Search results for "deep learning"

3154 documents found out of 7194 total.

• OpenML: Share, Discover and Do Machine Learning

  OpenML is designed to share, organize and reuse data, code and experiments, so that scientists can make discoveries more efficiently. It is an interesting idea to build a network of machine learning.

  https://www.kdnuggets.com/2014/08/openml-share-discover-do-machine-learning.html

• When Watson Meets Machine Learning

  Our report on a recent Cognitive Systems meetup co-sponsored by IBM Watson and NYU Center for Data Science, IBM Watson Ecosystem, and machine learning applications, from healthcare to cognitive toys. You will want Fang!

  https://www.kdnuggets.com/2014/07/watson-meets-machine-learning.html

• Vowpal Wabbit: Fast Learning on Big Data

  Vowpal Wabbit is a fast out-of-core machine learning system, which can learn from huge, terascale datasets faster than any other current algorithm. We also explain the cute name.

  https://www.kdnuggets.com/2014/05/vowpal-wabbit-fast-learning-on-big-data.html

• Blogs on AI, Analytics, Data Science, Machine Learning

  Here are some of the most interesting and regularly-updated blogs on Analytics, Big Data, Data Science, Data Mining, and Machine Learning, in alphabetical order. Blog Read more »

  https://www.kdnuggets.com/websites/blogs.html

• US/Canada Degree Programs in Data Science, Machine Learning, AI & Analytics

  Certificates | Courses | Online Masters | Europe Degrees | US/Canada Degrees United States of America   Northeast Connecticut: Central Connecticut State University (CCSU), exploring Read more »

  https://www.kdnuggets.com/education/usa-canada.html

• Certificates & Certifications in AI, Analytics, Data Science, Machine Learning

  Education | Certificates | Courses | Online Masters   Certificates Brandeis Graduate Certificate in Learning Analytics: 15-credit, five-course program to be completed in 1.5 years Read more »

  https://www.kdnuggets.com/education/analytics-data-mining-certificates.html

• Software Suites/Platforms for Analytics, Data Mining, Data Science, and Machine Learning

  commercial | free/open source A B C D E F G H I J K L M N O PQ R S T U V Read more »

  https://www.kdnuggets.com/software/suites.html

• Companies with Analytics, Data Mining, Data Science, and Machine Learning Products

  A B C D E F G H I J K L M N O P Q R S T U V W XYZ Advanced Read more »

  https://www.kdnuggets.com/companies/products.html

• Consulting Companies in AI, Analytics, Data Science, and Machine Learning

  A B C D E F G H I J K L M N O P Q R S T U V W XYZ 4i, Read more »

  https://www.kdnuggets.com/companies/consulting.html

• 3 Generations of Machine Learning and Data Mining Tools

  Three different paradigms available for implementing Machine Learning (ML) algorithms both from the literature and from the open source community.

  https://www.kdnuggets.com/2013/02/3-generations-machine-learning-data-mining-tools.html

• 5 Free Resources to Understand Neural Networks

  Here are five free resources in diverse formats and difficulty levels to acquaint with deep learning models at no cost.

  https://www.kdnuggets.com/5-free-resources-understand-neural-networks

• Keras vs. JAX: A Comparison

  This comparison analyzes and compares two salient frameworks for architecting deep learning solutions.

  https://www.kdnuggets.com/keras-vs-jax-a-comparison

• Ultimate Collection of 50 Free Courses for Mastering Data Science

  The collection includes free courses on Python, SQL, Data Analytics, Business Intelligence, Data Engineering, Machine Learning, Deep Learning, Generative AI, and MLOps.

  https://www.kdnuggets.com/ultimate-collection-of-50-free-courses-for-mastering-data-science

• WTF is Regularization and What is it For?

  This article explains the concept of regularization and its significance in machine learning and deep learning. We have discussed how regularization can be used to enhance the performance of linear models, as well as how it can be applied to improve the performance of deep learning models.

  https://www.kdnuggets.com/wtf-is-regularization-and-what-is-it-for

• KDnuggets News, December 13: 5 Super Cheat Sheets to Master Data Science • Using Google’s NotebookLM for Data Science: A Comprehensive Guide

  This week on KDnuggets: A collection of super cheat sheets that covers basic concepts of data science, probability & statistics, SQL, machine learning, and deep learning • An exploration of NotebookLM, its functionality, limitations, and advanced features essential for researchers and scientists • And much, much more!

  https://www.kdnuggets.com/newsletter-n45-2023-12-13

• 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

• Building a GPU Machine vs. Using the GPU Cloud

  The article examines the pros and cons of building an on-premise GPU machine versus using a GPU cloud service for projects involving deep learning and artificial intelligence, analyzing factors like cost, performance, operations, and scalability.

