Search results for Recurrent Neural Network

Found 275 documents, 5932 searched:

• Recurrent Neural Networks (RNN): Deep Learning for Sequential Data

  Recurrent Neural Networks can be used for a number of ways such as detecting the next word/letter, forecasting financial asset prices in a temporal space, action modeling in sports, music composition, image generation, and more.

  https://www.kdnuggets.com/2020/07/rnn-deep-learning-sequential-data.html

• Generating cooking recipes using TensorFlow and LSTM Recurrent Neural Network: A step-by-step guide

  A character-level LSTM (Long short-term memory) RNN (Recurrent Neural Network) is trained on ~100k recipes dataset using TensorFlow. The model suggested the recipes "Cream Soda with Onions", "Puff Pastry Strawberry Soup", "Zucchini flavor Tea", and "Salmon Mousse of Beef and Stilton Salad with Jalapenos". Yum!? Follow along this detailed guide with code to create your own recipe-generating chef.

  https://www.kdnuggets.com/2020/07/generating-cooking-recipes-using-tensorflow.html

• Using Genetic Algorithm for Optimizing Recurrent Neural Networks

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

  https://www.kdnuggets.com/2018/01/genetic-algorithm-optimizing-recurrent-neural-network.html

• Exploring Recurrent Neural Networks

  We explore recurrent neural networks, starting with the basics, using a motivating weather modeling problem, and implement and train an RNN in TensorFlow.

  https://www.kdnuggets.com/2017/12/exploring-recurrent-neural-networks.html

• A Guide For Time Series Prediction Using Recurrent Neural Networks (LSTMs)

  Looking at the strengths of a neural network, especially a recurrent neural network, I came up with the idea of predicting the exchange rate between the USD and the INR.

  https://www.kdnuggets.com/2017/10/guide-time-series-prediction-recurrent-neural-networks-lstms.html

• Building, Training, and Improving on Existing Recurrent Neural Networks

  In this post, we’ll provide a short tutorial for training a RNN for speech recognition, including code snippets throughout.

  https://www.kdnuggets.com/2017/05/building-training-improving-existing-recurrent-neural-networks.html

• How to Build a Recurrent Neural Network in TensorFlow

  This is a no-nonsense overview of implementing a recurrent neural network (RNN) in TensorFlow. Both theory and practice are covered concisely, and the end result is running TensorFlow RNN code.

  https://www.kdnuggets.com/2017/04/build-recurrent-neural-network-tensorflow.html

• Recurrent Neural Networks Tutorial, Introduction

  Recurrent Neural Networks (RNNs) are popular models that have shown great promise in NLP and many other Machine Learning tasks. Here is a much-needed guide to key RNN models and a few brilliant research papers.

  https://www.kdnuggets.com/2015/10/recurrent-neural-networks-tutorial.html

• Excellent Tutorial on Sequence Learning using Recurrent Neural Networks

  Excellent tutorial explaining Recurrent Neural Networks (RNNs) which hold great promise for learning general sequences, and have applications for text analysis, handwriting recognition and even machine translation.

  https://www.kdnuggets.com/2015/06/rnn-tutorial-sequence-learning-recurrent-neural-networks.html

• Recursive (not Recurrent!) Neural Networks in TensorFlow

  Learn how to implement recursive neural networks in TensorFlow, which can be used to learn tree-like structures, or directed acyclic graphs.

  https://www.kdnuggets.com/2016/06/recursive-neural-networks-tensorflow.html

• A Brief History of the Neural Networks

  From the biological neuron to LLMs: How AI became smart.

  https://www.kdnuggets.com/a-brief-history-of-the-neural-networks

• Exploring Neural Networks

  Unlocking the power of AI: a suide to neural networks and their applications.

  https://www.kdnuggets.com/exploring-neural-networks

• Neural Networks and Deep Learning: A Textbook (2nd Edition)

  The second edition of the book Neural Networks and Deep Learning is now available. This book covers both classical and modern models in deep learning. The book is intended to be a textbook for universities, and it covers the theoretical and algorithmic aspects of deep learning. The second edition is significantly expanded and covers many modern topics such as graph neural networks, adversarial learning, attention mechanisms, transformers, and large language models.

  https://www.kdnuggets.com/2023/07/aggarwal-neural-networks-deep-learning-textbook-2nd-edition.html

• Learn Deep Learning by Building 15 Neural Network Projects in 2022

  Here are 15 neural network projects you can take on in 2022 to build your skills, your know-how, and your portfolio.

  https://www.kdnuggets.com/2022/01/15-neural-network-projects-build-2022.html

• A Friendly Introduction to Graph Neural Networks

  Despite being what can be a confusing topic, graph neural networks can be distilled into just a handful of simple concepts. Read on to find out more.

  https://www.kdnuggets.com/2020/11/friendly-introduction-graph-neural-networks.html

• Batch Normalization in Deep Neural Networks

  Batch normalization is a technique for training very deep neural networks that normalizes the contributions to a layer for every mini batch.

