

• Top 15 Frameworks for Machine Learning Experts

  Either you are a researcher, start-up or big organization who wants to use machine learning, you will need the right tools to make it happen. Here is a list of the most popular frameworks for machine learning.

  https://www.kdnuggets.com/2016/04/top-15-frameworks-machine-learning-experts.html

• The ICLR Experiment: Deep Learning Pioneers Take on Scientific Publishing

  Deep learning pioneers Yann LeCun and Yoshua Bengio have undertaken a grand experiment in academic publishing. Embracing a radical level of transparency and unprecedented public participation, they've created an opportunity not only to find and vet the best papers, but also to gather data about the publication process itself.

  https://www.kdnuggets.com/2016/02/iclr-deep-learning-scientific-publishing-experiment.html

• Scikit Flow: Easy Deep Learning with TensorFlow and Scikit-learn">Scikit Flow: Easy Deep Learning with TensorFlow and Scikit-learn

  Scikit Learn is a new easy-to-use interface for TensorFlow from Google based on the Scikit-learn fit/predict model. Does it succeed in making deep learning more accessible?

  https://www.kdnuggets.com/2016/02/scikit-flow-easy-deep-learning-tensorflow-scikit-learn.html

• Google Launches Deep Learning with TensorFlow MOOC

  Google and Udacity have partnered for a new self-paced course on deep learning and TensorFlow, starting immediately.

  https://www.kdnuggets.com/2016/01/google-deep-learning-tensorflow-course.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

• Attention and Memory in Deep Learning and NLP

  An overview of attention mechanisms and memory in deep neural networks and why they work, including some specific applications in natural language processing and beyond.

  https://www.kdnuggets.com/2016/01/attention-memory-deep-learning-nlp.html

• 7 Steps to Understanding Deep Learning

  There are many deep learning resources freely available online, but it can be confusing knowing where to begin. Go from vague understanding of deep neural networks to knowledgeable practitioner in 7 steps!

  https://www.kdnuggets.com/2016/01/seven-steps-deep-learning.html

• 50 Deep Learning Software Tools and Platforms, Updated

  We present the popular software & toolkit resources for Deep Learning, including Caffe, Cuda-convnet, Deeplearning4j, Pylearn2, Theano, and Torch. Explore the new list!

  https://www.kdnuggets.com/2015/12/deep-learning-tools.html

• Deep Learning Transcends the Bag of Words

  Generative RNNs are now widely popular, many modeling text at the character level and typically using unsupervised approach. Here we show how to generate contextually relevant sentences and explain recent work that does it successfully.

  https://www.kdnuggets.com/2015/12/deep-learning-outgrows-bag-words-recurrent-neural-networks.html

• Deep Learning for Visual Question Answering

  Here we discuss about the Visual Question Answering problem, and I’ll also present neural network based approaches for same.

  https://www.kdnuggets.com/2015/11/deep-learning-visual-question-answering.html

• Deep Learning and the Triumph of Empiricism

  Theoretical guarantees are clearly desirable. And yet many of today's best-performing supervised learning algorithms offer none. What explains the gap between theoretical soundness and empirical success?

  https://www.kdnuggets.com/2015/07/deep-learning-triumph-empiricism-over-theoretical-mathematical-guarantees.html

• Top 20 R packages by popularity

  Wondering which are the most popular R packages? Here's an analysis based on most downloaded R packages from Jan to May 2015 to identify the top trending packages in the R world!

  https://www.kdnuggets.com/2015/06/top-20-r-packages.html

• 30 Years of Data Science: A Review From a Data Science Practitioner

  A review from a data science practitioner.

  https://www.kdnuggets.com/30-years-of-data-science-a-review-from-a-data-science-practitioner

• 38 Free Courses on Coursera for Data Science">38 Free Courses on Coursera for Data Science

  There are so many online resources for learning data science, and a great deal of it can be used at no cost. This collection of free courses hosted by Coursera will help you enhance your data science and machine learning skills, no matter your current level of expertise.

  https://www.kdnuggets.com/2021/10/38-free-courses-coursera-datascience.html

• Surpassing Trillion Parameters and GPT-3 with Switch Transformers – a path to AGI?">Surpassing Trillion Parameters and GPT-3 with Switch Transformers – a path to AGI?