  https://www.kdnuggets.com/building-a-gpu-machine-vs-using-the-gpu-cloud

• Why Prompt Engineering is a Fad

  Various media outlets have been talking about prompt engineering with much fanfare, making it seem like it’s the ideal job — you don’t need to learn how to code, nor do you have to be knowledgeable about ML concepts like deep learning, datasets, etc. You’d agree that it seems too good to be true, right?

  https://www.kdnuggets.com/why-prompt-engineering-is-a-fad

• KDnuggets News, October 27: 5 Free Books to Master Data Science • 7 Steps to Mastering LLMs

  This week on KDnuggets: Go from learning what large language models are to building and deploying LLM apps in 7 steps • Check this list of free books for learning Python, statistics, linear algebra, machine learning and deep learning • And much, much more!

  https://www.kdnuggets.com/2023/n38.html

• 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

• KDnuggets News, September 27: ChatGPT Projects Cheat Sheet • Introduction to PyTorch & Lightning AI

  10 ChatGPT Projects Cheat Sheet • Introduction to Deep Learning Libraries: PyTorch and Lightning AI • Top 5 Free Alternatives to GPT-4 • Machine Learning Evaluation Metrics: Theory and Overview • Kick Ass Midjourney Prompts with Poe

  https://www.kdnuggets.com/2023/n35.html

• KDnuggets 30th Anniversary Interview with Founder Gregory Piatetsky-Shapiro

  Gregory Piatetsky-Shapiro founded KDnuggets 30 years ago, after organizing early workshops on knowledge discovery. In this retrospective interview, he reflects on KDnuggets' growth, key innovations like deep learning, and concerns about AI's societal impact.

  https://www.kdnuggets.com/30th-anniversary-interview-with-founder-gregory-piatetsky-shapiro

• Keras 3.0: Everything You Need To Know

  Unlock the power of AI collaboration with Keras 3.0! Seamlessly switch between TensorFlow, JAX, and PyTorch, revolutionizing your deep learning projects. Read now and stay ahead in the world of AI.

  https://www.kdnuggets.com/2023/07/keras-30-everything-need-know.html

• 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

• Introduction to Safetensors

  Introducing a new tool that offers speed, efficiency, cross-platform compatibility, user-friendliness, and security for deep learning applications.

  https://www.kdnuggets.com/2023/07/introduction-safetensors.html

• A Complete Collection of Data Science Free Courses – Part 2

  The second part covers the list of Machine Learning, Deep Learning, Computer Vision, Natural Language Processing, Data Engineering, and MLOps.

  https://www.kdnuggets.com/2023/03/complete-collection-data-science-free-courses-part-2.html

• 5 Free Data Science Books You Must Read in 2023

  Get your hands on these gems to learn Python, data analytics, machine learning, and deep learning.

  https://www.kdnuggets.com/2023/01/5-free-data-science-books-must-read-2023.html

• Data Science Minimum: 10 Essential Skills You Need to Know to Start Doing Data Science

  Data science is ever-evolving, so mastering its foundational technical and soft skills will help you be successful in a career as a Data Scientist, as well as pursue advance concepts, such as deep learning and artificial intelligence.

  https://www.kdnuggets.com/2020/10/data-science-minimum-10-essential-skills.html

• A Guide to Train an Image Classification Model Using Tensorflow

  Classify images at scale and with very high accuracy with the advent of machine learning and deep learning algorithms.

  https://www.kdnuggets.com/2022/12/guide-train-image-classification-model-tensorflow.html

• 5 Python Projects for Data Science Portfolio

  Get more experience by working on web scraping, data analytics, time-series forecasting, machine learning, and deep learning projects.

  https://www.kdnuggets.com/2022/12/5-python-projects-data-science-portfolio.html

• KDnuggets Top Posts for October 2022: 10 Cheat Sheets You Need To Ace Data Science Interview

  10 Cheat Sheets You Need To Ace Data Science Interview • 7 Free Platforms for Building a Strong Data Science Portfolio • The Complete Free PyTorch Course for Deep Learning • 3 Valuable Skills That Have Doubled My Income as a Data Scientist • 25 Advanced SQL Interview Questions for Data Scientists • A Data Science Portfolio That Will Land You The Job in 2022 • Top Free Git GUI Clients for Beginners • Essential Books You Need to Become a Data Engineer

  https://www.kdnuggets.com/2022/11/top-posts-october-2022.html

• 10 Cheat Sheets You Need To Ace Data Science Interview

  The only cheat you need for a job interview and data professional life. It includes SQL, web scraping, statistics, data wrangling and visualization, business intelligence, machine learning, deep learning, NLP, and super cheat sheets.

  https://www.kdnuggets.com/2022/10/10-cheat-sheets-need-ace-data-science-interview.html

• The Complete Collection of Data Science Projects – Part 2

  The second part covers the list of Machine Learning, Deep Learning, Computer Vision, Natural Language Processing, Data Engineering, and MLOps.

