Search results for Generative Adversarial Network

Found 130 documents, 5922 searched:

• Semi-supervised learning with Generative Adversarial Networks

  The paper discussed in this post, Semi-supervised learning with Generative Adversarial Networks, utilizes a GAN architecture for multi-label classification.

  https://www.kdnuggets.com/2020/01/semi-supervised-learning-generative-adversarial-networks.html

• Intro to Adversarial Machine Learning and Generative Adversarial Networks

  In this crash course on GANs, we explore where they fit into the pantheon of generative models, how they've changed over time, and what the future has in store for this area of machine learning.

  https://www.kdnuggets.com/2019/10/adversarial-machine-learning-generative-adversarial-networks.html

• Graduating in GANs: Going From Understanding Generative Adversarial Networks to Running Your Own

  Read how generative adversarial networks (GANs) research and evaluation has developed then implement your own GAN to generate handwritten digits.

  https://www.kdnuggets.com/2019/04/graduating-gans-understanding-generative-adversarial-networks.html

• Generative Adversarial Networks – Key Milestones and State of the Art

  We provide an overview of Generative Adversarial Networks (GANs), discuss challenges in GANs learning, and examine two promising GANs: the RadialGAN, designed for numbers, and the StyleGAN, which does style transfer for images.

  https://www.kdnuggets.com/2019/04/future-generative-adversarial-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

• Generative Adversarial Networks – Paper Reading Road Map

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

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

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

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

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

• Generative Adversarial Networks – Hot Topic in Machine Learning

  What is Generative Adversarial Networks (GAN) ? A very illustrative explanation of GAN is presented here with simple examples like predicting next frame in video sequence or predicting next word while typing in google search.

  https://www.kdnuggets.com/2017/01/generative-adversarial-networks-hot-topic-machine-learning.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

• Free From Google: Generative AI Learning Path

  Want to keep updated about Generative AI? Check these free courses and resources from Google Cloud.

  https://www.kdnuggets.com/2023/07/free-google-generative-ai-learning-path.html

• Unveiling the Power of Meta’s Llama 2: A Leap Forward in Generative AI?

  This article explores the technical details and implications of Meta's newly released Llama 2, a large language model that promises to revolutionize the field of generative AI. We delve into its capabilities, performance, and potential applications, while also discussing its open-source nature and the company's commitment to safety and transparency.

  https://www.kdnuggets.com/2023/07/unveiling-power-metas-llama-2-leap-forward-generative-ai.html

• 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

• Synthetic Data Platforms: Unlocking the Power of Generative AI for Structured Data

  The article highlights various use cases of synthetic data, including generating confidential data, rebalancing imbalanced data, and imputing missing data points. It also provides information on popular synthetic data generation tools such as MOSTLY AI, SDV, and YData.

  https://www.kdnuggets.com/2023/07/synthetic-data-platforms-unlocking-power-generative-ai-structured-data.html

• Are Data Scientists Still Needed in the Age of Generative AI?

  The Rise of ChatGPT.

  https://www.kdnuggets.com/2023/06/data-scientists-still-needed-age-generative-ai.html

• The Future of AI: Exploring the Next Generation of Generative Models

  What Generative AI is currently capable of and the current challenges it needs to overcome to explore the next wave of generative AI models?

  https://www.kdnuggets.com/2023/05/future-ai-exploring-next-generation-generative-models.html

• Unveiling the Potential of CTGAN: Harnessing Generative AI for Synthetic Data

  CTGAN and other generative AI models can create synthetic tabular data for ML training, data augmentation, testing, privacy-preserving sharing, and more.

  https://www.kdnuggets.com/2023/04/unveiling-potential-ctgan-harnessing-generative-ai-synthetic-data.html

• What is Adversarial Neural Cryptography?