  https://www.kdnuggets.com/2020/08/batch-normalization-deep-neural-networks.html

• The Unreasonable Progress of Deep Neural Networks in Natural Language Processing (NLP)

  Natural language processing has made incredible advances through advanced techniques in deep learning. Learn about these powerful models, and find how close (or far away) these approaches are to human-level understanding.

  https://www.kdnuggets.com/2020/06/unreasonable-progress-deep-neural-networks-nlp.html

• 3 Reasons to Use Random Forest® Over a Neural Network: Comparing Machine Learning versus Deep Learning

  Both the random forest algorithm and Neural Networks are different techniques that learn differently but can be used in similar domains. Why would you use one over the other?

  https://www.kdnuggets.com/2020/04/3-reasons-random-forest-neural-network-comparison.html

• 5 Techniques to Prevent Overfitting in Neural Networks

  In this article, I will present five techniques to prevent overfitting while training neural networks.

  https://www.kdnuggets.com/2019/12/5-techniques-prevent-overfitting-neural-networks.html

• Can Neural Networks Develop Attention? Google Thinks they Can

  Google recently published some work about modeling attention mechanisms in deep neural networks.

  https://www.kdnuggets.com/2019/11/neural-networks-develop-attention-google.html

• Introduction to Artificial Neural Networks

  In this article, we’ll try to cover everything related to Artificial Neural Networks or ANN.

  https://www.kdnuggets.com/2019/10/introduction-artificial-neural-networks.html

• Training a Neural Network to Write Like Lovecraft">Training a Neural Network to Write Like Lovecraft

  In this post, the author attempts to train a neural network to generate Lovecraft-esque prose, known to be awkward and irregular at best. Did it end in success? If not, any suggestions on how it might have? Read on to find out.

  https://www.kdnuggets.com/2019/07/training-neural-network-write-like-lovecraft.html

• Evolving Deep Neural Networks

  This article reviews how evolutionary algorithms have been proposed and tested as a competitive alternative to address a number of issues related to neural network design.

  https://www.kdnuggets.com/2019/06/evolving-deep-neural-networks.html

• The Rise of Generative Adversarial Networks

  A comprehensive overview of Generative Adversarial Networks, covering its birth, different architectures including DCGAN, StyleGAN and BigGAN, as well as some real-world examples.

  https://www.kdnuggets.com/2019/04/rise-generative-adversarial-networks.html

• A comprehensive survey on graph neural networks

  This article summarizes a paper which presents us with a broad sweep of the graph neural network landscape. It’s a survey paper, so you’ll find details on the key approaches and representative papers, as well as information on commonly used datasets and benchmark performance on them.

  https://www.kdnuggets.com/2019/02/comprehensive-survey-graph-neural-networks.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

• Neural Networks and Deep Learning: A Textbook">Neural Networks and Deep Learning: A Textbook

  This book covers both classical and modern models in deep learning. The book is intended to be a textbook for universities, and it covers the theoretical and algorithmic aspects of deep learning.

  https://www.kdnuggets.com/2018/09/aggarwal-neural-networks-textbook.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

• Neural Network based Startup Name Generator

  How to build a recurrent neural network to generate suggestions for your new company’s name.

  https://www.kdnuggets.com/2018/04/neural-network-startup-name-generator.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 8 Neural Network Architectures Machine Learning Researchers Need to Learn">The 8 Neural Network Architectures Machine Learning Researchers Need to Learn

  In this blog post, I want to share the 8 neural network architectures from the course that I believe any machine learning researchers should be familiar with to advance their work.

  https://www.kdnuggets.com/2018/02/8-neural-network-architectures-machine-learning-researchers-need-learn.html

• Building an Audio Classifier using Deep Neural Networks

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

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

• 7 Types of Artificial Neural Networks for Natural Language Processing">7 Types of Artificial Neural Networks for Natural Language Processing

  What is an artificial neural network? How does it work? What types of artificial neural networks exist? How are different types of artificial neural networks used in natural language processing? We will discuss all these questions in the following article.

  https://www.kdnuggets.com/2017/10/7-types-artificial-neural-networks-natural-language-processing.html

• How Convolutional Neural Networks Accomplish Image Recognition?