  https://www.kdnuggets.com/2022/08/complete-collection-data-science-projects-part-2.html

• Online Training and Workshops with Nvidia

  Learn about the Nvidia Self-Paced Online Training from their Deep Learning Institute.

  https://www.kdnuggets.com/2022/07/online-training-workshops-nvidia.html

• The Complete Collection of Data Science Interviews – Part 2

  The second part covers the list of Data Management, Data Engineering, Machine Learning, Deep Learning, Natural Language Processing, MLOps, Cloud Computing, and AI Manager interview questions.

  https://www.kdnuggets.com/2022/06/complete-collection-data-science-interviews-part-2.html

• The Complete Collection of Data Science Books – Part 2

  Read the best books on Machine Learning, Deep Learning, Computer Vision, Natural Language Processing, MLOps, Robotics, IoT, AI Products Management, and Data Science for Executives.

  https://www.kdnuggets.com/2022/05/complete-collection-data-science-books-part-2.html

• Understanding Agent Environment in AI

  The role of the agent is always very important in artificial intelligence, machine learning, and deep learning. Learn more about agents here.

  https://www.kdnuggets.com/2022/05/understanding-agent-environment-ai.html

• From Google Colab to a Ploomber Pipeline: ML at Scale with GPUs

  In this short blog, we’ll review the process of taking a POC data science pipeline (ML/Deep learning/NLP) that was conducted on Google Colab, and transforming it into a pipeline that can run parallel at scale and works with Git so the team can collaborate on.

  https://www.kdnuggets.com/2022/03/google-colab-ploomber-pipeline-ml-scale-gpus.html

• The Complete Collection of Data Science Cheat Sheets – Part 2

  A collection of cheat sheets that will help you prepare for a technical interview on Data Structures & Algorithms, Machine learning, Deep Learning, Natural Language Processing, Data Engineering, Web Frameworks.

  https://www.kdnuggets.com/2022/02/complete-collection-data-science-cheat-sheets-part-2.html

• Advanced PyTorch Lightning with TorchMetrics and Lightning Flash

  In this tutorial we will be diving deeper into two additional tools you should be using: TorchMetrics and Lightning Flash. TorchMetrics unsurprisingly provides a modular approach to define and track useful metrics across batches and devices, while Lightning Flash offers a suite of functionality facilitating more efficient transfer learning and data handling, and a recipe book of state-of-the-art approaches to typical deep learning problems.

  https://www.kdnuggets.com/2021/11/advanced-pytorch-lightning-torchmetrics-lightning-flash.html

• Learn To Reproduce Papers: Beginner’s Guide">Learn To Reproduce Papers: Beginner’s Guide

  Step-by-step instructions on how to understand Deep Learning papers and implement the described approaches.

  https://www.kdnuggets.com/2021/10/learn-reproduce-papers-beginners-guide.html

• How our Obsession with Algorithms Broke Computer Vision: And how Synthetic Computer Vision can fix it

  Deep Learning radically improved Machine Learning as a whole. The Data-Centric revolution is about to do the same. In this post, we’ll take a look at the pitfalls of mainstream Computer Vision (CV) and discuss why Synthetic Computer Vision (SCV) is the future.

  https://www.kdnuggets.com/2021/10/obsession-algorithms-broke-computer-vision.html

• Computer Vision in Agriculture

  Deep learning isn’t just for placing ads or identifying cats anymore. Instead, a slew of young startups have started to incorporate the advances in computer vision made possible through larger and larger neural networks to real working robots in the fields.

  https://www.kdnuggets.com/2021/09/computer-vision-agriculture.html

• Top Stories, Jul 12-18: Top 6 Data Science Online Courses in 2021; Become an Analytics Engineer in 90 Days

  Also: Data Scientists and ML Engineers Are Luxury Employees; Geometric foundations of Deep Learning; How Can You Distinguish Yourself from Hundreds of Other Data Science Candidates?; A Learning Path To Becoming a Data Scientist

  https://www.kdnuggets.com/2021/07/top-news-week-0712-0718.html

• Beginners Guide to Debugging TensorFlow Models

  If you are new to working with a deep learning framework, such as TensorFlow, there are a variety of typical errors beginners face when building and training models. Here, we explore and solve some of the most common errors to help you develop a better intuition for debugging in TensorFlow.

  https://www.kdnuggets.com/2021/06/beginners-guide-debugging-tensorflow-models.html

• Best Python Books for Beginners and Advanced Programmers

  Let's take a look at nine of the best Python books for both beginners and advanced programmers, covering topics such as data science, machine learning, deep learning, NLP, and more.

  https://www.kdnuggets.com/2021/05/best-python-books-beginner-advanced.html

• Applying Natural Language Processing in Healthcare

  New advances in natural language processing (NLP) based on deep learning and transfer learning have made a whole set of software use cases in healthcare viable. The Healthcare NLP Summit is a free online conference on April 6th and 7th, bringing together 30+ technical sessions from across the community that works to apply these advances in the real world.