  The novel approach combines GANs and cryptography in a single, powerful security method.

  https://www.kdnuggets.com/2021/04/adversarial-neural-cryptography.html

• Adversarial generation of extreme samples

  In order to mitigate risks when modelling extreme events, it is vital to be able to generate a wide range of extreme, and realistic, scenarios. Researchers from the National University of Singapore and IIT Bombay have developed an approach to do just that.

  https://www.kdnuggets.com/2021/02/adversarial-generation-extreme-samples.html

• Machine learning adversarial attacks are a ticking time bomb

  Software developers and cyber security experts have long fought the good fight against vulnerabilities in code to defend against hackers. A new, subtle approach to maliciously targeting machine learning models has been a recent hot topic in research, but its statistical nature makes it difficult to find and patch these so-called adversarial attacks. Such threats in the real-world are becoming imminent as the adoption of machine learning spreads, and a systematic defense must be implemented.

  https://www.kdnuggets.com/2021/01/machine-learning-adversarial-attacks.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

• Microsoft Research Unveils Three Efforts to Advance Deep Generative Models

  Optimus, FQ-GAN and Prevalent bring new ideas to apply generative models at large scale.

  https://www.kdnuggets.com/2020/05/microsoft-research-three-efforts-advance-deep-generative-models.html

• Uber Creates Generative Teaching Networks to Better Train Deep Neural Networks

  The new technique can really improve how deep learning models are trained at scale.

  https://www.kdnuggets.com/2020/01/uber-generative-teaching-networks-train-neural-networks.html

• Interpolation in Autoencoders via an Adversarial Regularizer

  Adversarially Constrained Autoencoder Interpolation (ACAI; Berthelot et al., 2018) is a regularization procedure that uses an adversarial strategy to create high-quality interpolations of the learned representations in autoencoders.

  https://www.kdnuggets.com/2019/03/interpolation-autoencoders-adversarial-regularizer.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

• 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

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

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

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

• MNIST Generative Adversarial Model in Keras

  This post discusses and demonstrates the implementation of a generative adversarial network in Keras, using the MNIST dataset.

  https://www.kdnuggets.com/2016/07/mnist-generative-adversarial-model-keras.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

• Deep Learning Adversarial Examples – Clarifying Misconceptions

  Google scientist clarifies misconceptions and myths around Deep Learning Adversarial Examples, including: they do not occur in practice, Deep Learning is more vulnerable to them, they can be easily solved, and human brains make similar mistakes.

  https://www.kdnuggets.com/2015/07/deep-learning-adversarial-examples-misconceptions.html

• Generate Synthetic Time-series Data with Open-source Tools

  An introduction to the generative adversarial network model DoppelGANger, and how you can use a new open-source PyTorch implementation of it to create high-quality synthetic time-series data.

  https://www.kdnuggets.com/2022/06/generate-synthetic-timeseries-data-opensource-tools.html

• Generate Realistic Human Face using GAN

  This article contain a brief intro to Generative Adversarial Network(GAN) and how to build a Human Face Generator.

  https://www.kdnuggets.com/2020/03/generate-realistic-human-face-using-gan.html

• Which Face is Real?

  Which Face Is Real? was developed based on Generative Adversarial Networks as a web application in which users can select which image they believe is a true person and which was synthetically generated. The person in the synthetically generated photo does not exist.

  https://www.kdnuggets.com/2019/04/which-face-real-stylegan.html

• GANs Need Some Attention, Too

  Self-Attention Generative Adversarial Networks (SAGAN; Zhang et al., 2018) are convolutional neural networks that use the self-attention paradigm to capture long-range spatial relationships in existing images to better synthesize new images.

  https://www.kdnuggets.com/2019/03/gans-need-some-attention-too.html

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

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

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

• Synthetic Data for Machine Learning

  You don't always have high-quality labeled datasets for supervised machine learning. Learn about why you should augment your real data with synthetic data as well as the ways to generate it.

  https://www.kdnuggets.com/synthetic-data-for-machine-learning

• Free MIT Course: TinyML and Efficient Deep Learning Computing

  Curious about optimizing AI for everyday devices? Dive into the complete overview of MIT's TinyML and Efficient Deep Learning Computing course. Explore strategies to make AI smarter on small devices. Read the full article for an in-depth look!

  https://www.kdnuggets.com/free-mit-course-tinyml-and-efficient-deep-learning-computing

• 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

• Exploring the Latest Trends in AI/DL: From Metaverse to Quantum Computing

  The author discusses several emerging trends in Artificial Intelligence and Deep Learning such as Metaverse and Quantum Computing.

  https://www.kdnuggets.com/2023/07/exploring-latest-trends-aidl-metaverse-quantum-computing.html

• Ten Years of AI in Review

  From image classification to chatbot therapy.

