Search results for Data Science

Found 4789 documents, 5970 searched:

• Automate Microsoft Excel and Word Using Python

  Integrate Excel with Word to generate automated reports seamlessly.

  https://www.kdnuggets.com/2021/08/automate-microsoft-excel-word-python.html

• Why are More Developers Using Python for Their Machine Learning Projects?

  To support the creation of new and exciting ML and artificial intelligence (AI) applications, developers need a robust programming language. That's where the Python programming language comes in.

  https://www.kdnuggets.com/2022/01/developers-python-machine-learning-projects.html

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

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

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

• Hands-on Reinforcement Learning Course Part 3: SARSA

  This is part 3 of my hands-on course on reinforcement learning, which takes you from zero to HERO . Today we will learn about SARSA, a powerful RL algorithm.

  https://www.kdnuggets.com/2022/01/handson-reinforcement-learning-course-part-3-sarsa.html

• How AI/ML Technology Integration Will Help Business in Achieving Goals in 2022

  AI/ML systems have a wide range of applications in a variety of industries and sectors, and this article highlights the top ways AI/ML will impact your small business in 2022.

  https://www.kdnuggets.com/2021/12/aiml-technology-integration-help-business-achieving-goals-2022.html

• Hands-On Reinforcement Learning Course, Part 2

  Continue your learning journey in Reinforcement Learning with this second of two part tutorial that covers the foundations of the technique with examples and Python code.

  https://www.kdnuggets.com/2021/12/hands-on-reinforcement-learning-part-2.html

• Versioning Machine Learning Experiments vs Tracking Them

  Learn how to improve ML reproducibility by treating experiments as code.

  https://www.kdnuggets.com/2021/12/versioning-machine-learning-experiments-tracking.html

• Why we will always need humans to train AI — sometimes in real-time

  Customizable, real-time data labeling pipelines that can continuously receive and process unlabeled data are necessary to train and perfect the AI that impacts our lives and daily conveniences.

  https://www.kdnuggets.com/2021/12/why-we-need-humans-training-ai.html

• What Is AI Model Governance?

  How exactly does AI model governance help tackle these issues? And how can you ensure you’re using it to best fit your needs? Read on.

  https://www.kdnuggets.com/2021/12/ai-model-governance.html

• Introduction to Clustering in Python with PyCaret

  A step-by-step, beginner-friendly tutorial for unsupervised clustering tasks in Python using PyCaret.

  https://www.kdnuggets.com/2021/12/introduction-clustering-python-pycaret.html

• Inside DeepMind’s New Efforts to Use Deep Learning to Advance Mathematics

  Using deep learning techniques can help mathematicians develop intuitions about the toughest problems in the field.

  https://www.kdnuggets.com/2021/12/inside-deepmind-new-efforts-deep-learning-advance-mathematics.html

• Deep Neural Networks Don’t Lead Us Towards AGI

  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.

  https://www.kdnuggets.com/2021/12/deep-neural-networks-not-toward-agi.html

• Analyzing Scientific Articles with fine-tuned SciBERT NER Model and Neo4j

  In this article, we will be analyzing a dataset of scientific abstracts using the Neo4j Graph database and a fine-tuned SciBERT model.

  https://www.kdnuggets.com/2021/12/analyzing-scientific-articles-finetuned-scibert-ner-model-neo4j.html

• Introduction to Binary Classification with PyCaret

  PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with few lines only. See how to use it for binary classification.

  https://www.kdnuggets.com/2021/12/introduction-binary-classification-pycaret.html

• Using PyCaret’s New Time Series Module

  PyCaret’s new time series module is now available in beta. Staying true to the simplicity of PyCaret, it is consistent with the existing API and comes with a lot of functionalities.

  https://www.kdnuggets.com/2021/12/pycaret-new-time-series-module.html

• How to Use Permutation Tests

  A walkthrough of permutation tests and how they can be applied to time series data.