  https://www.kdnuggets.com/2021/03/formulated-natural-language-processing-healthcare.html

• Six Times Bigger than GPT-3: Inside Google’s TRILLION Parameter Switch Transformer Model

  Google’s Switch Transformer model could be the next breakthrough in this area of deep learning.

  https://www.kdnuggets.com/2021/01/google-trillion-parameter-switch-transformer-model.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

• Covid or just a Cough? AI for detecting COVID-19 from Cough Sounds

  Increased capabilities in screening and early testing for a disease can significantly support quelling its spread and impact. Recent progress in developing deep learning AI models to classify cough sounds as a prescreening tool for COVID-19 has demonstrated promising early success. Cough-based diagnosis is non-invasive, cost-effective, scalable, and, if approved, could be a potential game-changer in our fight against COVID-19.

  https://www.kdnuggets.com/2020/12/covid-cough-ai-detecting-sounds.html

• Building AI Models for High-Frequency Streaming Data – Part Two

  Many data scientists have implemented machine or deep learning algorithms on static data or in batch, but what considerations must you make when building models for a streaming environment? In this post, we will discuss these considerations.

  https://www.kdnuggets.com/2020/12/mathworks-pt2-ai-models-streaming-data.html

• Computer Vision at Scale With Dask And PyTorch

  A tutorial on conducting image classification inference using the Resnet50 deep learning model at scale with using GPU clusters on Saturn Cloud. The results were: 40x faster computer vision that made a 3+ hour PyTorch model run in just 5 minutes.

  https://www.kdnuggets.com/2020/11/computer-vision-scale-dask-pytorch.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

• The Most Complete Guide to PyTorch for Data Scientists

  All the PyTorch functionality you will ever need while doing Deep Learning. From an Experimentation/Research Perspective.

  https://www.kdnuggets.com/2020/09/most-complete-guide-pytorch-data-scientists.html

• Artificial Intelligence for Precision Medicine and Better Healthcare

  In this article, we will focus on various machine learning, deep learning models, and applications of AI which can pave the way for a new data-centric era of discovery in healthcare.

  https://www.kdnuggets.com/2020/09/artificial-intelligence-precision-medicine-better-healthcare.html

• KDnuggets™ News 20:n36, Sep 23: New Poll: What Python IDE / Editor you used the most in 2020?; Automating Every Aspect of Your Python Project

  New Poll: What Python IDE / Editor you used the most in 2020?; Automating Every Aspect of Your Python Project; Autograd: The Best Machine Learning Library You're Not Using?; Implementing a Deep Learning Library from Scratch in Python; Online Certificates/Courses in AI, Data Science, Machine Learning; Can Neural Networks Show Imagination?

  https://www.kdnuggets.com/2020/n36.html

• KDD-2020 (virtual), the leading conference on Data Science and Knowledge Discovery, Aug 23-27 – register now

  Using an interactive VR platform, KDD-2020 brings you the latest research in AI, Data Science, Deep Learning, and Machine Learning with tutorials to improve your skills, keynotes from top experts, workshops on state-of-the-art topics and over 200 research presentations.

  https://www.kdnuggets.com/2020/08/kdd-2020-virtual-august.html

• Is depth useful for self-attention?

  Learn about recent research that is the first to explain a surprising phenomenon where in BERT/Transformer-like architectures, deepening the network does not seem to be better than widening (or, increasing the representation dimension). This empirical observation is in contrast to a fundamental premise in deep learning.

  https://www.kdnuggets.com/2020/07/depth-useful-self-attention.html

• Getting Started with TensorFlow 2">Getting Started with TensorFlow 2

  Learn about the latest version of TensorFlow with this hands-on walk-through of implementing a classification problem with deep learning, how to plot it, and how to improve its results.

  https://www.kdnuggets.com/2020/07/getting-started-tensorflow2.html

• The Most Important Fundamentals of PyTorch you Should Know">The Most Important Fundamentals of PyTorch you Should Know

  PyTorch is a constantly developing deep learning framework with many exciting additions and features. We review its basic elements and show an example of building a simple Deep Neural Network (DNN) step-by-step.

  https://www.kdnuggets.com/2020/06/fundamentals-pytorch.html

• Build Dog Breeds Classifier Step By Step with AWS Sagemaker

  This post takes you through the basic steps for creating a cloud-based deep learning dog classifier, with everything accomplished from the AWS Management Console.

  https://www.kdnuggets.com/2020/06/build-dog-breeds-classifier-aws-sagemaker.html

• Exploring the Impact of Geographic Information Systems

  GIS has mostly been behind more popular buzzwords like machine learning and deep learning. GIS has always been around us in the background being used in government, business, medicine, real estate, transport, manufacturing etc.

  https://www.kdnuggets.com/2020/04/impact-geographic-information-systems.html

• Recreating Fingerprints using Convolutional Autoencoders

  The article gets you started working with fingerprints using Deep Learning.

  https://www.kdnuggets.com/2020/03/recreating-fingerprints-using-convolutional-autoencoders.html

• Can Edge Analytics Become a Game Changer?