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

• Fighting AI with AI Fraud Monitoring for Deepfake Applications

  An overview of Deepfake, and how they are being used to perpetuate financial fraud.

  https://www.kdnuggets.com/2023/05/fighting-ai-ai-fraud-monitoring-deepfake-applications.html

• Top 19 Skills You Need to Know in 2023 to Be a Data Scientist

  Skills like the ability to clean, transform, statistically analyze, visualize, communicate, and predict data.

  https://www.kdnuggets.com/2023/04/top-19-skills-need-know-2023-data-scientist.html

• Master Transformers with This Free Stanford Course!

  If you want a deep dive on transformers, this Stanford course has made its courseware freely available, including lecture videos, readings, assignments, and more.

  https://www.kdnuggets.com/2022/09/master-transformers-free-stanford-course.html

• 7 Things You Didn’t Know You Could do with a Low Code Tool

  Surprisingly easy solutions for complex data problems.

  https://www.kdnuggets.com/2022/09/7-things-didnt-know-could-low-code-tool.html

• Best Practices for Creating Domain-Specific AI Models

  Here are some best practices and techniques for domain-specific model adaptation that worked for us time and again.

  https://www.kdnuggets.com/2022/07/best-practices-creating-domainspecific-ai-models.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 Generate Synthetic Tabular Dataset

  Check out this article on using CTGANs to create synthetic datasets for reducing privacy risks, training and testing machine learning models, and developing data-centric AI products.

  https://www.kdnuggets.com/2022/03/generate-tabular-synthetic-dataset.html

• How To Use Synthetic Data To Overcome Data Shortages For Machine Learning Model Training

  It takes time and considerable resources to collect, document, and clean data before it can be used. But there is a way to address this challenge – by using synthetic data.

  https://www.kdnuggets.com/2022/03/synthetic-data-overcome-data-shortages-machine-learning-model-training.html

• Data Labeling for Machine Learning: Market Overview, Approaches, and Tools

  So much of data science and machine learning is founded on having clean and well-understood data sources that it is unsurprising that the data labeling market is growing faster than ever. Here, we highlight many of the top players in this industry and the techniques they use to help you consider which might make a good partner for your needs.

  https://www.kdnuggets.com/2021/12/data-labeling-ml-overview-and-tools.html

• Introduction to AutoEncoder and Variational AutoEncoder (VAE)">Introduction to AutoEncoder and Variational AutoEncoder (VAE)

  Autoencoders and their variants are interesting and powerful artificial neural networks used in unsupervised learning scenarios. Learn how autoencoders perform in their different approaches and how to implement with Keras on the instructional data set of the MNIST digits.

  https://www.kdnuggets.com/2021/10/introduction-autoencoder-variational-autoencoder-vae.html

• Data Science Cheat Sheet 2.0">Data Science Cheat Sheet 2.0

  Check out this helpful, 5-page data science cheat sheet to assist with your exam reviews, interview prep, and anything in-between.

  https://www.kdnuggets.com/2021/09/data-science-cheat-sheet.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

• 10 Amazing Machine Learning Projects of 2020">10 Amazing Machine Learning Projects of 2020

  So much progress in AI and machine learning happened in 2020, especially in the areas of AI-generating creativity and low-to-no-code frameworks. Check out these trending and popular machine learning projects released last year, and let them inspire your work throughout 2021.

  https://www.kdnuggets.com/2021/03/10-amazing-machine-learning-projects-2020.html

• An overview of synthetic data types and generation methods

  Synthetic data can be used to test new products and services, validate models, or test performances because it mimics the statistical property of production data. Today you'll find different types of structured and unstructured synthetic data.

  https://www.kdnuggets.com/2021/02/overview-synthetic-data-types-generation-methods.html

• Mastering TensorFlow Variables in 5 Easy Steps

  Learn how to use TensorFlow Variables, their differences from plain Tensor objects, and when they are preferred over these Tensor objects | Deep Learning with TensorFlow 2.x.

  https://www.kdnuggets.com/2021/01/mastering-tensorflow-variables-5-easy-steps.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