  https://www.kdnuggets.com/2021/12/use-permutation-tests.html

• A Spreadsheet that Generates Python: The Mito JupyterLab Extension

  You can call Mito into your Jupyter Environment and each edit you make will generate the equivalent Python in the code cell below.

  https://www.kdnuggets.com/2021/11/spreadsheet-generates-python-mito-jupyterlab-extension.html

• 5 Advanced Tips on Python Sequences

  Notes from Fluent Python by Luciano Ramalho.

  https://www.kdnuggets.com/2021/11/5-advanced-tips-python-sequences.html

• On-Device Deep Learning: PyTorch Mobile and TensorFlow Lite

  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.

  https://www.kdnuggets.com/2021/11/on-device-deep-learning-pytorch-mobile-tensorflow-lite.html

• Where NLP is heading">Where NLP is heading

  Natural language processing research and applications are moving forward rapidly. Several trends have emerged on this progress, and point to a future of more exciting possibilities and interesting opportunities in the field.

  https://www.kdnuggets.com/2021/11/where-nlp-is-heading.html

• Inside recommendations: how a recommender system recommends

  We describe types of recommender systems, more specifically, algorithms and methods for content-based systems, collaborative filtering, and hybrid systems.

  https://www.kdnuggets.com/2021/11/recommendations-recommender-system.html

• Deep Learning on your phone: PyTorch C++ API for use on Mobile Platforms

  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).

  https://www.kdnuggets.com/2021/11/deep-learning-mobile-phone-pytorch-c-api.html

• Dream Come True: Building websites by thinking about them

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

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

• Neural Networks from a Bayesian Perspective

  This article looks at neural networks from a Bayesian perspective.

  https://www.kdnuggets.com/2021/11/neural-networks-bayesian-perspective.html

• Design Patterns for Machine Learning Pipelines">Design Patterns for Machine Learning Pipelines

  ML pipeline design has undergone several evolutions in the past decade with advances in memory and processor performance, storage systems, and the increasing scale of data sets. We describe how these design patterns changed, what processes they went through, and their future direction.

  https://www.kdnuggets.com/2021/11/design-patterns-machine-learning-pipelines.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

• Deploying Serverless spaCy Transformer Model with AWS Lambda

  A step-by-step guide on how to deploy NER transformer model serverless.

  https://www.kdnuggets.com/2021/10/deploying-serverless-spacy-transformer-model-aws-lambda.html

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

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

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

• Knowledge Graph Forum: Technology Ecosystem and Business Applications

  Ontotext is thrilled to invite you to the Ontotext & partners virtual Knowledge Graph Forum, Oct 26 & 27, 2021. This event is shaped by Ontotext’s vision that knowledge graphs serve as a hub for data, metadata and content. 35+ speakers from around the globe will share their experiences through real-life cases and platforms demonstrations. Save your spot now.

  https://www.kdnuggets.com/2021/10/ontotext-knowledge-graph-forum.html

• Real Time Image Segmentation Using 5 Lines of Code

  PixelLib Library is a library created to allow easy integration of object segmentation in images and videos using few lines of python code. PixelLib now provides support for PyTorch backend to perform faster, more accurate segmentation and extraction of objects in images and videos using PointRend segmentation architecture.

  https://www.kdnuggets.com/2021/10/real-time-image-segmentation-5-lines-code.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

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

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

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

• GitHub Copilot and the Rise of AI Language Models in Programming Automation

  Read on to learn more about what makes Copilot different from previous autocomplete tools (including TabNine), and why this particular tool has been generating so much controversy.

  https://www.kdnuggets.com/2021/09/github-copilot-rise-ai-language-models-programming-automation.html

• How to Find Weaknesses in your Machine Learning Models">How to Find Weaknesses in your Machine Learning Models

  FreaAI: a new method from researchers at IBM.

  https://www.kdnuggets.com/2021/09/weaknesses-machine-learning-models.html

• How Many AI Neurons Does It Take to Simulate a Brain Neuron?

