Deep Learning (1051)

• Learn Deep Learning by Building 15 Neural Network Projects in 2022 - Jan 4, 2022.
  
  Here are 15 neural network projects you can take on in 2022 to build your skills, your know-how, and your portfolio.

  Deep Learning

• AI and climate change have a complicated relationship - Dec 23, 2021.
  
  Learn about the importance of environmental AI and its carbon impact in this comprehensive review.

  AI, Climate Change, Deep Learning, Environment, Machine Learning

• 10 Key AI & Data Analytics Trends for 2022 and Beyond - Dec 17, 2021.
  
  What AI and data analytics trends are taking the industry by storm this year? This comprehensive review highlights upcoming directions in AI to carefully watch and consider implementing in your personal work or organization.

  2022 Predictions, AI, Data, Data Analysis, Deep Learning, Environment, Low-Code, No-Code, Python, Trends

• Data Science & Analytics Industry Main Developments in 2021 and Key Trends for 2022 - Dec 14, 2021.
  
  We have solicited insights from experts at industry-leading companies, asking: "What were the main AI, Data Science, Machine Learning Developments in 2021 and what key trends do you expect in 2022?" Read their opinions here.

  2022 Predictions, AI, Analytics, Cloud, Data Lake, Data Science, Data Warehouse, Deep Learning, Machine Learning, Synthetic Data

• Inside DeepMind’s New Efforts to Use Deep Learning to Advance Mathematics - Dec 10, 2021.
  
  Using deep learning techniques can help mathematicians develop intuitions about the toughest problems in the field.

  Deep Learning, DeepMind, Mathematics

• Deep Neural Networks Don’t Lead Us Towards AGI - Dec 9, 2021.
  
  Machine learning techniques continue to evolve with increased efficiency for recognition problems. But, they still lack the critical element of intelligence, so we remain a long way from attaining AGI.

  AGI, Deep Learning, Google, Machine Learning

• AI, Analytics, Machine Learning, Data Science, Deep Learning Research Main Developments in 2021 and Key Trends for 2022 - Dec 8, 2021.
  
  2021 has almost come and gone. We saw some standout advancements in AI, Analytics, Machine Learning, Data Science, Deep Learning Research this past year, and the future, starting with 2022, looks bright. As per KDnuggets tradition, our collection of experts have contributed their insights on the matter. Read on to find out more.

  2022 Predictions, AI, Analytics, Data Science, Deep Learning, Machine Learning

• Using Datawig, an AWS Deep Learning Library for Missing Value Imputation - Dec 7, 2021.
  
  A lot of missing values in the dataset can affect the quality of prediction in the long run. Several methods can be used to fill the missing values and Datawig is one of the most efficient ones.

  AWS, Data Preparation, Data Preprocessing, Deep Learning, Missing Values

• 10 Simple Things to Try Before Neural Networks - Dec 6, 2021.
  
  Below are 10 simple things you should remember to try first before throwing in the towel and jumping straight to neural networks.

  Deep Learning, Machine Learning Engineer, Tips

• On-Device Deep Learning: PyTorch Mobile and TensorFlow Lite - Nov 22, 2021.
  
  PyTorch and TensorFlow are the two leading AI/ML Frameworks. In this article, we take a look at their on-device counterparts PyTorch Mobile and TensorFlow Lite and examine them more deeply from the perspective of someone who wishes to develop and deploy models for use on mobile platforms.

  Deep Learning, Mobile, PyTorch, TensorFlow

• What Are NVIDIA NGC Containers & How to Get Started Using Them - Nov 15, 2021.
  
  NVIDIA, the pioneer in the GPU technologies and deep learning revolution, has come up with an excellent catalog of specialized containers that they call NGC Collections. In this article, we explore their basic usage and some variations.

  Containers, Data Engineering, Deep Learning, NVIDIA

• Deep Learning on your phone: PyTorch C++ API for use on Mobile Platforms - Nov 12, 2021.
  
  The PyTorch Deep Learning framework has a C++ API for use on mobile platforms. This article shows an end-to-end demo of how to write a simple C++ application with Deep Learning capabilities using the PyTorch C++ API such that the same code can be built for use on mobile platforms (both Android and iOS).

  C++, Deep Learning, Mobile, Python, PyTorch

• Dream Come True: Building websites by thinking about them - Nov 11, 2021.
  
  From the mind to the computer, make websites using your imagination!

  Brain, Deep Learning, Hackathon, Machine Learning, NLP

• The Common Misconceptions About Machine Learning - Nov 9, 2021.
  
  Beginners in the field can often have many misconceptions about machine learning that sometimes can be a make-it-or-break-it moment for the individual switching careers or starting fresh. This article clearly describes the ground truth realities about learning new ML skills and eventually working professionally as a machine learning engineer.

  Beginners, Career Advice, Deep Learning, Machine Learning, Machine Learning Engineer

• What Comes After HDF5? Seeking a Data Storage Format for Deep Learning - Nov 9, 2021.
  
  In this article we are discussing that HDF5 is one of the most popular and reliable formats for non-tabular, numerical data. But this format is not optimized for deep learning work. This article suggests what kind of ML native data format should be to truly serve the needs of modern data scientists.

  Data Management, Deep Learning, Python

• Getting Started with PyTorch Lightning - Oct 26, 2021.
  
  As a library designed for production research, PyTorch Lightning streamlines hardware support and distributed training as well, and we’ll show how easy it is to move training to a GPU toward the end.