• 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

• 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

• Deep Learning for Virtual Try On Clothes – Challenges and Opportunities

  Learn about the experiments by MobiDev for transferring 2D clothing items onto the image of a person. As part of their efforts to bring AR and AI technologies into virtual fitting room development, they review the deep learning algorithms and architecture under development and the current state of results.

  https://www.kdnuggets.com/2020/10/deep-learning-virtual-try-clothes.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

• Tools to Spot Deepfakes and AI-Generated Text

  The technologies that generate deepfake content is at the forefront of manipulating humans. While the research developing these algorithms is fascinating and will lead to powerful tools that enhance the way people create and work, in the wrong hands, these same tools drive misinformation at a scale we can't yet imagine. Stopping these bad actors using awesome tools is in your hands.

  https://www.kdnuggets.com/2020/06/dont-click-this-how-spot-deepfakes.html

• AI and Machine Learning for Healthcare">AI and Machine Learning for Healthcare

  Traditional business and technology sectors are not the only fields being impacted by AI. Healthcare is a field that is thought to be highly suitable for the applications of AI tools and techniques.

  https://www.kdnuggets.com/2020/05/ai-machine-learning-healthcare.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

• 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

• Build an app to generate photorealistic faces using TensorFlow and Streamlit

  We’ll show you how to quickly build a Streamlit app to synthesize celebrity faces using GANs, Tensorflow, and st.cache.

  https://www.kdnuggets.com/2020/04/app-generate-photorealistic-faces-tensorflow-streamlit.html

• Prepare for a Long Battle against Deepfakes

  While deepfakes threaten to destroy our perception of reality, the tech giants are throwing down the gauntlet and working to enhance the state of the art in combating doctored videos and images.

  https://www.kdnuggets.com/2020/02/long-battle-against-deepfakes.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

• 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

• Deepfakes Security Risks

  Deepfakes have instilled panic in experts since they first emerged in 2017. Microsoft and Facebook have recently announced a contest to identify deepfakes more efficiently.

  https://www.kdnuggets.com/2020/01/deepfakes-security-risks.html

• AI, Analytics, Machine Learning, Data Science, Deep Learning Research Main Developments in 2019 and Key Trends for 2020">AI, Analytics, Machine Learning, Data Science, Deep Learning Research Main Developments in 2019 and Key Trends for 2020

  As we say goodbye to one year and look forward to another, KDnuggets has once again solicited opinions from numerous research & technology experts as to the most important developments of 2019 and their 2020 key trend predictions.

  https://www.kdnuggets.com/2019/12/predictions-ai-machine-learning-data-science-research.html

• Pro Tips: How to deal with Class Imbalance and Missing Labels

  Your spectacularly-performing machine learning model could be subject to the common culprits of class imbalance and missing labels. Learn how to handle these challenges with techniques that remain open areas of new research for addressing real-world machine learning problems.

  https://www.kdnuggets.com/2019/11/tips-class-imbalance-missing-labels.html

• Research Guide: Advanced Loss Functions for Machine Learning Models

  This guide explores research centered on a variety of advanced loss functions for machine learning models.

  https://www.kdnuggets.com/2019/11/research-guide-advanced-loss-functions-machine-learning-models.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

• Research Guide for Video Frame Interpolation with Deep Learning

  In this research guide, we’ll look at deep learning papers aimed at synthesizing video frames within an existing video.

  https://www.kdnuggets.com/2019/10/research-guide-video-frame-interpolation-deep-learning.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

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

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

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

• A Summary of DeepMind’s Protein Folding Upset at CASP13">A Summary of DeepMind’s Protein Folding Upset at CASP13

  Learn how DeepMind dominated the last CASP competition for advancing protein folding models. Their approach using gradient descent is today's state of the art for predicting the 3D structure of a protein knowing only its comprising amino acid compounds.

  https://www.kdnuggets.com/2019/07/deepmind-protein-folding-upset.html

• 10 New Things I Learnt from fast.ai Course V3

  Fastai offers some really good courses in machine learning and deep learning for programmers. I recently took their "Practical Deep Learning for Coders" course and found it really interesting. Here are my learnings from the course.

  https://www.kdnuggets.com/2019/06/things-learnt-fastai-course.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

• 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

• 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

• Deep Multi-Task Learning – 3 Lessons Learned

  We share specific points to consider when implementing multi-task learning in a Neural Network (NN) and present TensorFlow solutions to these issues.