  A new research shows some shocking answers to that question.

  https://www.kdnuggets.com/2021/09/ai-neurons-simulate-brain-neuron.html

• Text Preprocessing Methods for Deep Learning

  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.

  https://www.kdnuggets.com/2021/09/text-preprocessing-methods-deep-learning.html

• Top 18 Low-Code and No-Code Machine Learning Platforms">Top 18 Low-Code and No-Code Machine Learning Platforms

  Machine learning becomes more accessible to companies and individuals when there is less coding involved. Especially if you are just starting your path in ML, then check out these low-code and no-code platforms to help expedite your capabilities in learning and applying AI.

  https://www.kdnuggets.com/2021/09/top-18-low-code-no-code-machine-learning-platforms.html

• Fast AutoML with FLAML + Ray Tune

  Microsoft Researchers have developed FLAML (Fast Lightweight AutoML) which can now utilize Ray Tune for distributed hyperparameter tuning to scale up FLAML’s resource-efficient & easily parallelizable algorithms across a cluster.

  https://www.kdnuggets.com/2021/09/fast-automl-flaml-ray-tune.html

• How causal inference lifts augmented analytics beyond flatland

  In our quest to better understand and predict business outcomes, traditional predictive modeling tends to fall flat. However, causal inference techniques along with business analytics approaches can unravel what truly changes your KPIs.

  https://www.kdnuggets.com/2021/08/causal-inference-augmented-analytics-beyond-flatland.html

• Coding Ethics for AI & AIOps: Designing Responsible AI Systems

  AI ops has taken Human machine collaboration to the next level where humans and machines are not just coexisting but are collaborating and working together like team members.

  https://www.kdnuggets.com/2021/08/coding-ethics-ai-aiops-designing-responsible-ai-systems.html

• Prefect: How to Write and Schedule Your First ETL Pipeline with Python">Prefect: How to Write and Schedule Your First ETL Pipeline with Python

  Workflow management systems made easy — both locally and in the cloud.

  https://www.kdnuggets.com/2021/08/prefect-write-schedule-etl-pipeline-python.html

• How to Train a BERT Model From Scratch

  Meet BERT’s Italian cousin, FiliBERTo.

  https://www.kdnuggets.com/2021/08/train-bert-model-scratch.html

• Introduction to Statistical Learning Second Edition

  The second edition of the classic "An Introduction to Statistical Learning, with Applications in R" was published very recently, and is now freely-available via PDF on the book's website.

  https://www.kdnuggets.com/2021/08/introduction-statistical-learning-v2.html

• WHT: A Simpler Version of the fast Fourier Transform (FFT) you should know

  The fast Walsh Hadamard transform is a simple and useful algorithm for machine learning that was popular in the 1960s and early 1970s. This useful approach should be more widely appreciated and applied for its efficiency.

  https://www.kdnuggets.com/2021/07/wht-simpler-fast-fourier-transform-fft.html

• 7 Open Source Libraries for Deep Learning Graphs

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

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

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

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

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

• Predict Customer Churn (the right way) using PyCaret

  A step-by-step guide on how to predict customer churn the right way using PyCaret that actually optimizes the business objective and improves ROI.

  https://www.kdnuggets.com/2021/07/pycaret-predict-customer-churn-right-way.html

• High-Performance Deep Learning: How to train smaller, faster, and better models – Part 3

  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.

  https://www.kdnuggets.com/2021/07/high-performance-deep-learning-part3.html

• How to Use NVIDIA GPU Accelerated Libraries

  If you are wondering how you can take advantage of NVIDIA GPU accelerated libraries for your AI projects, this guide will help answer questions and get you started on the right path.

  https://www.kdnuggets.com/2021/07/nvidia-gpu-accelerated-libraries.html

• From Scratch: Permutation Feature Importance for ML Interpretability

  Use permutation feature importance to discover which features in your dataset are useful for prediction — implemented from scratch in Python.