  Deep Learning, Machine Learning, Python, PyTorch, PyTorch Lightning

• Learn To Reproduce Papers: Beginner’s Guide - Oct 25, 2021.
  
  Step-by-step instructions on how to understand Deep Learning papers and implement the described approaches.

  Beginners, Deep Learning, Papers with code, Research

• Introduction to AutoEncoder and Variational AutoEncoder (VAE) - Oct 22, 2021.
  
  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.

  Autoencoder, Deep Learning, Machine Learning, Python

• Introduction to PyTorch Lightning - Oct 4, 2021.
  
  PyTorch Lightning is a high-level programming layer built on top of PyTorch. It makes building and training models faster, easier, and more reliable.

  Deep Learning, Neural Networks, PyTorch, PyTorch Lightning

• Surpassing Trillion Parameters and GPT-3 with Switch Transformers – a path to AGI? - Oct 1, 2021.
  
  Ever larger models churning on increasingly faster machines suggest a potential path toward smarter AI, such as with the massive GPT-3 language model. However, new, more lean, approaches are being conceived and explored that may rival these super-models, which could lead to a future with more efficient implementations of advanced AI-driven systems.

  AGI, Deep Learning, GPT-3, Transformer

• KDnuggets™ News 21:n37, Sep 29: Nine Tools I Wish I Mastered Before My PhD in Machine Learning; Path to Full Stack Data Science - Sep 29, 2021.
  
  Whether you have a PhD or not, learn these very useful 9 tools to increase your mastery of Machine Learning; Check this detailed path to becoming a full stack Data Scientist; Then do one of these 20 Machine Learning Projects that will help you get a job; See a Breakdown of Deep Learning Frameworks; and more.

  Data Science Tools, Deep Learning, Machine Learning, Project

• A Breakdown of Deep Learning Frameworks - Sep 23, 2021.
  
  Deep Learning continues to evolve as one of the most powerful techniques in the AI toolbox. Many software packages exist today to support the development of models, and we highlight important options available with key qualities and differentiators to help you select the most appropriate for your needs.

  Deep Learning, Keras, MATLAB, MXNet, PyTorch, TensorFlow

• The Machine & Deep Learning Compendium Open Book - Sep 16, 2021.
  
  After years in the making, this extensive and comprehensive ebook resource is now available and open for data scientists and ML engineers. Learn from and contribute to this tome of valuable information to support all your work in data science from engineering to strategy to management.

  Deep Learning, ebook, GitHub, Machine Learning, Open Source

• KDnuggets™ News 21:n35, Sep 15: A Data Science Portfolio That Will Land You The Job; Top 18 Low-Code and No-Code Machine Learning Platforms - Sep 15, 2021.
  
  Here is a Data Science Portfolio that will land you the job; Review the top 18 Low-Code and No-Code Machine Learning platforms; Try these 8 Deep Learning Project Ideas for Beginners; Very useful - working with Python APIs for data science project.

  API, Deep Learning, Low-Code, No-Code, Portfolio, Project, Python

• Text Preprocessing Methods for Deep Learning - Sep 10, 2021.
  
  While the preprocessing pipeline we are focusing on in this post is mainly centered around Deep Learning, most of it will also be applicable to conventional machine learning models too.

  Data Preprocessing, Data Processing, Deep Learning, NLP, Text Analytics

• 8 Deep Learning Project Ideas for Beginners - Sep 9, 2021.
  
  Have you studied Deep Learning techniques, but never worked on a useful project? Here, we highlight eight deep learning project ideas for beginners that will help you sharpen your skills and boost your resume.

  Beginners, Deep Learning, Project

• Machine Learning Skills – Update Yours This Summer - Jul 27, 2021.
  
  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.

  Computer Vision, Data Science Skills, Deep Learning, Machine Learning, Skills

• Not Only for Deep Learning: How GPUs Accelerate Data Science & Data Analytics - Jul 26, 2021.
  
  Modern AI/ML systems’ success has been critically dependent on their ability to process massive amounts of raw data in a parallel fashion using task-optimized hardware. Can we leverage the power of GPU and distributed computing for regular data processing jobs too?

  Data Analytics, Data Science, Deep Learning, GPU

• KDnuggets™ News 21:n27, Jul 21: Top 6 Data Science Online Courses in 2021; Geometric Foundations of Deep Learning - Jul 21, 2021.
  
  Top 6 Data Science Online Courses in 2021; Geometric foundations of Deep Learning; Google’s Director of Research Advice for Learning Data Science; SQL, Syllogisms, and Explanations; How to Create Unbiased Machine Learning Models

  Bias, Courses, Data Science, Deep Learning, Machine Learning, Modeling, SQL

• High-Performance Deep Learning: How to train smaller, faster, and better models – Part 5 - Jul 16, 2021.
  
  Training efficient deep learning models with any software tool is nothing without an infrastructure of robust and performant compute power. Here, current software and hardware ecosystems are reviewed that you might consider in your development when the highest performance possible is needed.

  Deep Learning, Efficiency, Google, Hardware, Machine Learning, NVIDIA, PyTorch, Scalability, TensorFlow

• 7 Open Source Libraries for Deep Learning Graphs - Jul 15, 2021.
  
  In this article we’ll go through 7 up-and-coming open source libraries for graph deep learning, ranked in order of increasing popularity.

  Deep Learning, Graphs, Open Source

• Geometric foundations of Deep Learning - Jul 14, 2021.
  