  Ever larger models churning on increasingly faster machines suggest a potential path toward smarter AI, such as with the massive GPT-3 language model. However, new, more lean, approaches are being conceived and explored that may rival these super-models, which could lead to a future with more efficient implementations of advanced AI-driven systems.

  https://www.kdnuggets.com/2021/10/trillion-parameters-gpt-3-switch-transformers-path-agi.html

• 7 Open Source Libraries for Deep Learning Graphs

  In this article we’ll go through 7 up-and-coming open source libraries for graph deep learning, ranked in order of increasing popularity.

  https://www.kdnuggets.com/2021/07/7-open-source-libraries-deep-learning-graphs.html

• The Essential Guide to Transformers, the Key to Modern SOTA AI

  You likely know Transformers from their recent spate of success stories in natural language processing, computer vision, and other areas of artificial intelligence, but are familiar with all of the X-formers? More importantly, do you know the differences, and why you might use one over another?

  https://www.kdnuggets.com/2021/06/essential-guide-transformers-key-modern-sota-ai.html

• Reducing the High Cost of Training NLP Models With SRU++

  The increasing computation time and costs of training natural language models (NLP) highlight the importance of inventing computationally efficient models that retain top modeling power with reduced or accelerated computation. A single experiment training a top-performing language model on the 'Billion Word' benchmark would take 384 GPU days and as much as $36,000 using AWS on-demand instances.

  https://www.kdnuggets.com/2021/03/reducing-high-cost-training-nlp-models-sru.html

• My machine learning model does not learn. What should I do?

  This article presents 7 hints on how to get out of the quicksand.

  https://www.kdnuggets.com/2021/02/machine-learning-model-not-learn.html

• Backcasting: Building an Accurate Forecasting Model for Your Business

  This article will shed some light on processes happening under the roof of ML-based solutions on the example of the business case where the future success directly depends on the ability to predict unknown values from the past.

  https://www.kdnuggets.com/2021/02/backcasting-building-accurate-forecasting-model-business.html

• Vision Transformers: Natural Language Processing (NLP) Increases Efficiency and Model Generality

  Why do we hear so little about transformer models applied to computer vision tasks? What about attention in computer vision networks?

  https://www.kdnuggets.com/2021/02/vision-transformers-nlp-efficiency-model-generality.html

• Unsupervised Learning for Predictive Maintenance using Auto-Encoders

  This article outlines a machine learning approach to detect and diagnose anomalies in the context of machine maintenance, along with a number of introductory concepts, including: Introduction to machine maintenance; What is predictive maintenance?; ​​​​Approaches for machine diagnosis; Machine diagnosis using machine learning

  https://www.kdnuggets.com/2021/01/unsupervised-learning-predictive-maintenance-auto-encoders.html

• Attention mechanism in Deep Learning, Explained

  Attention is a powerful mechanism developed to enhance the performance of the Encoder-Decoder architecture on neural network-based machine translation tasks. Learn more about how this process works and how to implement the approach into your work.

  https://www.kdnuggets.com/2021/01/attention-mechanism-deep-learning-explained.html

• Top 10 Computer Vision Papers 2020">Top 10 Computer Vision Papers 2020

  The top 10 computer vision papers in 2020 with video demos, articles, code, and paper reference.

  https://www.kdnuggets.com/2021/01/top-10-computer-vision-papers-2020.html

• 2020: A Year Full of Amazing AI Papers — A Review

  So much happened in the world during 2020 that it may have been easy to miss the great progress in the world of AI. To catch you up quickly, check out this curated list of the latest breakthroughs in AI by release date, along with a video explanation, link to an in-depth article, and code.

  https://www.kdnuggets.com/2020/12/2020-amazing-ai-papers.html

• Compute Goes Brrr: Revisiting Sutton’s Bitter Lesson for AI

  "It's just about having more compute." Wait, is that really all there is to AI? As Richard Sutton's 'bitter lesson' sinks in for more AI researchers, a debate has stirred that considers a potentially more subtle relationship between advancements in AI based on ever-more-clever algorithms and massively scaled computational power.

  https://www.kdnuggets.com/2020/11/revisiting-sutton-bitter-lesson-ai.html

• Roadmap to Computer Vision

  Read this introduction to the main steps which compose a computer vision system, starting from how images are pre-processed, features extracted and predictions are made.

  https://www.kdnuggets.com/2020/10/roadmap-computer-vision.html

• Understanding Transformers, the Data Science Way

  Read this accessible and conversational article about understanding transformers, the data science way — by asking a lot of questions that is.

  https://www.kdnuggets.com/2020/10/understanding-transformers-data-science-way.html

• A Deep Dive Into the Transformer Architecture – The Development of Transformer Models

  Even though transformers for NLP were introduced only a few years ago, they have delivered major impacts to a variety of fields from reinforcement learning to chemistry. Now is the time to better understand the inner workings of transformer architectures to give you the intuition you need to effectively work with these powerful tools.

  https://www.kdnuggets.com/2020/08/transformer-architecture-development-transformer-models.html

• 13 must-read papers from AI experts">13 must-read papers from AI experts

  What research articles do top AI experts in the field recommend? Find out which ones and why, then be sure to add each to your reading to do list.