  https://www.kdnuggets.com/2021/06/from-scratch-permutation-feature-importance-ml-interpretability.html

• Add A New Dimension To Your Photos Using Python">Add A New Dimension To Your Photos Using Python

  Read this to learn how to breathe new life into your photos with a 3D Ken Burns Effect.

  https://www.kdnuggets.com/2021/06/new-dimension-photos-python.html

• High-Performance Deep Learning: How to train smaller, faster, and better models – Part 2

  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.

  https://www.kdnuggets.com/2021/06/high-performance-deep-learning-part2.html

• Fine-Tuning Transformer Model for Invoice Recognition

  The author presents a step-by-step guide from annotation to training.

  https://www.kdnuggets.com/2021/06/fine-tuning-transformer-model-invoice-recognition.html

• Overview of AutoNLP from Hugging Face with Example Project

  AutoNLP is a beta project from Hugging Face that builds on the company’s work with its Transformer project. With AutoNLP you can get a working model with just a few simple terminal commands.

  https://www.kdnuggets.com/2021/06/overview-autonlp-hugging-face-example-project.html

• High Performance Deep Learning, Part 1

  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.

  https://www.kdnuggets.com/2021/06/efficiency-deep-learning-part1.html

• Building a Knowledge Graph for Job Search Using BERT

  A guide on how to create knowledge graphs using NER and Relation Extraction.

  https://www.kdnuggets.com/2021/06/knowledge-graph-job-search-bert.html

• How to Generate Automated PDF Documents with Python">How to Generate Automated PDF Documents with Python

  Discover how to leverage automation to create dazzling PDF documents effortlessly.

  https://www.kdnuggets.com/2021/06/generate-automated-pdf-documents-python.html

• The 7 Best Open Source AI Libraries You May Not Have Heard Of

  AI researchers today have many exciting options for working with specialized tools. Although starting original projects from scratch is often not necessary, knowing which existing library to leverage remains a challenge. This list of generally unknown yet awesome, open-source libraries offers an interesting collection to consider for state-of-the-art research that spans from automatic machine learning to differentiable quantum circuits.

  https://www.kdnuggets.com/2021/06/7-open-source-ai-libraries.html

• How to Fine-Tune BERT Transformer with spaCy 3

  A step-by-step guide on how to create a knowledge graph using NER and Relation Extraction.

  https://www.kdnuggets.com/2021/06/fine-tune-bert-transformer-spacy.html

• PyCaret 101: An introduction for beginners

  This article is a great overview of how to get started with PyCaret for all your machine learning projects.

  https://www.kdnuggets.com/2021/06/pycaret-101-introduction-beginners.html

• Supercharge Your Machine Learning Experiments with PyCaret and Gradio

  A step-by-step tutorial to develop and interact with machine learning pipelines rapidly.

  https://www.kdnuggets.com/2021/05/supercharge-machine-learning-experiments-pycaret-gradio.html

• Write and train your own custom machine learning models using PyCaret

  A step-by-step, beginner-friendly tutorial on how to write and train custom machine learning models in PyCaret.

  https://www.kdnuggets.com/2021/05/pycaret-write-train-custom-machine-learning-models.html

• Animated Bar Chart Races in Python

  A quick and step-by-step beginners project to create an animation bar graph for an amazing Covid dataset.

  https://www.kdnuggets.com/2021/05/animated-race-bar-charts-python.html

• Easy MLOps with PyCaret + MLflow

  A beginner-friendly, step-by-step tutorial on integrating MLOps in your Machine Learning experiments using PyCaret.

  https://www.kdnuggets.com/2021/05/easy-mlops-pycaret-mlflow.html

• Machine Translation in a Nutshell

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

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

• Similarity Metrics in NLP

  This post covers the use of euclidean distance, dot product, and cosine similarity as NLP similarity metrics.

  https://www.kdnuggets.com/2021/05/similarity-metrics-nlp.html

• Feature stores – how to avoid feeling that every day is Groundhog Day

  Feature stores stop the duplication of each task in the ML lifecycle. You can reuse features and pipelines for different models, monitor models consistently, and sidestep data leakage with this MLOps technology that everyone is talking about.

  https://www.kdnuggets.com/2021/05/feature-stores-how-avoid-feeling-every-day-is-groundhog-day.html

• What is Neural Search?