  Image recognition is very interesting and challenging field of study. Here we explain concepts, applications and techniques of image recognition using Convolutional Neural Networks.

  https://www.kdnuggets.com/2017/08/convolutional-neural-networks-image-recognition.html

• Mind Reading: Using Artificial Neural Nets to Predict Viewed Image Categories From EEG Readings

  This post outlines the approach taken at a recent deep learning hackathon, hosted by YCombinator-backed startup DeepGram. The dataset: EEG readings from a Stanford research project that predicted which category of images their test subjects were viewing using linear discriminant analysis.

  https://www.kdnuggets.com/2017/08/mind-reading-using-artificial-neural-nets.html

• Going deeper with recurrent networks: Sequence to Bag of Words Model

  Deep learning makes it possible to convert unstructured text to computable formats, incorporating semantic knowledge to train machine learning models. These digital data troves help us understand people on a new level.

  https://www.kdnuggets.com/2017/08/deeper-recurrent-networks-sequence-bag-words-model.html

• Introduction to Neural Networks, Advantages and Applications">Introduction to Neural Networks, Advantages and Applications

  Artificial Neural Network (ANN) algorithm mimic the human brain to process information. Here we explain how human brain and ANN works.

  https://www.kdnuggets.com/2017/07/introduction-neural-networks-advantages-applications.html

• A Quick Introduction to Neural Networks

  This article provides a beginner level introduction to multilayer perceptron and backpropagation.

  https://www.kdnuggets.com/2016/11/quick-introduction-neural-networks.html

• Artificial Intelligence, Deep Learning, and Neural Networks, Explained">Artificial Intelligence, Deep Learning, and Neural Networks, Explained

  This article is meant to explain the concepts of AI, deep learning, and neural networks at a level that can be understood by most non-practitioners, and can also serve as a reference or review for technical folks as well.

  https://www.kdnuggets.com/2016/10/artificial-intelligence-deep-learning-neural-networks-explained.html

• Are Deep Neural Networks Creative?

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

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

• How do Neural Networks Learn?

  Neural networks are generating a lot of excitement, while simultaneously posing challenges to people trying to understand how they work. Visualize how neural nets work from the experience of implementing a real world project.

  https://www.kdnuggets.com/2015/12/how-do-neural-networks-learn.html

• Understanding Convolutional Neural Networks for NLP

  Dive into the world of Convolution Neural Networks (CNN), learn how they work, how to apply them for NLP, and how to tune CNN hyperparameters for best performance.

  https://www.kdnuggets.com/2015/11/understanding-convolutional-neural-networks-nlp.html

• Interactive Machine Learning Experiments

  Dive into experimenting with machine learning techniques using this open-source collection of interactive demos built on multilayer perceptrons, convolutional neural networks, and recurrent neural networks. Each package consists of ready-to-try web browser interfaces and fully-developed notebooks for you to fine tune the training for better performance.

  https://www.kdnuggets.com/2020/05/interactive-machine-learning-experiments.html

• Deep Learning for NLP: Creating a Chatbot with Keras!">Deep Learning for NLP: Creating a Chatbot with Keras!

  Learn how to use Keras to build a Recurrent Neural Network and create a Chatbot! Who doesn’t like a friendly-robotic personal assistant?

  https://www.kdnuggets.com/2019/08/deep-learning-nlp-creating-chatbot-keras.html

• Deep Learning for NLP: ANNs, RNNs and LSTMs explained!">Deep Learning for NLP: ANNs, RNNs and LSTMs explained!

  Learn about Artificial Neural Networks, Deep Learning, Recurrent Neural Networks and LSTMs like never before and use NLP to build a Chatbot!

  https://www.kdnuggets.com/2019/08/deep-learning-nlp-explained.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

• The Ultimate Roadmap to Becoming Specialised in The Tech Industry

  There is more than one route that you can take to be a competitive tech professional.

  https://www.kdnuggets.com/the-ultimate-roadmap-to-becoming-specialised-in-the-tech-industry

• Free Harvard Course: Introduction to AI with Python

  Looking for a great course to learn Artificial Intelligence with Python? Check out this free course from Harvard University.

  https://www.kdnuggets.com/free-harvard-course-introduction-to-ai-with-python

• 5 Super Cheat Sheets to Master Data Science

  The collection of super cheat sheets covers basic concepts of data science, probability & statistics, SQL, machine learning, and deep learning.

  https://www.kdnuggets.com/5-super-cheat-sheets-to-master-data-science

• A Comprehensive List of Resources to Master Large Language Models

  Large Language Models (LLMs) have now become an integral part of various applications. This article provides an extensive list of resources for anyone interested to dive into the world of LLMs.

  https://www.kdnuggets.com/a-comprehensive-list-of-resources-to-master-large-language-models

• Tackle computer science problems using both fundamental and modern algorithms in machine learning

  Master algorithms, including deep learning like LSTMs, GRUs, RNNs, and Generative AI & LLMs such as ChatGPT, with Packt's 50 Algorithms Every Programmer Should Know.

  https://www.kdnuggets.com/2023/11/packt-tackle-computer-science-problems-fundamental-modern-algorithms-machine-learning

• Top 7 Essential Cheat Sheets To Ace Your Data Science Interview

  The blog covers cheat sheets on SQL, statistics, pandas, data visualization, scikit-learn, Git, and theoretical data science concepts.

  https://www.kdnuggets.com/top-7-essential-cheat-sheets-to-ace-your-data-science-interview

• 5 Free Books to Master Data Science

  Want to break into data science? Check this list of free books for learning Python, statistics, linear algebra, machine learning and deep learning.