  Geometric Deep Learning is an attempt for geometric unification of a broad class of machine learning problems from the perspectives of symmetry and invariance. These principles not only underlie the breakthrough performance of convolutional neural networks and the recent success of graph neural networks but also provide a principled way to construct new types of problem-specific inductive biases.

  Deep Learning, Geometry, Research

• High-Performance Deep Learning: How to train smaller, faster, and better models – Part 4 - Jul 9, 2021.
  
  With the right software, hardware, and techniques at your fingertips, your capability to effectively develop high-performing models now hinges on leveraging automation to expedite the experimental process and building with the most efficient model architectures for your data.

  Attention, Convolution, Deep Learning, Efficiency, Hyperparameter, Machine Learning, Scalability

• How to Build An Image Classifier in Few Lines of Code with Flash - Jul 7, 2021.
  
  Introducing Flash: The high-level deep learning framework for beginners.

  Deep Learning, Image Classification, Image Recognition, Neural Networks, Python

• High-Performance Deep Learning: How to train smaller, faster, and better models – Part 3 - Jul 2, 2021.
  
  Now that you are ready to efficiently build advanced deep learning models with the right software and hardware tools, the techniques involved in implementing such efforts must be explored to improve model quality and obtain the performance that your organization desires.

  Compression, Deep Learning, Efficiency, Machine Learning, Scalability

• Computational Complexity of Deep Learning: Solution Approaches - Jun 29, 2021.
  
  Why has deep learning been so successful? What is the fundamental reason that deep learning can learn from big data? Why cannot traditional ML learn from the large data sets that are now available for different tasks as efficiently as deep learning can?

  Complexity, Deep Learning, Neural Networks

• High-Performance Deep Learning: How to train smaller, faster, and better models – Part 2 - Jun 25, 2021.
  
  As your organization begins to consider building advanced deep learning models with efficiency in mind to improve the power delivered through your solutions, the software and hardware tools required for these implementations are foundational to achieving high-performance.

  Deep Learning, Efficiency, Machine Learning, Scalability

• High Performance Deep Learning, Part 1 - Jun 18, 2021.
  
  Advancing deep learning techniques continue to demonstrate incredible potential to deliver exciting new AI-enhanced software and systems. But, training the most powerful models is expensive--financially, computationally, and environmentally. Increasing the efficiency of such models will have profound impacts in many ways, so developing future models with this intension in mind will only help to further expand the reach, applicability, and value of what deep learning has to offer.

  Deep Learning, Efficiency, History, Machine Learning

• An introduction to Explainable AI (XAI) and Explainable Boosting Machines (EBM) - Jun 16, 2021.
  
  Understanding why your AI-based models make the decisions they do is crucial for deploying practical solutions in the real-world. Here, we review some techniques in the field of Explainable AI (XAI), why explainability is important, example models of explainable AI using LIME and SHAP, and demonstrate how Explainable Boosting Machines (EBMs) can make explainability even easier.

  AI, Deep Learning, Explainability, Gradient Boosting, Interpretability, LIME, Machine Learning, SHAP

• Beginners Guide to Debugging TensorFlow Models - Jun 15, 2021.
  
  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.

  Beginners, Deep Learning, TensorFlow

• The Essential Guide to Transformers, the Key to Modern SOTA AI - Jun 10, 2021.
  
  You likely know Transformers from their recent spate of success stories in natural language processing, computer vision, and other areas of artificial intelligence, but are familiar with all of the X-formers? More importantly, do you know the differences, and why you might use one over another?

  AI, Computer Vision, Deep Learning, NLP, Transformer

• How to speed up a Deep Learning Language model by almost 50X at half the cost - Jun 9, 2021.
  
  In this blog post, we show how to accelerate fine-tuning the ALBERT language model while also reducing costs by using Determined’s built-in support for distributed training with AWS spot instances.

  AWS, Deep Learning, Distributed Computing, Hugging Face, NLP

• A checklist to track your Data Science progress - May 19, 2021.
  
  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.

  Advice, Beginners, Data Preparation, Data Science, Deep Learning

• Best Python Books for Beginners and Advanced Programmers - May 14, 2021.
  
  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.

  Analytics, Books, Data Science, Deep Learning, Machine Learning, Python

• The Machine Learning Research Championed by the Biggest AI Labs in the World - May 5, 2021.
  
  How Google, Microsoft, Facebook, DeepMind, OpenAI, Amazon and IBM think about the future of AI.

  AI, Deep Learning, Machine Learning, Research

• Disentangling AI, Machine Learning, and Deep Learning - May 4, 2021.
  
  The field of Artificial Intelligence is extremely broad and captures a winding history through the evolution of various sub-fields that experienced many ups and downs over the years. Appreciating AI within its historical contexts will enhance your communication with the public, colleagues, and potential hiring managers, as well as guide your thinking as you progress in the application and study of state-of-the-art techniques.

  AI, Deep Learning, History, Machine Learning

• Data Science Books You Should Start Reading in 2021 - Apr 23, 2021.
  
  Check out this curated list of the best data science books for any level.

  Books, Data Science, Data Scientist, Deep Learning, Machine Learning

• Deep Learning Recommendation Models (DLRM): A Deep Dive - Apr 9, 2021.
  
  The currency in the 21st century is no longer just data. It's the attention of people. This deep dive article presents the architecture and deployment issues experienced with the deep learning recommendation model, DLRM, which was open-sourced by Facebook in March 2019.

  Deep Learning, Recommendations, Recommender Systems

• How to deploy Machine Learning/Deep Learning models to the web - Apr 5, 2021.
  