  And how to get started with it with no prior experience in Machine Learning.

  https://www.kdnuggets.com/2021/05/what-neural-search.html

• Deploy a Dockerized FastAPI App to Google Cloud Platform

  A short guide to deploying a Dockerized Python app to Google Cloud Platform using Cloud Run and a SQL instance.

  https://www.kdnuggets.com/2021/05/deploy-dockerized-fastapi-app-google-cloud-platform.html

• Multiple Time Series Forecasting with PyCaret

  A step-by-step tutorial to forecast multiple time series with PyCaret.

  https://www.kdnuggets.com/2021/04/multiple-time-series-forecasting-pycaret.html

• Getting Started with Reinforcement Learning

  Demystifying some of the main concepts and terminologies associated with Reinforcement Learning and their association with other fields of AI.

  https://www.kdnuggets.com/2021/04/getting-started-reinforcement-learning.html

• Improving model performance through human participation

  Certain industries, such as medicine and finance, are sensitive to false positives. Using human input in the model inference loop can increase the final precision and recall. Here, we describe how to incorporate human feedback at inference time, so that Machines + Humans = Higher Precision & Recall.

  https://www.kdnuggets.com/2021/04/improving-model-performance-through-human-participation.html

• Time Series Forecasting with PyCaret Regression Module

  PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with few lines only. See how to use PyCaret's Regression Module for Time Series Forecasting.

  https://www.kdnuggets.com/2021/04/time-series-forecasting-pycaret-regression-module.html

• How to Apply Transformers to Any Length of Text

  Read on to find how to restore the power of NLP for long sequences.

  https://www.kdnuggets.com/2021/04/apply-transformers-any-length-text.html

• Multilingual CLIP with Huggingface + PyTorch Lightning

  An overview of training OpenAI's CLIP on Google Colab.

  https://www.kdnuggets.com/2021/03/multilingual-clip--huggingface-pytorch-lightning.html

• Learning from machine learning mistakes

  Read this article and discover how to find weak spots of a regression model.

  https://www.kdnuggets.com/2021/03/learning-from-machine-learning-mistakes.html

• A Simple Way to Time Code in Python

  Read on to find out how to use a decorator to time your functions.

  https://www.kdnuggets.com/2021/03/simple-way-time-code-python.html

• Natural Language Processing Pipelines, Explained

  This article presents a beginner's view of NLP, as well as an explanation of how a typical NLP pipeline might look.

  https://www.kdnuggets.com/2021/03/natural-language-processing-pipelines-explained.html

• A Beginner’s Guide to the CLIP Model

  CLIP is a bridge between computer vision and natural language processing. I'm here to break CLIP down for you in an accessible and fun read! In this post, I'll cover what CLIP is, how CLIP works, and why CLIP is cool.

  https://www.kdnuggets.com/2021/03/beginners-guide-clip-model.html

• DeepMind’s AlphaFold & the Protein Folding Problem

  Recently, DeepMind's AlphaFold made impressive headway in the protein structure prediction problem. Read this for an overview and explanation.

  https://www.kdnuggets.com/2021/03/deepmind-alphafold-protein-folding-problem.html

• Getting Started with Distributed Machine Learning with PyTorch and Ray

  Ray is a popular framework for distributed Python that can be paired with PyTorch to rapidly scale machine learning applications.

  https://www.kdnuggets.com/2021/03/getting-started-distributed-machine-learning-pytorch-ray.html

• Machine Learning Systems Design: A Free Stanford Course">Machine Learning Systems Design: A Free Stanford Course