  Edge analytics is considered to be the future of sensor handling, and this article discusses its benefits and architecture of modern edge devices, gateways, and sensors. Deep Learning for edge analytics is also considered along with a review of experiments in human and chess figure detection using edge devices.

  https://www.kdnuggets.com/2020/02/edge-analytics-game-changer.html

• Practical Hyperparameter Optimization

  An introduction on how to fine-tune Machine and Deep Learning models using techniques such as: Random Search, Automated Hyperparameter Tuning and Artificial Neural Networks Tuning.

  https://www.kdnuggets.com/2020/02/practical-hyperparameter-optimization.html

• Disentangling disentanglement: Ideas from NeurIPS 2019

  This year’s NEURIPS-2019 Vancouver conference recently concluded and featured a dozen papers on disentanglement in deep learning. What is this idea and why is it so interesting in machine learning? This summary of these papers will give you initial insight in disentanglement as well as ideas on what you can explore next.

  https://www.kdnuggets.com/2020/01/disentangling-disentanglement-neurips-2019.html

• Google Open Sources MobileNetV3 with New Ideas to Improve Mobile Computer Vision Models

  The latest release of MobileNets incorporates AutoML and other novel ideas in mobile deep learning.

  https://www.kdnuggets.com/2019/12/google-open-sources-mobilenetv3-improve-mobile-computer-vision-models.html

• Three Methods of Data Pre-Processing for Text Classification

  This blog shows how text data representations can be used to build a classifier to predict a developer’s deep learning framework of choice based on the code that they wrote, via examples of TensorFlow and PyTorch projects.

  https://www.kdnuggets.com/2019/11/ibm-data-preprocessing-text-classification.html

• Generalization in Neural Networks

  When training a neural network in deep learning, its performance on processing new data is key. Improving the model's ability to generalize relies on preventing overfitting using these important methods.

  https://www.kdnuggets.com/2019/11/generalization-neural-networks.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

• Convolutional Neural Network for Breast Cancer Classification

  See how Deep Learning can help in solving one of the most commonly diagnosed cancer in women.

  https://www.kdnuggets.com/2019/10/convolutional-neural-network-breast-cancer-classification.html

• Using Neural Networks to Design Neural Networks: The Definitive Guide to Understand Neural Architecture Search

  A recent survey outlined the main neural architecture search methods used to automate the design of deep learning systems.

  https://www.kdnuggets.com/2019/10/using-neural-networks-design-neural-networks-definitive-guide-understand-neural-architecture-search.html

• A 2019 Guide for Automatic Speech Recognition

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

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

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

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

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

• 9 Tips For Training Lightning-Fast Neural Networks In Pytorch

  Who is this guide for? Anyone working on non-trivial deep learning models in Pytorch such as industrial researchers, Ph.D. students, academics, etc. The models we're talking about here might be taking you multiple days to train or even weeks or months.

  https://www.kdnuggets.com/2019/08/9-tips-training-lightning-fast-neural-networks-pytorch.html

• This New Google Technique Help Us Understand How Neural Networks are Thinking">This New Google Technique Help Us Understand How Neural Networks are Thinking

  Recently, researchers from the Google Brain team published a paper proposing a new method called Concept Activation Vectors (CAVs) that takes a new angle to the interpretability of deep learning models.

  https://www.kdnuggets.com/2019/07/google-technique-understand-neural-networks-thinking.html

• Building a Recommender System, Part 2

  This post explores an technique for collaborative filtering which uses latent factor models, a which naturally generalizes to deep learning approaches. Our approach will be implemented using Tensorflow and Keras.

  https://www.kdnuggets.com/2019/07/building-recommender-system-part-2.html

• 10 Gradient Descent Optimisation Algorithms + Cheat Sheet

  Gradient descent is an optimization algorithm used for minimizing the cost function in various ML algorithms. Here are some common gradient descent optimisation algorithms used in the popular deep learning frameworks such as TensorFlow and Keras.

  https://www.kdnuggets.com/2019/06/gradient-descent-algorithms-cheat-sheet.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

• How to Automate Hyperparameter Optimization

  A step-by-step guide into performing a hyperparameter optimization task on a deep learning model by employing Bayesian Optimization that uses the Gaussian Process. We used the gp_minimize package provided by the Scikit-Optimize (skopt) library to perform this task.