  https://www.kdnuggets.com/5-free-books-to-master-data-science

• Comparing Natural Language Processing Techniques: RNNs, Transformers, BERT

  RNN, Transformers, and BERT are popular NLP techniques with tradeoffs in sequence modeling, parallelization, and pre-training for downstream tasks.

  https://www.kdnuggets.com/comparing-natural-language-processing-techniques-rnns-transformers-bert

• 7 Steps to Mastering Natural Language Processing

  Want to learn all about Natural Language Processing (NLP)? Here is a 7 step guide to help you go from the fundamentals of machine learning and Python to Transformers, recent advances in NLP, and beyond.

  https://www.kdnuggets.com/7-steps-to-mastering-natural-language-processing

• Overcoming Barriers in Multi-lingual Voice Technology: Top 5 Challenges and Innovative Solutions

  Voice assistants like Siri, Alexa and Google Assistant are household names, but they still don't do well in multilingual settings. This article first provides an overview of how voice assistants work, and then dives into the top 5 challenges for voice assistants when it comes to providing a superior multilingual user experience. It also provides strategies for mitigation of these challenges.

  https://www.kdnuggets.com/2023/08/overcoming-barriers-multilingual-voice-technology-top-5-challenges-innovative-solutions.html

• Breaking the Data Barrier: How Zero-Shot, One-Shot, and Few-Shot Learning are Transforming Machine Learning

  Discover the concepts of Zero-Shot, One-Shot, and Few-Shot Learning, which enable machine learning models to classify and recognize objects or patterns with a limited number of examples.

  https://www.kdnuggets.com/2023/08/breaking-data-barrier-zeroshot-oneshot-fewshot-learning-transforming-machine-learning.html

• Introduction to Statistical Learning, Python Edition: Free Book

  The highly anticipated Python edition of Introduction to Statistical Learning is here. And you can read it for free! Here’s everything you need to know about the book.

  https://www.kdnuggets.com/2023/07/introduction-statistical-learning-python-edition-free-book.html

• Your Ultimate Guide to Chat GPT and Other Abbreviations

  Everyone seems to have gone crazy about ChatGPT, which has become a cultural phenomenon. If you’re not on the ChatGPT train yet, this article might help you better understand the context and excitement around this innovation.

  https://www.kdnuggets.com/2023/06/ultimate-guide-chat-gpt-abbreviations.html

• Ten Years of AI in Review

  From image classification to chatbot therapy.

  https://www.kdnuggets.com/2023/06/ten-years-ai-review.html

• Advanced Feature Selection Techniques for Machine Learning Models

  Mastering Feature Selection: An Exploration of Advanced Techniques for Supervised and Unsupervised Machine Learning Models.

  https://www.kdnuggets.com/2023/06/advanced-feature-selection-techniques-machine-learning-models.html

• Introducing TPU v4: Googles Cutting Edge Supercomputer for Large Language Models

  TPU v4: Google's fifth domain-specific architecture and third supercomputer for machine learning models.

  https://www.kdnuggets.com/2023/04/introducing-tpu-v4-googles-cutting-edge-supercomputer-large-language-models.html

• Multimodal Models Explained

  Unlocking the Power of Multimodal Learning: Techniques, Challenges, and Applications.

  https://www.kdnuggets.com/2023/03/multimodal-models-explained.html

• Free TensorFlow 2.0 Complete Course

  Are you a beginner python programmer aiming to make a career in Machine Learning? If yes, then you are at the right place! This FREE tutorial will give you a solid understanding of the foundations of Machine Learning and Neural Networks using TensorFlow 2.0.

  https://www.kdnuggets.com/2023/02/free-tensorflow-20-complete-course.html

• Concepts You Should Know Before Getting Into Transformers

  Learn about Input Embedding, Positional Encoding, Scaled Dot-Product Attention, Residual Connections, Mask, and Softmax function.

  https://www.kdnuggets.com/2023/01/concepts-know-getting-transformer.html

• 7 Super Cheat Sheets You Need To Ace Machine Learning Interview

  Revise the concepts of machine learning algorithms, frameworks, and methodologies to ace the technical interview round.

  https://www.kdnuggets.com/2022/12/7-super-cheat-sheets-need-ace-machine-learning-interview.html

• Memory Complexity with Transformers

  What’s the problem with running a transformer model on a book with 1 million tokens? What can be a solution to this problem?

  https://www.kdnuggets.com/2022/12/memory-complexity-transformers.html

• The Complete Machine Learning Study Roadmap

  Find out where you need to be to start your Machine Learning journey and what you need to do to succeed in the field.

  https://www.kdnuggets.com/2022/12/complete-machine-learning-study-roadmap.html

• Research Papers for NLP Beginners

  Read research papers on neural models, word embedding, language modeling, and attention & transformers.

  https://www.kdnuggets.com/2022/11/research-papers-nlp-beginners.html

• Approaches to Text Summarization: An Overview

  This article will present the main approaches to text summarization currently employed, as well as discuss some of their characteristics.