  This freely-available course from Stanford should give you a toolkit for designing machine learning systems.

  https://www.kdnuggets.com/2021/02/machine-learning-systems-design-free-stanford-course.html

• The Difficulty of Graph Anonymisation

  Lessons from network science and the difficulty of graph anonymization. A data scientist's take on the difficultly of striking a balance between privacy and utility in anonymizing connected data.

  https://www.kdnuggets.com/2021/02/difficulty-graph-anonymisation.html

• Feature Store as a Foundation for Machine Learning

  With so many organizations now taking the leap into building production-level machine learning models, many lessons learned are coming to light about the supporting infrastructure. For a variety of important types of use cases, maintaining a centralized feature store is essential for higher ROI and faster delivery to market. In this review, the current feature store landscape is described, and you can learn how to architect one into your MLOps pipeline.

  https://www.kdnuggets.com/2021/02/feature-store-foundation-machine-learning.html

• IBM Uses Continual Learning to Avoid The Amnesia Problem in Neural Networks

  Using continual learning might avoid the famous catastrophic forgetting problem in neural networks.

  https://www.kdnuggets.com/2021/02/ibm-continual-learning-avoid-amnesia-problem-neural-networks.html

• Deep learning doesn’t need to be a black box">Deep learning doesn’t need to be a black box

  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.

  https://www.kdnuggets.com/2021/02/deep-learning-not-black-box.html

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

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

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

• Baidu Research: 10 Technology Trends in 2021

  Understanding future technology trends may never have been as important as it is today. Check out the prediction of the 10 technology trends in 2021 from Baidu Research.

  https://www.kdnuggets.com/2021/01/top-10-technology-trends-2021.html

• Machine learning adversarial attacks are a ticking time bomb

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

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

• What is Graph Theory, and Why Should You Care?

  Go from graph theory to path optimization.

  https://www.kdnuggets.com/2021/01/graph-theory-why-care.html

• Popular Machine Learning Interview Questions, part 2

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

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

• Deep Learning Pioneer Geoff Hinton on his Latest Research and the Future of AI

  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.

  https://www.kdnuggets.com/2021/01/deep-learning-pioneer-geoff-hinton-research-future-ai.html

• The Ultimate Scikit-Learn Machine Learning Cheatsheet">The Ultimate Scikit-Learn Machine Learning Cheatsheet

  With the power and popularity of the scikit-learn for machine learning in Python, this library is a foundation to any practitioner's toolset. Preview its core methods with this review of predictive modelling, clustering, dimensionality reduction, feature importance, and data transformation.

  https://www.kdnuggets.com/2021/01/ultimate-scikit-learn-machine-learning-cheatsheet.html

• Attention mechanism in Deep Learning, Explained

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

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

• Production Machine Learning Monitoring: Outliers, Drift, Explainers & Statistical Performance

  A practical deep dive on production monitoring architectures for machine learning at scale using real-time metrics, outlier detectors, drift detectors, metrics servers and explainers.

  https://www.kdnuggets.com/2020/12/production-machine-learning-monitoring-outliers-drift-explainers-statistical-performance.html

• Navigate the road to Responsible AI

  Deploying AI ethically and responsibly will involve cross-functional team collaboration, new tools and processes, and proper support from key stakeholders.

  https://www.kdnuggets.com/2020/12/navigate-road-responsible-ai.html

• Implementing the AdaBoost Algorithm From Scratch

  AdaBoost technique follows a decision tree model with a depth equal to one. AdaBoost is nothing but the forest of stumps rather than trees. AdaBoost works by putting more weight on difficult to classify instances and less on those already handled well. AdaBoost algorithm is developed to solve both classification and regression problem. Learn to build the algorithm from scratch here.

  https://www.kdnuggets.com/2020/12/implementing-adaboost-algorithm-from-scratch.html

• A Journey from Software to Machine Learning Engineer

  In this blog post, the author explains his journey from Software Engineer to Machine Learning Engineer. The focus of the blog post is on the areas that the author wished he'd have focused on during his learning journey, and what should you look for outside of books and courses when pursuing your Machine Learning career.