  https://www.kdnuggets.com/2019/06/automate-hyperparameter-optimization.html

• Probability Mass and Density Functions

  This content is part of a series about the chapter 3 on probability from the Deep Learning Book by Goodfellow, I., Bengio, Y., and Courville, A. (2016). It aims to provide intuitions/drawings/python code on mathematical theories and is constructed as my understanding of these concepts.

  https://www.kdnuggets.com/2019/05/probability-mass-density-functions.html

• Data Science with Optimus Part 1: Intro

  With Optimus you can clean your data, prepare it, analyze it, create profilers and plots, and perform machine learning and deep learning, all in a distributed fashion, because on the back-end we have Spark, TensorFlow, Sparkling Water and Keras. It’s super easy to use.

  https://www.kdnuggets.com/2019/04/data-science-with-optimus-part-1-intro.html

• Towards Automatic Text Summarization: Extractive Methods

  The basic idea looks simple: find the gist, cut off all opinions and detail, and write a couple of perfect sentences, the task inevitably ended up in toil and turmoil. Here is a short overview of traditional approaches that have beaten a path to advanced deep learning techniques.

  https://www.kdnuggets.com/2019/03/towards-automatic-text-summarization.html

• How to do Everything in Computer Vision

  The many standard tasks in computer vision all require special consideration: classification, detection, segmentation, pose estimation, enhancement and restoration, and action recognition. Let me show you how to do everything in Computer Vision with Deep Learning!

  https://www.kdnuggets.com/2019/02/everything-computer-vision.html

• How to Engineer Your Way Out of Slow Models

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

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

• An Introduction to AI">An Introduction to AI

  We provide an introduction to AI key terminologies and methodologies, covering both Machine Learning and Deep Learning, with an extensive list including Narrow AI, Super Intelligence, Classic Artificial Intelligence, and more.

  https://www.kdnuggets.com/2018/11/an-introduction-ai.html

• Latest Trends in Computer Vision Technology and Applications

  We investigate the advancements in deep learning, the rise of edge computing, object recognition with point cloud, VR and AR enhanced merged reality, semantic instance segmentation and more.

  https://www.kdnuggets.com/2018/11/trends-computer-vision-technology-applications.html

• The Most in Demand Skills for Data Scientists">The Most in Demand Skills for Data Scientists

  Data scientists are expected to know a lot — machine learning, computer science, statistics, mathematics, data visualization, communication, and deep learning. How should data scientists who want to be in demand by employers spend their learning budget?

  https://www.kdnuggets.com/2018/11/most-demand-skills-data-scientists.html

• Building a Question-Answering System from Scratch

  This part will focus on introducing Facebook sentence embeddings and how it can be used in building QA systems. In the future parts, we will try to implement deep learning techniques, specifically sequence modeling for this problem.

  https://www.kdnuggets.com/2018/10/building-question-answering-system-from-scratch.html

• Understand Why ODSC is the Most Recommended Conference for Applied Data Science

  Running 4 days, 40 training sessions, 50 workshops, and over 200 speakers, an ODSC conference offers unparalleled depth and breadth in deep learning, machine learning, and other data science topics. Save 20% offer ends tomorrow. Register now!

  https://www.kdnuggets.com/2018/10/odsc-understand-most-recommended-conference-applied-data-science.html

• Data Augmentation For Bounding Boxes: Rethinking image transforms for object detection

  Data Augmentation is one way to battle this shortage of data, by artificially augmenting our dataset. In fact, the technique has proven to be so successful that it's become a staple of deep learning systems.

  https://www.kdnuggets.com/2018/09/data-augmentation-bounding-boxes-image-transforms.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

• Analyze a Soccer (Football) Game Using Tensorflow Object Detection and OpenCV">Analyze a Soccer (Football) Game Using Tensorflow Object Detection and OpenCV

  For the data scientist within you let's use this opportunity to do some analysis on soccer clips. With the use of deep learning and opencv we can extract interesting insights from video clips

  https://www.kdnuggets.com/2018/07/analyze-soccer-game-using-tensorflow-object-detection-opencv.html

• Building a Basic Keras Neural Network Sequential Model

  The approach basically coincides with Chollet's Keras 4 step workflow, which he outlines in his book "Deep Learning with Python," using the MNIST dataset, and the model built is a Sequential network of Dense layers. A building block for additional posts.

  https://www.kdnuggets.com/2018/06/basic-keras-neural-network-sequential-model.html

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

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

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

• Boost your data science skills. Learn linear algebra.">Boost your data science skills. Learn linear algebra.