  https://www.kdnuggets.com/2019/01/approaches-text-summarization-overview.html

• 15 More Free Machine Learning and Deep Learning Books

  Check out this second list of 15 FREE ebooks for learning machine learning and deep learning.

  https://www.kdnuggets.com/2022/11/15-free-machine-learning-deep-learning-books.html

• The ABCs of NLP, From A to Z

  There is no shortage of tools today that can help you through the steps of natural language processing, but if you want to get a handle on the basics this is a good place to start. Read about the ABCs of NLP, all the way from A to Z.

  https://www.kdnuggets.com/2022/10/abcs-nlp-a-to-z.html

• KDnuggets News, September 14: Free Python for Data Science Course • Everything You’ve Ever Wanted to Know About Machine Learning

  Free Python for Data Science Course • Everything You’ve Ever Wanted to Know About Machine Learning • Progress Bars in Python with tqdm for Fun and Profit • 7 Tips for Python Beginners • 7 Data Analytics Interview Questions & Answers

  https://www.kdnuggets.com/2022/n36.html

• Free Artificial Intelligence And Deep Learning Crash Course

  Deep learning forms the backbone of modern day artificial intelligence. Learn more about the important aspects of this connection with this freely available course.

  https://www.kdnuggets.com/2022/07/free-artificial-intelligence-deep-learning-crash-course.html

• Deep Learning Key Terms, Explained

  Gain a beginner's perspective on artificial neural networks and deep learning with this set of 14 straight-to-the-point related key concept definitions.

  https://www.kdnuggets.com/2016/10/deep-learning-key-terms-explained.html

• How Activation Functions Work in Deep Learning

  Check out a this article for a better understanding of activation functions.

  https://www.kdnuggets.com/2022/06/activation-functions-work-deep-learning.html

• Data Science, Statistics and Machine Learning Dictionary

  Check out this curated list of the most used data science terminology and get a leg up on your learning.

  https://www.kdnuggets.com/2022/05/data-science-statistics-machine-learning-dictionary.html

• HuggingFace Has Launched a Free Deep Reinforcement Learning Course

  Hugging Face has released a free course on Deep RL. It is self-paced and shares a lot of pointers on theory, tutorials, and hands-on guides.

  https://www.kdnuggets.com/2022/05/huggingface-launched-free-deep-reinforcement-learning-course.html

• How to Start Using Natural Language Processing With PyTorch

  In this guide, we will address some of the obvious questions that may arise when starting to dive into natural language processing, but we will also engage with deeper questions and give you the right steps to get started working on your own NLP programs.

  https://www.kdnuggets.com/2022/04/start-natural-language-processing-pytorch.html

• Artificial Intelligence Project Ideas for 2022

  In this article, I will provide you with a list of artificial intelligence project ideas that would look great on your resume. 

  https://www.kdnuggets.com/2022/01/artificial-intelligence-project-ideas-2022.html

• 6 Predictive Models Every Beginner Data Scientist Should Master">6 Predictive Models Every Beginner Data Scientist Should Master

  Data Science models come with different flavors and techniques — luckily, most advanced models are based on a couple of fundamentals. Which models should you learn when you want to begin a career as Data Scientist? This post brings you 6 models that are widely used in the industry, either in standalone form or as a building block for other advanced techniques.

  https://www.kdnuggets.com/2021/12/6-predictive-models-every-beginner-data-scientist-master.html

• Sentiment Analysis with KNIME

  Check out this tutorial on how to approach sentiment classification with supervised machine learning algorithms.

  https://www.kdnuggets.com/2021/11/sentiment-analysis-knime.html

• Dream Come True: Building websites by thinking about them

  From the mind to the computer, make websites using your imagination!

  https://www.kdnuggets.com/2021/11/dream-come-true-allennlp-hacks-21.html

• 7 of The Coolest Machine Learning Topics of 2021 at ODSC West

  At our upcoming event this November 16th-18th in San Francisco, ODSC West 2021 will feature a plethora of talks, workshops, and training sessions on machine learning topics, deep learning, NLP, MLOps, and so on. You can register now for 20% off all ticket types, or register for a free AI Expo Pass to see what some big names in AI are doing now.

  https://www.kdnuggets.com/2021/11/odsc-7-coolest-machine-learning-topics.html

• Multivariate Time Series Analysis with an LSTM based RNN

  Check out this codeless solution using the Keras integration.

  https://www.kdnuggets.com/2021/10/multivariate-time-series-analysis-lstm-based-rnn.html

• Machine Learning Model Development and Model Operations: Principles and Practices">Machine Learning Model Development and Model Operations: Principles and Practices

  The ML model management and the delivery of highly performing model is as important as the initial build of the model by choosing right dataset. The concepts around model retraining, model versioning, model deployment and model monitoring are the basis for machine learning operations (MLOps) that helps the data science teams deliver highly performing models.