  https://www.kdnuggets.com/2020/12/journey-from-software-machine-learning-engineer.html

• Change the Background of Any Video with 5 Lines of Code

  Learn to blur, color, grayscale and create a virtual background for a video with PixelLib.

  https://www.kdnuggets.com/2020/12/change-background-video-5-lines-code.html

• Remembering Pluribus: The Techniques that Facebook Used to Master World’s Most Difficult Poker Game

  Pluribus used incredibly simple AI methods to set new records in six-player no-limit Texas Hold’em poker. How did it do it?

  https://www.kdnuggets.com/2020/12/remembering-pluribus-facebook-master-difficult-poker-game.html

• A Friendly Introduction to Graph Neural Networks

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

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

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

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

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

• 5 Things You Are Doing Wrong in PyCaret

  PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with few words only. This makes experiments exponentially fast and efficient. Find out 5 ways to improve your usage of the library.

  https://www.kdnuggets.com/2020/11/5-things-doing-wrong-pycaret.html

• Most Popular Distance Metrics Used in KNN and When to Use Them

  For calculating distances KNN uses a distance metric from the list of available metrics. Read this article for an overview of these metrics, and when they should be considered for use.

  https://www.kdnuggets.com/2020/11/most-popular-distance-metrics-knn.html

• Doing the impossible? Machine learning with less than one example

  Machine learning algorithms are notoriously known for needing data, a lot of data -- the more data the better. But, much research has gone into developing new methods that need fewer examples to train a model, such as "few-shot" or "one-shot" learning that require only a handful or a few as one example for effective learning. Now, this lower boundary on training examples is being taken to the next extreme.

  https://www.kdnuggets.com/2020/11/machine-learning-less-than-one-example.html

• Change the Background of Any Image with 5 Lines of Code

  Blur, color, grayscale and change the background of any image with a picture using PixelLib.

  https://www.kdnuggets.com/2020/11/change-background-image-5-lines-code.html

• How to deploy PyTorch Lightning models to production

  A complete guide to serving PyTorch Lightning models at scale.

  https://www.kdnuggets.com/2020/11/deploy-pytorch-lightning-models-production.html

• How to Make Sense of the Reinforcement Learning Agents?

  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.

  https://www.kdnuggets.com/2020/10/make-sense-reinforcement-learning-agents.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

• Can AI Learn Human Values?

  OpenAI believes that the path to safe AI requires social sciences.

  https://www.kdnuggets.com/2020/10/ai-learn-human-values.html

• Roadmap to Computer Vision

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

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

• Deploying Secure and Scalable Streamlit Apps on AWS with Docker Swarm, Traefik and Keycloak

  If you are a data scientist who just wants to get the work done but doesn’t necessarily want to go down the DevOps rabbit hole, this tutorial offers a relatively straightforward deployment solution leveraging Docker Swarm and Traefik, with an option of adding user authentication with Keycloak.

  https://www.kdnuggets.com/2020/10/deploying-secure-scalable-streamlit-apps-aws-docker-swarm-traefik-keycloak.html

• DeepMind Relies on this Old Statistical Method to Build Fair Machine Learning Models

  Causal Bayesian Networks are used to model the influence of fairness attributes in a dataset.

  https://www.kdnuggets.com/2020/10/deepmind-relies-statistical-method-build-fair-machine-learning-models.html

• The Ethics of AI

  Marketing scientist Kevin Gray asks Dr. Anna Farzindar of the University of Southern California about a very important subject - the ethics of AI.

  https://www.kdnuggets.com/2020/10/ethics-ai-qa-farzindar.html

• Deploying Streamlit Apps Using Streamlit Sharing

  Read this sneak peek into Streamlit’s new deployment platform.

  https://www.kdnuggets.com/2020/10/deploying-streamlit-apps-streamlit-sharing.html