  https://www.kdnuggets.com/2019/02/deep-multi-task-learning.html

• Machine Learning & AI Main Developments in 2018 and Key Trends for 2019">Machine Learning & AI Main Developments in 2018 and Key Trends for 2019

  As we bid farewell to one year and look to ring in another, KDnuggets has solicited opinions from numerous Machine Learning and AI experts as to the most important developments of 2018 and their 2019 key trend predictions.

  https://www.kdnuggets.com/2018/12/predictions-machine-learning-ai-2019.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

• 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

• The Long Tail of Medical Data

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

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

• Recent Advances for a Better Understanding of Deep Learning">Recent Advances for a Better Understanding of Deep Learning

  A summary of the newest deep learning trends, including Non Convex Optimization, Overparametrization and Generalization, Generative Models, Stochastic Gradient Descent (SGD) and more.

  https://www.kdnuggets.com/2018/10/recent-advances-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

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

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

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

• How GOAT Taught a Machine to Love Sneakers

  Embeddings are a fantastic tool to create reusable value with inherent properties similar to how humans interpret objects. GOAT uses deep learning to generate these for their entire sneaker catalogue.

  https://www.kdnuggets.com/2018/08/goat-taught-machine-love-sneakers.html

• How to Organize Data Labeling for Machine Learning: Approaches and Tools

  The main challenge for a data science team is to decide who will be responsible for labeling, estimate how much time it will take, and what tools are better to use.

  https://www.kdnuggets.com/2018/05/data-labeling-machine-learning.html

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

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

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

• A “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

• 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

• Visual Aesthetics: Judging photo quality using AI techniques

  We built a deep learning system that can automatically analyze and score an image for aesthetic quality with high accuracy. Check the demo and see your photo measures up!

  https://www.kdnuggets.com/2018/01/visual-aesthetics-photo-quality-ai.html

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

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

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

• 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

• 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

• Deep Learning – Past, Present, and Future">Deep Learning – Past, Present, and Future

  There is a lot of buzz around deep learning technology. First developed in the 1940s, deep learning was meant to simulate neural networks found in brains, but in the last decade 3 key developments have unleashed its potential.

  https://www.kdnuggets.com/2017/05/deep-learning-big-deal.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

• 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

• How To Stay Competitive In Machine Learning Business

  To stay competitive in machine learning business, you have to be superior than your rivals and not the best possible – says one of the leading machine learning expert. Simple rules are defined here to make that happen. Let’s see how.

  https://www.kdnuggets.com/2017/01/stay-competitive-machine-learning-business.html

• Machine Learning & Artificial Intelligence: Main Developments in 2016 and Key Trends in 2017">Machine Learning & Artificial Intelligence: Main Developments in 2016 and Key Trends in 2017

  As 2016 comes to a close and we prepare for a new year, check out the final instalment in our "Main Developments in 2016 and Key Trends in 2017" series, where experts weigh in with their opinions.

  https://www.kdnuggets.com/2016/12/machine-learning-artificial-intelligence-main-developments-2016-key-trends-2017.html

• Deep Learning Research Review: Reinforcement Learning

  This edition of Deep Learning Research Review explains recent research papers in Reinforcement Learning (RL). If you don't have the time to read the top papers yourself, or need an overview of RL in general, this post has you covered.

  https://www.kdnuggets.com/2016/11/deep-learning-research-review-reinforcement-learning.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

• Yann LeCun Quora Session Overview

  Here is a quick oversight, with excerpts, of the Yann LeCun Quora Session which took place on Thursday July 28, 2016.

  https://www.kdnuggets.com/2016/08/yann-lecun-quora-session.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

• 7 Steps to Mastering Large Language Models (LLMs)

  Large Language Models (LLMs) have unlocked a new era in natural language processing. So why not learn more about them? Go from learning what large language models are to building and deploying LLM apps in 7 easy steps with this guide.

  https://www.kdnuggets.com/7-steps-to-mastering-large-language-models-llms

• A Deep Dive into GPT Models: Evolution & Performance Comparison

  The blog focuses on GPT models, providing an in-depth understanding and analysis. It explains the three main components of GPT models: generative, pre-trained, and transformers.