  The full value of your deep learning models comes from enabling others to use them. Learn how to deploy your model to the web and access it as a REST API, and begin to share the power of your machine learning development with the world.

  Deep Learning, Deployment, Machine Learning, RESTful API

• Deep Learning Is Becoming Overused - Mar 29, 2021.
  
  Understanding the data is the first port of call.

  Classification, Deep Learning, Neural Networks, Regression, Time Series

• 10 Amazing Machine Learning Projects of 2020 - Mar 15, 2021.
  
  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.

  Chatbot, Deep Learning, Image Processing, Machine Learning, Project, Trends

• Reducing the High Cost of Training NLP Models With SRU++ - Mar 4, 2021.
  
  The increasing computation time and costs of training natural language models (NLP) highlight the importance of inventing computationally efficient models that retain top modeling power with reduced or accelerated computation. A single experiment training a top-performing language model on the 'Billion Word' benchmark would take 384 GPU days and as much as $36,000 using AWS on-demand instances.

  Deep Learning, Machine Learning, Neural Networks, NLP

• KDnuggets™ News 21:n08, Feb 24: Powerful Exploratory Data Analysis in just two lines of code; Cartoon: Data Scientist vs Data Engineer - Feb 24, 2021.
  
  Powerful Exploratory Data Analysis in just two lines of code; Cartoon: Data Scientist vs Data Engineer; Evaluating Deep Learning Models: The Confusion Matrix, Accuracy, Precision, and Recall; Feature Store as a Foundation for Machine Learning; Approaching (Almost) Any Machine Learning Problem

  Cartoon, Data Analysis, Data Engineering, Data Science, Deep Learning, Machine Learning, Metrics, Python

• Evaluating Deep Learning Models: The Confusion Matrix, Accuracy, Precision, and Recall - Feb 19, 2021.
  
  This tutorial discusses the confusion matrix, and how the precision, recall and accuracy are calculated, and how they relate to evaluating deep learning models.

  Accuracy, Confusion Matrix, Deep Learning, Metrics, Precision, Recall

• Approaching (Almost) Any Machine Learning Problem - Feb 18, 2021.
  
  This freely-available book is a fantastic walkthrough of practical approaches to machine learning problems.

  Deep Learning, Free ebook, Machine Learning, Python

• Deep Learning-based Real-time Video Processing - Feb 17, 2021.
  
  In this article, we explore how to build a pipeline and process real-time video with Deep Learning to apply this approach to business use cases overviewed in our research.

  Computer Vision, Deep Learning, Neural Networks, Video recognition

• Hugging Face Transformers Package – What Is It and How To Use It - Feb 16, 2021.
  
  The rapid development of Transformers have brought a new wave of powerful tools to natural language processing. These models are large and very expensive to train, so pre-trained versions are shared and leveraged by researchers and practitioners. Hugging Face offers a wide variety of pre-trained transformers as open-source libraries, and you can incorporate these with only one line of code.

  Deep Learning, Hugging Face, Natural Language Generation, NLP, PyTorch, TensorFlow, Transformer, Zero-shot Learning

• KDnuggets™ News 21:n06, Feb 10: The Best Data Science Project to Have in Your Portfolio; Deep learning doesn’t need to be a black box - Feb 10, 2021.
  
  The Best Data Science Project to Have in Your Portfolio; Deep learning doesn’t need to be a black box; Build Your First Data Science Application; How to create stunning visualizations using python from scratch; How to Get Your First Job in Data Science without Any Work Experience

  Career Advice, Data Science, Data Visualization, Deep Learning, Explainability, Portfolio, Python

• Deep learning doesn’t need to be a black box - Feb 5, 2021.
  
  The cultural perception of AI is often suspect because of the described challenges in knowing why a deep neural network makes its predictions. So, researchers try to crack open this "black box" after a network is trained to correlate results with inputs. But, what if the goal of explainability could be designed into the network's architecture -- before the model is trained and without reducing its predictive power? Maybe the box could stay open from the beginning.

  Convolutional Neural Networks, Deep Learning, Explainability, Explainable AI, Image Recognition

• Saving and loading models in TensorFlow — why it is important and how to do it - Feb 3, 2021.
  
  So much time and effort can go into training your machine learning models. But, shut down the notebook or system, and all those trained weights and more vanish with the memory flush. Saving your models to maximize reusability is key for efficient productivity.

  Deep Learning, Machine Learning, TensorFlow

• KDnuggets™ News 21:n04, Jan 27: The Ultimate Scikit-Learn Machine Learning Cheatsheet; Building a Deep Learning Based Reverse Image Search - Jan 27, 2021.
  
  The Ultimate Scikit-Learn Machine Learning Cheatsheet; Building a Deep Learning Based Reverse Image Search; Data Engineering — the Cousin of Data Science, is Troublesome; Going Beyond the Repo: GitHub for Career Growth in AI & Machine Learning; Popular Machine Learning Interview Questions

  Cheat Sheet, Data Engineering, Data Science, Deep Learning, GitHub, Image Recognition, Machine Learning, scikit-learn, Search

• Deep Learning Pioneer Geoff Hinton on his Latest Research and the Future of AI - Jan 26, 2021.
  
  Geoff Hinton has lived at the outer reaches of machine learning research since an aborted attempt at a carpentry career a half century ago. He spoke to Craig Smith about his work In 2020 and what he sees on the horizon for AI.

  AI, Capsule Networks, Deep Learning, Geoff Hinton, Neural Networks, Research

• Building a Deep Learning Based Reverse Image Search - Jan 22, 2021.
  