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

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

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

• Getting Started with PyTorch

  A practical walkthrough on how to use PyTorch for data analysis and inference.

  https://www.kdnuggets.com/2020/10/getting-started-pytorch.html

• A Guide to Preparing OpenCV for Android

  This tutorial guides Android developers in preparing the popular library OpenCV for use. Using a step-by-step guide, the library will be imported into Android Studio and then can be used for performing any of the operations it supports, such as object detection, segmentation, tracking, and more.

  https://www.kdnuggets.com/2020/10/guide-preparing-opencv-android.html

• AI in Healthcare: A review of innovative startups

  The AI innovation in healthcare has been overwhelming with the Global Healthcare AI Market accounting for $0.95 billion in 2017, and is expected to reach $19.25 billion by 2026. What drives this vibrant growth?

  https://www.kdnuggets.com/2020/09/ai-healthcare-review-innovative-startups.html

• How AI is Driving Innovation in Astronomy

  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.

  https://www.kdnuggets.com/2020/09/ai-driving-innovation-astronomy.html

• Looking Inside The Blackbox: How To Trick A Neural Network

  In this tutorial, I’ll show you how to use gradient ascent to figure out how to misclassify an input.

  https://www.kdnuggets.com/2020/09/inside-blackbox-trick-neural-network.html

• Making Python Programs Blazingly Fast

  Let’s look at the performance of our Python programs and see how to make them up to 30% faster!

  https://www.kdnuggets.com/2020/09/making-python-programs-blazingly-fast.html

• Implementing a Deep Learning Library from Scratch in Python">Implementing a Deep Learning Library from Scratch in Python

  A beginner’s guide to understanding the fundamental building blocks of deep learning platforms.

  https://www.kdnuggets.com/2020/09/implementing-deep-learning-library-scratch-python.html

• Visualization Of COVID-19 New Cases Over Time In Python

  Inspired by another concise data visualization, the author of this article has crafted and shared the code for a heatmap which visualizes the COVID-19 pandemic in the United States over time.

  https://www.kdnuggets.com/2020/09/visualization-covid-19-new-cases-over-time-python.html

• 4 Tricks to Effectively Use JSON in Python

  Working with JSON in Python is a breeze, this will get you started right away.

  https://www.kdnuggets.com/2020/09/4-tricks-effectively-use-json-python.html

• Creating Powerful Animated Visualizations in Tableau">Creating Powerful Animated Visualizations in Tableau

  In this post we explore animated data visualization in Tableau,one of the tool's powerful features for making visualizations appealing and interactive.

  https://www.kdnuggets.com/2020/09/creating-powerful-animated-visualizations-tableau.html

• Which methods should be used for solving linear regression?

  As a foundational set of algorithms in any machine learning toolbox, linear regression can be solved with a variety of approaches. Here, we discuss. with with code examples, four methods and demonstrate how they should be used.

  https://www.kdnuggets.com/2020/09/solving-linear-regression.html

• DeepMind’s Three Pillars for Building Robust Machine Learning Systems

  Specification Testing, Robust Training and Formal Verification are three elements that the AI powerhouse believe hold the essence of robust machine learning models.

  https://www.kdnuggets.com/2020/08/deepmind-three-pillars-building-robust-machine-learning-systems.html

• Build Your Own AutoML Using PyCaret 2.0

  In this post we present a step-by-step tutorial on how PyCaret can be used to build an Automated Machine Learning Solution within Power BI, thus allowing data scientists and analysts to add a layer of machine learning to their Dashboards without any additional license or software costs.

  https://www.kdnuggets.com/2020/08/build-automl-pycaret.html

• Top Google AI, Machine Learning Tools for Everyone">Top Google AI, Machine Learning Tools for Everyone

  Google is much more than a search company. Learn about all the tools they are developing to help turn your ideas into reality through Google AI.

  https://www.kdnuggets.com/2020/08/top-google-ai-machine-learning-tools.html