  The aim of these notebooks is to help beginners/advanced beginners to grasp linear algebra concepts underlying deep learning and machine learning. Acquiring these skills can boost your ability to understand and apply various data science algorithms.

  https://www.kdnuggets.com/2018/05/boost-data-science-skills-learn-linear-algebra.html

• KDnuggets™ News 18:n18, May 2: Blockchain Explained in 7 Python Functions; Data Science Dirty Secret; Choosing the Right Evaluation Metric

  Also: Building Convolutional Neural Network using NumPy from Scratch; Data Science Interview Guide; Implementing Deep Learning Methods and Feature Engineering for Text Data: The GloVe Model; Jupyter Notebook for Beginners: A Tutorial

  https://www.kdnuggets.com/2018/n18.html

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

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

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

• 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

• How (and Why) to Create a Good Validation Set

  The definitions of training, validation, and test sets can be fairly nuanced, and the terms are sometimes inconsistently used. In the deep learning community, “test-time inference” is often used to refer to evaluating on data in production, which is not the technical definition of a test set.

  https://www.kdnuggets.com/2017/11/create-good-validation-set.html

• Tensorflow Tutorial, Part 2 – Getting Started

  This tutorial will lay a solid foundation to your understanding of Tensorflow, the leading Deep Learning platform. The second part shows how to get started, install, and build a small test case.

  https://www.kdnuggets.com/2017/09/tensorflow-tutorial-part-2.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

• Visualizing Convolutional Neural Networks with Open-source Picasso

  Toolkits for standard neural network visualizations exist, along with tools for monitoring the training process, but are often tied to the deep learning framework. Could a general, easy-to-setup tool for generating standard visualizations provide a sanity check on the learning process?

  https://www.kdnuggets.com/2017/08/visualizing-convolutional-neural-networks-open-source-picasso.html

• ResNets, HighwayNets, and DenseNets, Oh My!

  This post walks through the logic behind three recent deep learning architectures: ResNet, HighwayNet, and DenseNet. Each make it more possible to successfully trainable deep networks by overcoming the limitations of traditional network design.

  https://www.kdnuggets.com/2016/12/resnets-highwaynets-densenets-oh-my.html

• Data Science, Predictive Analytics Main Developments in 2016 and Key Trends for 2017">Data Science, Predictive Analytics Main Developments in 2016 and Key Trends for 2017

  Key themes included the polling failures in 2016 US Elections, Deep Learning, IoT, greater focus on value and ROI, and increasing adoption of predictive analytics by the "masses" of industry.
   
  

  https://www.kdnuggets.com/2016/12/data-science-predictive-analytics-main-developments-trends.html

• R, Python Duel As Top Analytics, Data Science software – KDnuggets 2016 Software Poll Results

  R remains the leading tool, with 49% share, but Python grows faster and almost catches up to R. RapidMiner remains the most popular general Data Science platform. Big Data tools used by almost 40%, and Deep Learning usage doubles.

  https://www.kdnuggets.com/2016/06/r-python-top-analytics-data-mining-data-science-software.html

• New KDnuggets Tutorials Page: Learn R, Python, Data Visualization, Data Science, and more

  Introducing new KDnuggets Tutorials page with useful resources for learning about Business Analytics, Big Data, Data Science, Data Mining, R, Python, Data Visualization, Spark, Deep Learning and more.

  https://www.kdnuggets.com/2016/03/new-tutorials-section-r-python-data-visualization-data-science.html

• MetaMind Mastermind Richard Socher: Uncut Interview

  In a wide-ranging interview, Richard Socher opens up about MetaMind, deep learning, the nature of corporate research, and the future of machine learning.

  https://www.kdnuggets.com/2015/10/metamind-mastermind-richard-socher-deep-learning-interview.html

• Data is Ugly – Tales of Data Cleaning

  Whether you want to do business analytics or build the deep learning models, getting correct data and cleansing it appropriately remains the major task. Find out experts opinions on how you can make efficient data cleansing and collection efforts.

  https://www.kdnuggets.com/2015/08/data-ugly-tales-data-cleaning.html

• TheWalnut.io: An Easy Way to Create Algorithm Visualizations

  Google's DeepDream project has gone viral which allows to visualize the deep learning neural networks. It highlights a need for a generalized algorithm visualization tool, in this post we introduce to you one such effort.

  https://www.kdnuggets.com/2015/07/thewalnutio-algorithm-visualizations.html

• Most Viewed Big Data Videos on YouTube

  The top Big Data YouTube videos by those like Hortonworks and Kirk D. Borne cover diverse topics including Hadoop, Big Data Trends, Deep Learning, and Big Data Leadership.

  https://www.kdnuggets.com/2015/05/most-viewed-big-data-videos-youtube.html

• The Inconvenient Truth About Data Science

  Data is never clean, you will spend most of your time cleaning and preparing data, 95% of tasks do not require deep learning, and more inconvenient wisdom.

  https://www.kdnuggets.com/2015/05/data-science-inconvenient-truth.html

• Top KDnuggets tweets, Sep 19-21: Dilbert funniest cartoons on #BigData, data mining; Guess which pattern is random