  Following the journey from unstructured data to content based image retrieval.

  Deep Learning, Image Recognition, Search

• Attention mechanism in Deep Learning, Explained - Jan 11, 2021.
  
  Attention is a powerful mechanism developed to enhance the performance of the Encoder-Decoder architecture on neural network-based machine translation tasks. Learn more about how this process works and how to implement the approach into your work.

  Attention, Deep Learning, Explained, LSTM, Machine Translation

• DeepMind’s MuZero is One of the Most Important Deep Learning Systems Ever Created - Jan 4, 2021.
  
  MuZero takes a unique approach to solve the problem of planning in deep learning models.

  AlphaZero, Deep Learning, DeepMind, MuZero, Reinforcement Learning

• 15 Free Data Science, Machine Learning & Statistics eBooks for 2021 - Dec 31, 2020.
  
  We present a curated list of 15 free eBooks compiled in a single location to close out the year.

  Automated Machine Learning, Data Science, Deep Learning, Free ebook, Machine Learning, NLP, Python, R, Statistics

• Covid or just a Cough? AI for detecting COVID-19 from Cough Sounds - Dec 15, 2020.
  
  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.

  AI, Audio, COVID-19, Deep Learning, Healthcare

• How to Create Custom Real-time Plots in Deep Learning - Dec 14, 2020.
  
  How to generate real-time visualizations of custom metrics while training a deep learning model using Keras callbacks.

  Data Visualization, Deep Learning, Keras, Metrics, Neural Networks, Python

• Deep Learning Design Patterns! - Dec 9, 2020.
  
  New book, "Deep Learning Design Patterns" presents deep learning models in a unique-but-familiar new way: as extendable design patterns you can easily plug-and-play into your software projects. Use code kdmath50 to save 50% off.

  Book, Deep Learning, Manning

• AI, Analytics, Machine Learning, Data Science, Deep Learning Research Main Developments in 2020 and Key Trends for 2021 - Dec 3, 2020.
  
  2020 is finally coming to a close. While likely not to register as anyone's favorite year, 2020 did have some noteworthy advancements in our field, and 2021 promises some important key trends to look forward to. As has become a year-end tradition, our collection of experts have once again contributed their thoughts. Read on to find out more.

  2021 Predictions, AI, Ajit Jaokar, Analytics, Brandon Rohrer, Daniel Tunkelang, Data Science, Deep Learning, Machine Learning, Pedro Domingos, Predictions, Research, Rosaria Silipo

• KDnuggets™ News 20:n45, Dec 2: TabPy: Combining Python and Tableau; Learn Deep Learning with this Free Course from Yann LeCun - Dec 2, 2020.
  
  Combine Python and Tableau with TabPy; Learn Deep Learning with this Free Course from Yann LeCun; Find 15 Exciting AI Project Ideas for Beginners; Read about the Rise of the Machine Learning Engineer; See How to Incorporate Tabular Data with HuggingFace Transformers

  AI, Courses, Deep Learning, Hugging Face, Machine Learning Engineer, Python, Tableau, Transformer, Yann LeCun

• Learn Deep Learning with this Free Course from Yann LeCun - Nov 27, 2020.
  
  Here is a freely-available NYU course on deep learning to check out from Yann LeCun and Alfredo Canziani, including videos, slides, and other helpful resources.

  Courses, Deep Learning, NYU, Yann LeCun

• How to Incorporate Tabular Data with HuggingFace Transformers - Nov 25, 2020.
  
  In real-world scenarios, we often encounter data that includes text and tabular features. Leveraging the latest advances for transformers, effectively handling situations with both data structures can increase performance in your models.

  Data Preparation, Deep Learning, Machine Learning, NLP, Python, Transformer

• Adversarial Examples in Deep Learning – A Primer - Nov 20, 2020.
  
  Bigger compute has led to increasingly impressive deep learning computer vision model SOTA results. However most of these SOTA deep learning models are brought down to their knees when making predictions on adversarial images. Read on to find out more.

  Adversarial, Computer Vision, Deep Learning

• Primer on TensorFlow and how PerceptiLabs Makes it Easier - Nov 18, 2020.
  
  With PerceptiLabs, beginners can get started building a model more quickly, and those with more experience can still dive into the code. Given that PerceptiLabs runs TensorFlow behind the scenes, we thought we'd walk through the framework so you can understand its basics, and how it is utilized by PerceptiLabs.

  AutoML, Deep Learning, Machine Learning, MLOps, PerceptiLabs, TensorFlow

• Facebook Open Sourced New Frameworks to Advance Deep Learning Research - Nov 17, 2020.
  
  Polygames, PyTorch3D and HiPlot are the new additions to Facebook’s open source deep learning stack.

  Deep Learning, Facebook, Open Source, PyTorch, Research

• Top Python Libraries for Deep Learning, Natural Language Processing & Computer Vision - Nov 16, 2020.
  
  This article compiles the 30 top Python libraries for deep learning, natural language processing & computer vision, as best determined by KDnuggets staff.

  Computer Vision, Data Science, Deep Learning, Machine Learning, Neural Networks, NLP, Python

• How to Acquire the Most Wanted Data Science Skills - Nov 13, 2020.
  
  We recently surveyed KDnuggets readers to determine the "most wanted" data science skills. Since they seem to be those most in demand from practitioners, here is a collection of resources for getting started with this learning.