  https://www.kdnuggets.com/2021/10/machine-learning-model-development-operations-principles-practice.html

• Machine Learning Skills – Update Yours This Summer

  The process of mastering new knowledge often requires multiple passes to ensure the information is deeply understood. If you already began your journey into machine learning and data science, then you are likely ready for a refresher on topics you previously covered. This eight-week self-learning path will help you recapture the foundations and prepare you for future success in applying these skills.

  https://www.kdnuggets.com/2021/07/update-your-machine-learning-skills.html

• Geometric foundations of Deep Learning">Geometric foundations of Deep Learning

  Geometric Deep Learning is an attempt for geometric unification of a broad class of machine learning problems from the perspectives of symmetry and invariance. These principles not only underlie the breakthrough performance of convolutional neural networks and the recent success of graph neural networks but also provide a principled way to construct new types of problem-specific inductive biases.

  https://www.kdnuggets.com/2021/07/geometric-foundations-deep-learning.html

• High-Performance Deep Learning: How to train smaller, faster, and better models – Part 4

  With the right software, hardware, and techniques at your fingertips, your capability to effectively develop high-performing models now hinges on leveraging automation to expedite the experimental process and building with the most efficient model architectures for your data.

  https://www.kdnuggets.com/2021/07/high-performance-deep-learning-part4.html

• Will There Be a Shortage of Data Science Jobs in the Next 5 Years?">Will There Be a Shortage of Data Science Jobs in the Next 5 Years?

  The data science workflow is getting automated day by day.

  https://www.kdnuggets.com/2021/06/shortage-data-science-jobs-5-years.html

• A checklist to track your Data Science progress">A checklist to track your Data Science progress

  Whether you are just starting out in data science or already a gainfully-employed professional, always learning more to advance through state-of-the-art techniques is part of the adventure. But, it can be challenging to track of your progress and keep an eye on what's next. Follow this checklist to help you scale your expertise from entry-level to advanced.

  https://www.kdnuggets.com/2021/05/checklist-data-science-progress.html

• Machine Translation in a Nutshell

  Marketing scientist Kevin Gray asks Dr. Anna Farzindar of the University of Southern California for a snapshot of machine translation. Dr. Farzindar also provided the original art for this article.

  https://www.kdnuggets.com/2021/05/machine-translation-nutshell.html

• Popular Machine Learning Interview Questions, part 2

  Get ready for your next job interview requiring domain knowledge in machine learning with answers to these thirteen common questions.

  https://www.kdnuggets.com/2021/01/popular-machine-learning-interview-questions-part2.html

• AI, Analytics, Machine Learning, Data Science, Deep Learning Research Main Developments in 2020 and Key Trends for 2021">AI, Analytics, Machine Learning, Data Science, Deep Learning Research Main Developments in 2020 and Key Trends for 2021

  2020 is finally coming to a close. While likely not to register as anyone's favorite year, 2020 did have some noteworthy advancements in our field, and 2021 promises some important key trends to look forward to. As has become a year-end tradition, our collection of experts have once again contributed their thoughts. Read on to find out more.

  https://www.kdnuggets.com/2020/12/predictions-ai-machine-learning-data-science-research.html

• Mastering TensorFlow Tensors in 5 Easy Steps

  Discover how the building blocks of TensorFlow works at the lower level and learn how to make the most of Tensor objects.

  https://www.kdnuggets.com/2020/11/mastering-tensorflow-tensors-5-easy-steps.html

• Mastering Time Series Analysis with Help From the Experts

  Read this discussion with the “Time Series” Team at KNIME, answering such classic questions as "how much past is enough past?" others that any practitioner of time series analysis will find useful.

  https://www.kdnuggets.com/2020/10/mastering-time-series-analysis-experts.html

• An Introduction to AI, updated">An Introduction to AI, updated

  We provide an introduction to key concepts and methods in AI, covering Machine Learning and Deep Learning, with an updated extensive list that includes Narrow AI, Super Intelligence, and Classic Artificial Intelligence, as well as recent ideas of NeuroSymbolic AI, Neuroevolution, and Federated Learning.

  https://www.kdnuggets.com/2020/10/introduction-ai-updated.html

• Roadmap to Natural Language Processing (NLP)">Roadmap to Natural Language Processing (NLP)

  Check out this introduction to some of the most common techniques and models used in Natural Language Processing (NLP).

  https://www.kdnuggets.com/2020/10/roadmap-natural-language-processing-nlp.html

• MathWorks Deep learning workflow: tips, tricks, and often forgotten steps

  Getting started in deep learning – and adopting an organized, sustainable, and reproducible workflow – can be challenging. This blog post will share some tips and tricks to help you develop a systematic, effective, attainable, and scalable deep learning workflow as you experiment with different deep learning models, datasets, and applications.

  https://www.kdnuggets.com/2020/09/mathworks-deep-learning-workflow.html

• The Insiders’ Guide to Generative and Discriminative Machine Learning Models

  In this article, we will look at the difference between generative and discriminative models, how they contrast, and one another.