  Guess which pattern is random, which machine-generated? Dilbert 20 funniest cartoons on #BigData, data mining, privacy; Data Scientist Cartoon; Neural Networks and Deep Learning, free online book (draft).

  https://www.kdnuggets.com/2014/09/top-tweets-sep19-21.html

• Vibe Coding a Private AI Financial Analyst with Python and Local LLMs

  Learn to build an AI data analyst with Python: analyzes data, detects anomalies, and generates predictions using local LLMs.

  https://www.kdnuggets.com/vibe-coding-a-private-ai-financial-analyst-with-python-and-local-llms

• 10 Best X (Twitter) Accounts to Follow for LLM Updates

  Skip the hype and follow these 10 X accounts for reliable LLM papers, product launches, and thoughtful takes on where AI is heading.

  https://www.kdnuggets.com/10-best-x-twitter-accounts-to-follow-for-llm-updates

• The 2026 Data Science Starter Kit: What to Learn First (And What to Ignore)

  New to data science? Cut through the noise with the 2026 starter kit. Which Python, SQL, and machine learning essentials are important, and what can be ignored?

  https://www.kdnuggets.com/the-2026-data-science-starter-kit-what-to-learn-first-and-what-to-ignore

• Bindu Reddy: Navigating the Path to AGI

  How the Abacus.AI CEO Views Artificial General Intelligence and the Best AI Models for Every Use Case

  https://www.kdnuggets.com/2026/02/abacus/bindu-reddy-navigating-the-path-to-agi

• 5 Time Series Foundation Models You Are Missing Out On

  Five widely adopted time series foundation models delivering accurate zero-shot forecasting across industries and time horizons.

  https://www.kdnuggets.com/5-time-series-foundation-models-you-are-missing-out-on

• 7 Under-the-Radar Python Libraries for Scalable Feature Engineering

  This article lists 7 under-the-radar Python libraries that push the boundaries of feature engineering processes at scale.

  https://www.kdnuggets.com/7-under-the-radar-python-libraries-for-scalable-feature-engineering

• 5 Breakthroughs in Graph Neural Networks to Watch in 2026

  This article outlines 5 recent breakthroughs in GNNs that are worth watching in the year ahead: from integration with LLMs to interdisciplinary scientific discoveries.

  https://www.kdnuggets.com/5-breakthroughs-in-graph-neural-networks-to-watch-in-2026

• 10 GitHub Repositories to Ace Any Tech Interview

  The most trusted GitHub repositories to help you master coding interviews, system design, backend engineering, scalability, data structures and algorithms, and machine learning interviews with confidence.

  https://www.kdnuggets.com/10-github-repositories-to-ace-any-tech-interview

• The KDnuggets Gradio Crash Course

  Build ML web apps in minutes with Gradio's Python framework. Create interactive demos for models with text, image, or audio inputs with no frontend skills needed. Deploy and share instantly.

  https://www.kdnuggets.com/the-kdnuggets-gradio-crash-course

• The Data Detox: Training Yourself for the Messy, Noisy, Real World

  In this article, we’ll use a real-life data project to explore four practical steps for preparing to deal with messy, real-life datasets.

  https://www.kdnuggets.com/the-data-detox-training-yourself-for-the-messy-noisy-real-world

• 5 Docker Containers for Language Model Development

  This article walks through five container setups that consistently help developers move from idea to experiment to deployment without fighting their own toolchains.

  https://www.kdnuggets.com/5-docker-containers-for-language-model-development

• The 5 FREE Must-Read Books for Every Data Scientist

  Want to level up your data skills? Check out these 5 free books that explain data science clearly and practically.

  https://www.kdnuggets.com/the-5-free-must-read-books-for-every-data-scientist

• 5 Fun NLP Projects for Absolute Beginners

  Hands-on projects to help beginners understand how machines read, understand, generate, and translate human language.

  https://www.kdnuggets.com/5-fun-nlp-projects-for-absolute-beginners

• The Benefits of an “Everything” Notebook in NotebookLM

  The goal of an "everything" notebook in NotebookLM is having your entire professional memory instantly accessible and understandable.

  https://www.kdnuggets.com/the-benefits-of-an-everything-notebook-in-notebooklm

• The 5 FREE Must-Read Books for Every AI Engineer

  A handpicked list of free reads that teach you the science, logic, and real-world side of artificial intelligence.

  https://www.kdnuggets.com/the-5-free-must-read-books-for-every-ai-engineer

• What Does the End of GIL Mean for Python?

  The GIL is finally being dismantled. The implications aren't just technical; they're cultural. This shift could redefine how we write, scale, and even think about Python in the modern era.

  https://www.kdnuggets.com/what-does-the-end-of-gil-mean-for-python

• What Is Big Tech’s Influence on AI Development?

  So, what is big tech's influence on ai development? It is both transformative and concerning.

  https://www.kdnuggets.com/2025/09/what-is-big-techs-influence-on-ai-development