  Algorithms, Amazon, Apache Spark, AWS, Computer Vision, Data Science, Data Science Skills, Deep Learning, Docker, NLP, NoSQL, PyTorch, Reinforcement Learning, TensorFlow

• Deep Learning Design Patterns. - Nov 11, 2020.
  
  New book, "Deep Learning Design Patterns" presents deep learning models in a unique-but-familiar new way: as extendable design patterns you can easily plug-and-play into your software projects. Use code kdmath50 to save 50% off.

  Book, Deep Learning, Manning

• Mastering TensorFlow Tensors in 5 Easy Steps - Nov 11, 2020.
  
  Discover how the building blocks of TensorFlow works at the lower level and learn how to make the most of Tensor objects.

  Deep Learning, Python, Tensor, TensorFlow

• Top 5 Free Machine Learning and Deep Learning eBooks Everyone should read - Nov 5, 2020.
  
  There is always so much new to learn in machine learning, and keeping well grounded in the fundamentals will help you stay up-to-date with the latest advancements while acing your career in Data Science.

  Deep Learning, Free ebook, Machine Learning

• Building Deep Learning Projects with fastai — From Model Training to Deployment - Nov 4, 2020.
  
  A getting started guide to develop computer vision application with fastai.

  Deep Learning, Deployment, fast.ai, Modeling, Python, Training

• Microsoft and Google Open Sourced These Frameworks Based on Their Work Scaling Deep Learning Training - Nov 2, 2020.
  
  Google and Microsoft have recently released new frameworks for distributed deep learning training.

  Deep Learning, Google, Microsoft, Open Source, Scalability, Training

• How to Make Sense of the Reinforcement Learning Agents? - Oct 30, 2020.
  
  In this blog post, you’ll learn what to keep track of to inspect/debug your agent learning trajectory. I’ll assume you are already familiar with the Reinforcement Learning (RL) agent-environment setting and you’ve heard about at least some of the most common RL algorithms and environments.

  Agents, Deep Learning, Reinforcement Learning

• Building Neural Networks with PyTorch in Google Colab - Oct 30, 2020.
  
  Combining PyTorch and Google's cloud-based Colab notebook environment can be a good solution for building neural networks with free access to GPUs. This article demonstrates how to do just that.

  Deep Learning, Google Colab, Neural Networks, Python, PyTorch

• An Introduction to AI, updated - Oct 28, 2020.
  
  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.

  AGI, AI, Beginners, Deep Learning, Machine Learning

• PerceptiLabs – A GUI and Visual API for TensorFlow - Oct 27, 2020.
  
  Recently released PerceptiLabs 0.11, is quickly becoming the GUI and visual API for TensorFlow. PerceptiLabs is built around a sophisticated visual ML modeling editor in which you drag and drop components and connect them together to form your model, automatically creating the underlying TensorFlow code. Try it now.

  API, Deep Learning, PerceptiLabs, TensorFlow

• Deep Learning for Virtual Try On Clothes – Challenges and Opportunities - Oct 16, 2020.
  
  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.

  Computer Vision, Deep Learning, Fashion, Generative Adversarial Network, Generative Models, Humans, Image Generation

• Deep Learning Design Patterns - Oct 14, 2020.
  
  New book, "Deep Learning Design Patterns" presents deep learning models in a unique-but-familiar new way: as extendable design patterns you can easily plug-and-play into your software projects. Use code kdmath50 to save 50% off.

  Book, Deep Learning, Manning

• 10 Best Machine Learning Courses in 2020 - Oct 6, 2020.
  
  If you are ready to take your career in machine learning to the next level, then these top 10 Machine Learning Courses covering both practical and theoretical work will help you excel.

  Courses, DataCamp, Deep Learning, fast.ai, Machine Learning, Online Education, Python, Stanford

• 10 Days With “Deep Learning for Coders” - Oct 1, 2020.
  
  Read about the author's experience with the course and the book from fast.ai.

  Courses, Deep Learning, fast.ai, MOOC

• How AI is Driving Innovation in Astronomy - Sep 29, 2020.
  
  In this blog, we look at a disruptive AI program - Morpheus - developed by Researchers at UC Santa Cruz that can analyze astronomical image data and classify galaxies and stars with surgical precision. If you're reading this with "starry" eyes, we bet we've got you hooked.

  AI, Deep Learning, Innovation, Science

• 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 - Sep 23, 2020.
  
  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?

  Automation, Certificate, Courses, Data Science, Deep Learning, DeepMind, Machine Learning, Neural Networks, Python

• MathWorks Deep learning workflow: tips, tricks, and often forgotten steps - Sep 22, 2020.
  
  Getting started in deep learning – and adopting an organized, sustainable, and reproducible workflow – can be challenging. This blog post will share some tips and tricks to help you develop a systematic, effective, attainable, and scalable deep learning workflow as you experiment with different deep learning models, datasets, and applications.

  Deep Learning, MathWorks, MATLAB, Tips

• The Insiders’ Guide to Generative and Discriminative Machine Learning Models - Sep 18, 2020.
  
  In this article, we will look at the difference between generative and discriminative models, how they contrast, and one another.

  Deep Learning, GANs, Generative Adversarial Network, Modeling

• Implementing a Deep Learning Library from Scratch in Python - Sep 17, 2020.
  
  A beginner’s guide to understanding the fundamental building blocks of deep learning platforms.

  Deep Learning, Neural Networks, Python

• Autograd: The Best Machine Learning Library You’re Not Using? - Sep 16, 2020.
  
  If there is a Python library that is emblematic of the simplicity, flexibility, and utility of differentiable programming it has to be Autograd.