  https://www.kdnuggets.com/2020/09/insiders-guide-generative-discriminative-machine-learning-models.html

• Deep Learning’s Most Important Ideas">Deep Learning’s Most Important Ideas

  In the field of deep learning, there continues to be a deluge of research and new papers published daily. Many well-adopted ideas that have stood the test of time provide the foundation for much of this new work. To better understand modern deep learning, these techniques cover the basic necessary knowledge, especially as a starting point if you are new to the field.

  https://www.kdnuggets.com/2020/09/deep-learnings-most-important-ideas.html

• AI Papers to Read in 2020

  Reading suggestions to keep you up-to-date with the latest and classic breakthroughs in AI and Data Science.

  https://www.kdnuggets.com/2020/09/ai-papers-read-2020.html

• 8 AI/Machine Learning Projects To Make Your Portfolio Stand Out">8 AI/Machine Learning Projects To Make Your Portfolio Stand Out

  If you are just starting down a path toward a career in Data Science, or you are already a seasoned practitioner, then keeping active to advance your experience through side projects is invaluable to take you to the next professional level. These eight interesting project ideas with source code and reference articles will jump start you to thinking outside of the box.

  https://www.kdnuggets.com/2020/09/8-ml-ai-projects-stand-out.html

• Accelerated Natural Language Processing: A Free Course From Amazon

  Amazon's Machine Learning University is making its online courses available to the public, starting with this Accelerated Natural Language Processing offering.

  https://www.kdnuggets.com/2020/08/accelerated-nlp-free-amazon-machine-learning-university.html

• Awesome Machine Learning and AI Courses">Awesome Machine Learning and AI Courses

  Check out this list of awesome, free machine learning and artificial intelligence courses with video lectures.

  https://www.kdnuggets.com/2020/07/awesome-machine-learning-ai-courses.html

• 5 Fantastic Natural Language Processing Books

  This curated collection of 5 natural language processing books attempts to cover a number of different aspects of the field, balancing the practical and the theoretical. Check out these 5 fantastic selections now in order to improve your NLP skills.

  https://www.kdnuggets.com/2020/07/5-fantastic-nlp-books.html

• Deep Learning for Signal Processing: What You Need to Know

  Signal Processing is a branch of electrical engineering that models and analyzes data representations of physical events. It is at the core of the digital world. And now, signal processing is starting to make some waves in deep learning.

  https://www.kdnuggets.com/2020/07/deep-learning-signal-processing.html

• PyTorch LSTM: Text Generation Tutorial

  Key element of LSTM is the ability to work with sequences and its gating mechanism.

  https://www.kdnuggets.com/2020/07/pytorch-lstm-text-generation-tutorial.html

• Deep Learning in Finance: Is This The Future of the Financial Industry?

  Get a handle on how deep learning is affecting the finance industry, and identify resources to further this understanding and increase your knowledge of the various aspects.

  https://www.kdnuggets.com/2020/07/deep-learning-finance-future-financial-industry.html

• LSTM for time series prediction

  Learn how to develop a LSTM neural network with PyTorch on trading data to predict future prices by mimicking actual values of the time series data.

  https://www.kdnuggets.com/2020/04/lstm-time-series-prediction.html

• Dive Into Deep Learning: The Free eBook

  This freely available text on deep learning is fully interactive and incredibly thorough. Check out "Dive Into Deep Learning" now and increase your neural networks theoretical understanding and practical implementation skills.

  https://www.kdnuggets.com/2020/04/dive-deep-learning-book.html

• Federated Learning: An Introduction

  Improving machine learning models and making them more secure by training on decentralized data.

  https://www.kdnuggets.com/2020/04/federated-learning-introduction.html

• 10 Must-read Machine Learning Articles (March 2020)">10 Must-read Machine Learning Articles (March 2020)

  This list will feature some of the recent work and discoveries happening in machine learning, as well as guides and resources for both beginner and intermediate data scientists.

  https://www.kdnuggets.com/2020/04/10-must-read-machine-learning-articles-march-2020.html

• How (not) to use Machine Learning for time series forecasting: The sequel">How (not) to use Machine Learning for time series forecasting: The sequel

  Developing machine learning predictive models from time series data is an important skill in Data Science. While the time element in the data provides valuable information for your model, it can also lead you down a path that could fool you into something that isn't real. Follow this example to learn how to spot trouble in time series data before it's too late.

  https://www.kdnuggets.com/2020/03/machine-learning-time-series-forecasting-sequel.html

• Phishytics – Machine Learning for Detecting Phishing Websites

  Since phishing is such a widespread problem in the cybersecurity domain, let us take a look at the application of machine learning for phishing website detection.

  https://www.kdnuggets.com/2020/03/phishytics-machine-learning-detecting-phishing-websites.html

• 20 AI, Data Science, Machine Learning Terms You Need to Know in 2020 (Part 2)">20 AI, Data Science, Machine Learning Terms You Need to Know in 2020 (Part 2)