  https://www.kdnuggets.com/2023/05/deep-dive-gpt-models.html

• Learn Machine Learning 4X Faster by Participating in Competitions

  Participating in competitions has taught me everything about machine learning and how It can help you learn multiple domains faster than online courses.

  https://www.kdnuggets.com/2022/01/learn-machine-learning-4x-faster-participating-competitions.html

• 2021: A Year Full of Amazing AI papers — A Review

  A curated list of the latest breakthroughs in AI by release date with a clear video explanation, link to a more in-depth article, and code.

  https://www.kdnuggets.com/2021/12/2021-year-review-amazing-ai-papers.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

• 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

• Software 2.0 takes shape

  Software developers remain in very high demand as many organizations continue to experience workloads that far exceed available talent. AI-enhanced approaches that automate more areas of the software development lifecycle are in development with interesting potentials for how machine learning and natural language processing can significantly impact how software is designed, developed, tested, and deployed in the future.

  https://www.kdnuggets.com/2020/10/software-20-takes-shape.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

• 7 Tips for Dealing With Small Data

  At my workplace, we produce a lot of functional prototypes for our clients. Because of this, I often need to make Small Data go a long way. In this article, I’ll share 7 tips to improve your results when prototyping with small datasets.

  https://www.kdnuggets.com/2019/07/7-tips-dealing-small-data.html

• Cartoon: AI + Self-Driving + BBQ = ?

  KDnuggets Cartoon looks at what happens when AI and self-driving technology collide with the traditional summer pastime of grilling.

  https://www.kdnuggets.com/2019/07/cartoon-self-driving-grill.html

• Explainable Artificial Intelligence (Part 2) – Model Interpretation Strategies

  The aim of this article is to give you a good understanding of existing, traditional model interpretation methods, their limitations and challenges. We will also cover the classic model accuracy vs. model interpretability trade-off and finally take a look at the major strategies for model interpretation.

  https://www.kdnuggets.com/2018/12/explainable-ai-model-interpretation-strategies.html

• Only Numpy: Implementing GANs and Adam Optimizer using Numpy">Only Numpy: Implementing GANs and Adam Optimizer using Numpy

  This post is an implementation of GANs and the Adam optimizer using only Python and Numpy, with minimal focus on the underlying maths involved.

  https://www.kdnuggets.com/2018/08/only-numpy-implementing-gans-adam-optimizer.html

• How To Create Natural Language Semantic Search For Arbitrary Objects With Deep Learning

  An end-to-end example of how to build a system that can search objects semantically.

  https://www.kdnuggets.com/2018/06/natural-language-semantic-search-arbitrary-objects-deep-learning.html

• Machine Learning & Artificial Intelligence: Main Developments in 2017 and Key Trends in 2018">Machine Learning & Artificial Intelligence: Main Developments in 2017 and Key Trends in 2018

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

  https://www.kdnuggets.com/2017/12/machine-learning-ai-main-developments-2017-key-trends-2018.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

• Detecting Facial Features Using Deep Learning

  A challenging task in the past was detection of faces and their features like eyes, nose, mouth and even deriving emotions from their shapes. This task can be now “magically” solved by deep learning and any talented teenager can do it in a few hours.

  https://www.kdnuggets.com/2017/09/detecting-facial-features-deep-learning.html

• Cartoon: The First Ever Self-Driving, Deep Learning Grill

  New KDnuggets Cartoon looks at what happens when self-driving craze collides with the traditional summer pastime of grilling.

  https://www.kdnuggets.com/2017/07/cartoon-self-driving-grill.html

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

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

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

• 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

• The Five Capability Levels of Deep Learning Intelligence

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

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

• Questions To Ask When Moving Machine Learning From Practice to Production

  An overview of applying machine learning techniques to solve problems in production. This articles covers some of the varied questions to ponder when incorporating machine learning into teams and processes.

  https://www.kdnuggets.com/2016/11/moving-machine-learning-practice-production.html

• The 10 Algorithms Machine Learning Engineers Need to Know">The 10 Algorithms Machine Learning Engineers Need to Know

  Read this introductory list of contemporary machine learning algorithms of importance that every engineer should understand.

  https://www.kdnuggets.com/2016/08/10-algorithms-machine-learning-engineers.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