  Deep Learning, Neural Networks, Python, PyTorch

• KDnuggets™ News 20:n35, Sep 16: Data Science Skills: Core, Emerging, and Most Wanted; Free From MIT: Intro to CS, Programming in Python - Sep 16, 2020.
  
  Check the analysis of latest KDnuggets Poll: which data science skills are core, which are emerging, and what is the most wanted skill readers want to learn; Free From MIT: Intro to CS and Programming in Python; 8 AI/Machine Learning Projects To Make Your Portfolio Stand Out; Statistics with Julia: The Free eBook; and more.

  Deep Learning, Julia, Kaggle, MIT, Python

• Deep Learning’s Most Important Ideas - Sep 14, 2020.
  
  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.

  Attention, Deep Learning, GANs, History, ImageNet, Reinforcement Learning, Transformer

• A Deep Learning Dream: Accuracy and Interpretability in a Single Model - Sep 7, 2020.
  
  IBM Research believes that you can improve the accuracy of interpretable models with knowledge learned in pre-trained models.

  Accuracy, Deep Learning, Interpretability

• KDnuggets™ News 20:n33, Aug 26: If I had to start learning Data Science again, how would I do it? Must-read NLP and Deep Learning articles for Data Scientists - Aug 26, 2020.
  
  If I had to start learning Data Science again, how would I do it? Must-read NLP and Deep Learning articles for Data Scientists; These Data Science Skills will be your Superpower; Accelerated Natural Language Processing: A Free Amazon Machine Learning University Course.

  Career, Career Advice, Data Science, Deep Learning, NLP, Research

• A Deep Dive Into the Transformer Architecture – The Development of Transformer Models - Aug 24, 2020.
  
  Even though transformers for NLP were introduced only a few years ago, they have delivered major impacts to a variety of fields from reinforcement learning to chemistry. Now is the time to better understand the inner workings of transformer architectures to give you the intuition you need to effectively work with these powerful tools.

  Attention, Deep Learning, Hugging Face, NLP, Transformer

• Must-read NLP and Deep Learning articles for Data Scientists - Aug 21, 2020.
  
  NLP and deep learning continue to advance, nearly on a daily basis. Check out these recent must-read guides, feature articles, and other resources to keep you on top of the latest advancements and ahead of the curve.

  Deep Learning, Google, GPT-3, NLP, OpenAI, Privacy, Research, Self-Driving, TensorFlow, Trends

• KDD-2020 (virtual), the leading conference on Data Science and Knowledge Discovery, Aug 23-27 – register now - Aug 18, 2020.
  
  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.

  ACM SIGKDD, COVID-19, Data Science, Deep Learning, KDD, KDD-2020, Machine Learning, Meetings, Research

• Top KDnuggets tweets, Aug 5-11: Unselfie: Translating Selfies to Neutral-pose Portraits in the Wild - Aug 12, 2020.
  
  Unselfie: Translating Selfies to Neutral-pose Portraits in the Wild; How to Evaluate the Performance of Your Machine Learning Model; Deep Learning Most Important Ideas - an excellent review

  Deep Learning, History, Model Performance, Photography, Top tweets

• Batch Normalization in Deep Neural Networks - Aug 7, 2020.
  
  Batch normalization is a technique for training very deep neural networks that normalizes the contributions to a layer for every mini batch.

  Deep Learning, Neural Networks, Normalization, Regularization

• Metrics to Use to Evaluate Deep Learning Object Detectors - Aug 6, 2020.
  
  It's important to understand which metric should be used to evaluate trained object detectors and which one is more important. Is mAP alone enough to evaluate the objector models? Can the same metric be used to evaluate object detectors on validation set and test set?

  Computer Vision, Deep Learning, Metrics, Object Detection

• I have a joke about … - Aug 1, 2020.
  
  I have a machine learning joke, but it is not performing as well on a new audience. We bring you a selection of the nerdy self-referential computer jokes that were popular on the web recently.

  Cartoon, Deep Learning, Humor

• Deep Learning for Signal Processing: What You Need to Know - Jul 27, 2020.
  
  Signal Processing is a branch of electrical engineering that models and analyzes data representations of physical events. It is at the core of the digital world. And now, signal processing is starting to make some waves in deep learning.

  Deep Learning, Neural Networks

• Is depth useful for self-attention? - Jul 27, 2020.
  
  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.

  Attention, BERT, Deep Learning, Research, Scalability, Transformer

• Labelling Data Using Snorkel - Jul 24, 2020.
  
  In this tutorial, we walk through the process of using Snorkel to generate labels for an unlabelled dataset. We will provide you examples of basic Snorkel components by guiding you through a real clinical application of Snorkel.

  Data Labeling, Data Science, Deep Learning, Machine Learning, NLP, Python

• Recurrent Neural Networks (RNN): Deep Learning for Sequential Data - Jul 20, 2020.
  
  Recurrent Neural Networks can be used for a number of ways such as detecting the next word/letter, forecasting financial asset prices in a temporal space, action modeling in sports, music composition, image generation, and more.

  Deep Learning, Python, Recurrent Neural Networks, Sequences, TensorFlow

• Math and Architectures of Deep Learning! - Jul 15, 2020.
  
  This hands-on book bridges the gap between theory and practice, showing you the math of deep learning algorithms side by side with an implementation in PyTorch. Save 50% with code kdarch50.

  Architecture, Deep Learning, Manning, Mathematics, PyTorch

• Auto Rotate Images Using Deep Learning - Jul 14, 2020.
  
