Search results for "gradient descent"

292 documents found out of 7025 total.

• Gradient Descent: The Mountain Trekker’s Guide to Optimization with Mathematics

  Gradient descent is an optimization technique used to minimise errors in machine learning models. By iteratively adjusting parameters in the steepest direction of decrease, it seeks the lowest error value.

  https://www.kdnuggets.com/gradient-descent-the-mountain-trekker-guide-to-optimization-with-mathematics

• Back To Basics, Part Dos: Gradient Descent

  Explore the inner workings of the powerful optimization algorithm.

  https://www.kdnuggets.com/2023/03/back-basics-part-dos-gradient-descent.html

• 5 Concepts You Should Know About Gradient Descent and Cost Function

  Why is Gradient Descent so important in Machine Learning? Learn more about this iterative optimization algorithm and how it is used to minimize a loss function.

  https://www.kdnuggets.com/2020/05/5-concepts-gradient-descent-cost-function.html

• 10 Gradient Descent Optimisation Algorithms + Cheat Sheet

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

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

• An Intuitive Introduction to Gradient Descent

  This post provides a good introduction to Gradient Descent, covering the intuition, variants and choosing the learning rate.

  https://www.kdnuggets.com/2018/06/intuitive-introduction-gradient-descent.html

• Neural Network Foundations, Explained: Updating Weights with Gradient Descent & Backpropagation

  In neural networks, connection weights are adjusted in order to help reconcile the differences between the actual and predicted outcomes for subsequent forward passes. But how, exactly, do these weights get adjusted?

  https://www.kdnuggets.com/2017/10/neural-network-foundations-explained-gradient-descent.html

• The Two Phases of Gradient Descent in Deep Learning

  In short, you reach different resting placing with different SGD algorithms. That is, different SGDs just give you differing convergence rates due to different strategies, but we do expect that they all end up at the same results!

  https://www.kdnuggets.com/2017/05/two-phases-gradient-descent-deep-learning.html

• Keep it simple! How to understand Gradient Descent algorithm">Keep it simple! How to understand Gradient Descent algorithm

  In Data Science, Gradient Descent is one of the important and difficult concepts. Here we explain this concept with an example, in a very simple way. Check this out.

  https://www.kdnuggets.com/2017/04/simple-understand-gradient-descent-algorithm.html

• Learning to Learn by Gradient Descent by Gradient Descent

  What if instead of hand designing an optimising algorithm (function) we learn it instead? That way, by training on the class of problems we’re interested in solving, we can learn an optimum optimiser for the class!

  https://www.kdnuggets.com/2017/02/learning-learn-gradient-descent.html

• Gradient Boosted Decision Trees – A Conceptual Explanation

  Gradient boosted decision trees involves implementing several models and aggregating their results. These boosted models have become popular thanks to their performance in machine learning competitions on Kaggle. In this article, we’ll see what gradient boosted decision trees are all about.

  https://www.kdnuggets.com/2021/04/gradient-boosted-trees-conceptual-explanation.html

• Fast Gradient Boosting with CatBoost

  In this piece, we’ll take a closer look at a gradient boosting library called CatBoost.

  https://www.kdnuggets.com/2020/10/fast-gradient-boosting-catboost.html

• Linear Regression from Scratch with NumPy

  Mastering the Basics of Linear Regression and Fundamentals of Gradient Descent and Loss Minimization.

  https://www.kdnuggets.com/linear-regression-from-scratch-with-numpy

• KDnuggets News, September 21: 7 Machine Learning Portfolio Projects to Boost the Resume • Free SQL and Database Course

  7 Machine Learning Portfolio Projects to Boost the Resume • Free SQL and Database Course • Top 5 Bookmarks Every Data Analyst Should Have • 7 Steps to Mastering Python for Data Science • 5 Concepts You Should Know About Gradient Descent and Cost Function

  https://www.kdnuggets.com/2022/n37.html

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

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

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

• The Hundred-Page Machine Learning Book

  This book covers supervised and unsupervised learning, support vector machines, neural networks, ensemble methods, gradient descent, cluster analysis and dimensionality reduction, autoencoders and transfer learning, feature engineering and hyperparameter tuning.

  https://www.kdnuggets.com/2019/01/hundred-page-machine-learning-book.html

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

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

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

• Artificial Neural Networks (ANN) Introduction, Part 2

  Matching the performance of a human brain is a difficult feat, but techniques have been developed to improve the performance of neural network algorithms, 3 of which are discussed in this post: Distortion, mini-batch gradient descent, and dropout.

  https://www.kdnuggets.com/2016/12/artificial-neural-networks-intro-part-2.html

• A Concise Overview of Standard Model-fitting Methods

  A very concise overview of 4 standard model-fitting methods, focusing on their differences: closed-form equations, gradient descent, stochastic gradient descent, and mini-batch learning.

  https://www.kdnuggets.com/2016/05/concise-overview-model-fitting-methods.html

• The 5 FREE Must-Read Books for Every Machine Learning Engineer

  Learn the theory, math, and engineering behind machine learning with these highly recommended free books.