  We explain important AI, ML, Data Science terms you should know in 2020, including Double Descent, Ethics in AI, Explainability (Explainable AI), Full Stack Data Science, Geospatial, GPT-2, NLG (Natural Language Generation), PyTorch, Reinforcement Learning, and Transformer Architecture.

  https://www.kdnuggets.com/2020/03/ai-data-science-machine-learning-key-terms-part2.html

• Illustrating the Reformer

  In this post, we will try to dive into the Reformer model and try to understand it with some visual guides.

  https://www.kdnuggets.com/2020/02/illustrating-reformer.html

• Top 10 AI, Machine Learning Research Articles to know">Top 10 AI, Machine Learning Research Articles to know

  We’ve seen many predictions for what new advances are expected in the field of AI and machine learning. Here, we review a “data set” based on what researchers were apparently studying at the turn of the decade to take a fresh glimpse into what might come to pass in 2020.

  https://www.kdnuggets.com/2020/01/top-10-ai-ml-articles-to-know.html

• NLP Year in Review — 2019

  In this blog post, I want to highlight some of the most important stories related to machine learning and NLP that I came across in 2019.

  https://www.kdnuggets.com/2020/01/nlp-year-review-2019.html

• The Book to Start You on Machine Learning">The Book to Start You on Machine Learning

  This book is thought for beginners in Machine Learning, that are looking for a practical approach to learning by building projects and studying the different Machine Learning algorithms within a specific context.

  https://www.kdnuggets.com/2020/01/book-start-machine-learning.html

• A Comprehensive Guide to Natural Language Generation

  Follow this overview of Natural Language Generation covering its applications in theory and practice. The evolution of NLG architecture is also described from simple gap-filling to dynamic document creation along with a summary of the most popular NLG models.

  https://www.kdnuggets.com/2020/01/guide-natural-language-generation.html

• Automatic Text Summarization in a Nutshell

  Marketing scientist Kevin Gray asks Dr. Anna Farzindar of the University of Southern California about Automatic Text Summarization and the various ways it is used.

  https://www.kdnuggets.com/2019/12/automatic-text-summarization-nutshell.html

• The 4 Hottest Trends in Data Science for 2020">The 4 Hottest Trends in Data Science for 2020

  The field of Data Science is growing with new capabilities and reach into every industry. With digital transformations occurring in organizations around the world, 2019 included trends of more companies leveraging more data to make better decisions. Check out these next trends in Data Science expected to take off in 2020.

  https://www.kdnuggets.com/2019/12/4-hottest-trends-data-science-2020.html

• 10 Free Top Notch Machine Learning Courses">10 Free Top Notch Machine Learning Courses

  Are you interested in studying machine learning over the holidays? This collection of 10 free top notch courses will allow you to do just that, with something for every approach to improving your machine learning skills.

  https://www.kdnuggets.com/2019/12/10-free-top-notch-courses-machine-learning.html

• The Rise of User-Generated Data Labeling

  Let’s say your project is humongous and needs data labeling to be done continuously - while you’re on-the-go, sleeping, or eating. I’m sure you’d appreciate User-generated Data Labeling. I’ve got 6 interesting examples to help you understand this, let’s dive right in!

  https://www.kdnuggets.com/2019/12/rise-user-generated-data-labeling.html

• Data Science Curriculum Roadmap">Data Science Curriculum Roadmap

  What follows is a set of broad recommendations, and it will inevitably require a lot of adjustments in each implementation. Given that caveat, here are our curriculum recommendations.

  https://www.kdnuggets.com/2019/12/data-science-curriculum-roadmap.html

• Text Encoding: A Review

  We will focus here exactly on that part of the analysis that transforms words into numbers and texts into number vectors: text encoding.

  https://www.kdnuggets.com/2019/11/text-encoding-review.html

• Research Guide for Transformers

  The problem with RNNs and CNNs is that they aren’t able to keep up with context and content when sentences are too long. This limitation has been solved by paying attention to the word that is currently being operated on. This guide will focus on how this problem can be addressed by Transformers with the help of deep learning.

  https://www.kdnuggets.com/2019/10/research-guide-transformers.html

• Anomaly Detection, A Key Task for AI and Machine Learning, Explained

  One way to process data faster and more efficiently is to detect abnormal events, changes or shifts in datasets. Anomaly detection refers to identification of items or events that do not conform to an expected pattern or to other items in a dataset that are usually undetectable by a human expert.

  https://www.kdnuggets.com/2019/10/anomaly-detection-explained.html

• 10 Free Top Notch Natural Language Processing Courses">10 Free Top Notch Natural Language Processing Courses

  Are you looking to learn natural language processing? This collection of 10 free top notch courses will allow you to do just that, with something for every approach to learning NLP and its varied topics.

  https://www.kdnuggets.com/2019/10/10-free-top-notch-courses-natural-language-processing.html