  Follow these 5 simple steps to auto rotate images and get the right angle in human photos using computer vision.

  Computer Vision, Deep Learning, Face Detection, Image Processing

• Deep Learning in Finance: Is This The Future of the Financial Industry? - Jul 10, 2020.
  
  Get a handle on how deep learning is affecting the finance industry, and identify resources to further this understanding and increase your knowledge of the various aspects.

  Deep Learning, Finance

• Learn Python, ML, Deep Learning, Data Visualization and more in Italy with BIG DIVE - Jul 9, 2020.
  
  Do you want to learn or upgrade your data data proficiency and push your career forward? This year, under the umbrella of BIG DIVE, TOP-IX presents four full-time 1-week courses from beginner to advanced levels. Read more and register now.

  Courses, Deep Learning, Italy, Python

• KDnuggets™ News 20:n26, Jul 8: Speed up Your Numpy and Pandas; A Layman’s Guide to Data Science; Getting Started with TensorFlow 2 - Jul 8, 2020.
  
  Speed up your Numpy and Pandas with NumExpr Package; A Layman's Guide to Data Science. Part 3: Data Science Workflow; Getting Started with TensorFlow 2; Feature Engineering in SQL and Python: A Hybrid Approach; Deploy Machine Learning Pipeline on AWS Fargate

  AWS, Data Science, Deep Learning, numpy, Pandas, Python, TensorFlow, Workflow

• PyTorch for Deep Learning: The Free eBook - Jul 7, 2020.
  
  For this week's free eBook, check out the newly released Deep Learning with PyTorch from Manning, made freely available via PyTorch's website for a limited time. Grab it now!

  Deep Learning, Free ebook, Neural Networks, PyTorch

• Generating cooking recipes using TensorFlow and LSTM Recurrent Neural Network: A step-by-step guide - Jul 3, 2020.
  
  A character-level LSTM (Long short-term memory) RNN (Recurrent Neural Network) is trained on ~100k recipes dataset using TensorFlow. The model suggested the recipes "Cream Soda with Onions", "Puff Pastry Strawberry Soup", "Zucchini flavor Tea", and "Salmon Mousse of Beef and Stilton Salad with Jalapenos". Yum!? Follow along this detailed guide with code to create your own recipe-generating chef.

  Cooking, Deep Learning, Humor, LSTM, Natural Language Generation, TensorFlow

• Getting Started with TensorFlow 2 - Jul 2, 2020.
  
  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.

  Advice, Beginners, Deep Learning, Python, Regularization, TensorFlow

• The Unreasonable Progress of Deep Neural Networks in Natural Language Processing (NLP) - Jun 29, 2020.
  
  Natural language processing has made incredible advances through advanced techniques in deep learning. Learn about these powerful models, and find how close (or far away) these approaches are to human-level understanding.

  Deep Learning, LSTM, NLP, Transfer Learning, Transformer, Trends

• Learning by Forgetting: Deep Neural Networks and the Jennifer Aniston Neuron - Jun 25, 2020.
  
  DeepMind’s research shows how to understand the role of individual neurons in a neural network.

  Deep Learning, DeepMind, Learning, Neural Networks

• Tools to Spot Deepfakes and AI-Generated Text - Jun 23, 2020.
  
  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.

  AI, Deep Learning, Deepfakes, Natural Language Generation

• The Most Important Fundamentals of PyTorch you Should Know - Jun 18, 2020.
  
  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.

  Deep Learning, Neural Networks, Python, PyTorch, Tensor

• Crop Disease Detection Using Machine Learning and Computer Vision - Jun 17, 2020.
  
  Computer vision has tremendous promise for improving crop monitoring at scale. We present our learnings from building such models for detecting stem and wheat rust in crops.

  Agriculture, Computer Vision, Deep Learning, fast.ai

• A Complete guide to Google Colab for Deep Learning - Jun 16, 2020.
  
  Google Colab is a widely popular cloud service for machine learning that features free access to GPU and TPU computing. Follow this detailed guide to help you get up and running fast to develop your next deep learning algorithms with Colab.

  Deep Learning, GitHub, Google Colab, GPU, Jupyter

• Math and Architectures of Deep Learning - Jun 11, 2020.
  
  This hands-on book bridges the gap between theory and practice, showing you the math of deep learning algorithms side by side with an implementation in PyTorch. Save 40% off Math and Architectures of Deep Learning with code nlkdarch40

  Deep Learning, Manning, Mathematics

• GPT-3, a giant step for Deep Learning and NLP? - Jun 9, 2020.
  
  Recently, OpenAI announced a new successor to their language model, GPT-3, that is now the largest model trained so far with 175 billion parameters. Training a language model this large has its merits and limitations, so this article covers some of its most interesting and important aspects.

  AI, Deep Learning, GPT-2, GPT-3, NLP, OpenAI

• Deep Learning for Detecting Pneumonia from X-ray Images - Jun 5, 2020.
  
  This article covers an end to end pipeline for pneumonia detection from X-ray images.

  Deep Learning, Healthcare, Image Recognition, Python

• Metis Webinar: Deep Learning Approaches to Forecasting - Jun 4, 2020.
  
  Metis Corporate Training is offering Deep Learning Approaches to Forecasting and Planning, a free webinar focusing on the intuition behind various deep learning approaches, and exploring how business leaders, data science managers, and decision makers can tackle highly complex models by asking the right questions, and evaluating the models with familiar tools.

  Deep Learning, Forecasting, Metis, Webinar