  https://www.kdnuggets.com/2020/05/13-must-read-papers-ai-experts.html

• I Designed My Own Machine Learning and AI Degree

  With so many pioneering online resources for open education, check out this organized collection of courses you can follow to become a well-rounded machine learning and AI engineer.

  https://www.kdnuggets.com/2020/05/designed-machine-learning-ai-degree.html

• Customer Churn Prediction: A Global Performance Study

  This article details an automated machine-learned approach to predict customer churn and its results across selected communication service providers around the globe.

  https://www.kdnuggets.com/2020/05/customer-churn-prediction-global-performance-study.html

• Deep Learning: The Free eBook">Deep Learning: The Free eBook

  "Deep Learning" is the quintessential book for understanding deep learning theory, and you can still read it freely online.

  https://www.kdnuggets.com/2020/05/deep-learning-free-ebook.html

• DeepMind Unveils Agent57, the First AI Agents that Outperforms Human Benchmarks in 57 Atari Games

  The new reinforcement learning agent innovates over previous architectures achieving one of the most important milestones in the AI space.

  https://www.kdnuggets.com/2020/04/deepmind-agent57-atari-games.html

• 20 AI, Data Science, Machine Learning Terms You Need to Know in 2020 (Part 1)">20 AI, Data Science, Machine Learning Terms You Need to Know in 2020 (Part 1)

  2020 is well underway, and we bring you 20 AI, data science, and machine learning terms we should all be familiar with as the year marches onward.

  https://www.kdnuggets.com/2020/02/ai-data-science-machine-learning-key-terms-2020.html

• Deploying a pretrained GPT-2 model on AWS

  This post attempts to summarize my recent detour into NLP, describing how I exposed a Huggingface pre-trained Language Model (LM) on an AWS-based web application.

  https://www.kdnuggets.com/2019/12/deploying-pretrained-gpt-2-model-aws.html

• DeepMind Unveils MuZero, a New Agent that Mastered Chess, Shogi, Atari and Go Without Knowing the Rules

  The new model showed great improvements over the previous AlphaZero agent.

  https://www.kdnuggets.com/2019/12/deepmind-unveils-muzero-agent-chess-shogi-atari-go.html

• 10 Free Must-read Books on AI">10 Free Must-read Books on AI

  Artificial Intelligence continues to fill the media headlines while scientists and engineers rapidly expand its capabilities and applications. With such explosive growth in the field, there is a great deal to learn. Dive into these 10 free books that are must-reads to support your AI study and work.

  https://www.kdnuggets.com/2019/11/10-free-must-read-books-ai.html

• Beyond Word Embedding: Key Ideas in Document Embedding

  This literature review on document embedding techniques thoroughly covers the many ways practitioners develop rich vector representations of text -- from single sentences to entire books.

  https://www.kdnuggets.com/2019/10/beyond-word-embedding-document-embedding.html

• My journey path from a Software Engineer to BI Specialist to a Data Scientist">My journey path from a Software Engineer to BI Specialist to a Data Scientist

  The career path of the Data Scientist remains a hot target for many with its continuing high demand. Becoming one requires developing a broad set of skills including statistics, programming, and even business acumen. Learn more about one person's experience making this journey, and discover the many resources available to help you find your way into a world of data science.

  https://www.kdnuggets.com/2019/09/journey-software-engineer-bi-data-scientist.html

• Seven Key Dimensions to Help You Understand Artificial Intelligence Environments

  Understanding an AI environment is an incredibly complex task but there are several key dimensions that provide clarity on that reasoning.

  https://www.kdnuggets.com/2019/07/seven-key-dimensions-understand-artificial-intelligence-environments.html

• XLNet Outperforms BERT on Several NLP Tasks">XLNet Outperforms BERT on Several NLP Tasks

  XLNet is a new pretraining method for NLP that achieves state-of-the-art results on several NLP tasks.

  https://www.kdnuggets.com/2019/07/xlnet-outperforms-bert-several-nlp-tasks.html

• State of the art in AI and Machine Learning – highlights of papers with code

  We introduce papers with code, the free and open resource of state-of-the-art Machine Learning papers, code and evaluation tables.

  https://www.kdnuggets.com/2019/02/paperswithcode-ai-machine-learning-highlights.html

• Best Machine Learning Languages, Data Visualization Tools, DL Frameworks, and Big Data Tools">Best Machine Learning Languages, Data Visualization Tools, DL Frameworks, and Big Data Tools

  We cover a variety of topics, from machine learning to deep learning, from data visualization to data tools, with comments and explanations from experts in the relevant fields.

  https://www.kdnuggets.com/2018/12/machine-learning-data-visualization-deep-learning-tools.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

• 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