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

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

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

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

• Is ChatGPT Study Mode a Hidden Gem or a Gimmick?

  This article critically explores both perspectives, weighing the benefits, drawbacks, and future potential of Study Mode to determine whether it lives up to the hype.

  https://www.kdnuggets.com/is-chatgpt-study-mode-a-hidden-gem-or-a-gimmick

• How to Learn Math for Data Science: A Roadmap for Beginners

  Confused about where to start with data science math? Learn what math concepts to learn, in what order, and how to use them in practice.

  https://www.kdnuggets.com/how-to-learn-math-for-data-science-a-roadmap-for-beginners

• From Python to AI Engineer: A Self-Study Roadmap

  A practical roadmap for Python programmers to develop the advanced skills, specialized knowledge, and engineering mindset needed to become successful AI engineers in 2025.

  https://www.kdnuggets.com/from-python-to-ai-engineer-a-self-study-roadmap

• Top 5 Career Paths in Data Science and How to Self-Learn for Each

  Are you a self-learner wanting to break into one of the top 5 data science career paths? If yes, this article is for you.

  https://www.kdnuggets.com/top-5-career-paths-in-data-science-and-how-to-self-learn-for-each

• Math, Machine Learning & Coding Needed For LLMs

  The goal of this article is to guide you through the essential mathematical foundations, machine learning techniques, and coding practices needed to work with LLMs.

  https://www.kdnuggets.com/math-machine-learning-coding-needed-llms

• The Ultimate Roadmap to Becoming an LLM Engineer

  Unsure what to learn, where to start, and which order to follow to master LLM engineering concepts and skills? This comprehensive roadmap with clear milestones and stages is here to help!

  https://www.kdnuggets.com/ultimate-roadmap-llm-engineer

• My Top Picks: 5 Free NLP Courses I’d Recommend for 2025

  Want to become an NLP pro by 2025? Check out these top free courses and learn from experts who’ve shaped the future of language models.

  https://www.kdnuggets.com/top-picks-5-free-nlp-courses-recommend-2025

• Top 5 Tips for Fine-Tuning LLMs

  Whether you’re building an LLM from scratch or augmenting an LLM with additional finetuning data, following these tips will deliver a more robust model.

  https://www.kdnuggets.com/top-5-tips-fine-tuning-llms

• Math Myths Busted: What Beginners Actually Need for Data Science

  Terrified of calculus but dream of being a data scientist? Breathe easy! Discover the surprising truth about math in data science and how you can succeed without being a math genius.

  https://www.kdnuggets.com/math-myths-busted-beginners-actually-need-data-science

• 5 Free Courses to Understand Machine Learning Algorithms

  To help you navigate this complex subject, we’ve compiled five free online courses that will give you a solid foundation in machine learning algorithms.

  https://www.kdnuggets.com/5-free-courses-understand-machine-learning-algorithms

• 7 Steps to Mastering Math for Data Science

  Want to learn math for data science? This guide will help you go about learning math for data science—linear algebra, calculus, statistics, and more.

  https://www.kdnuggets.com/7-steps-to-mastering-math-for-data-science

• 5 Free Courses to Master Math for Data Science

  Want to learn math for data science? Check out these three courses to learn linear algebra, calculus, statistics, and more.

  https://www.kdnuggets.com/5-free-courses-to-master-math-for-data-science

• The Only Interview Prep Course You Need for Deep Learning

  Dive into the 50 most popular deep-learning questions to get you ready for your interview.

  https://www.kdnuggets.com/the-only-interview-prep-course-you-need-for-deep-learning

• 5 Free Courses to Master Python for Data Science

  Want to learn Python to kickstart your career in data? Here are five free courses to help you master Python for data science.

  https://www.kdnuggets.com/5-free-courses-to-master-python-for-data-science

• Free Harvard Course: Introduction to AI with Python

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

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

• WTF is the Difference Between GBM and XGBoost?

  See the substantial differences between the famous algorithms.

  https://www.kdnuggets.com/wtf-is-the-difference-between-gbm-and-xgboost

• Building Predictive Models: Logistic Regression in Python

  Want to learn how to build predictive models using logistic regression? This tutorial covers logistic regression in depth with theory, math, and code to help you build better models.

  https://www.kdnuggets.com/building-predictive-models-logistic-regression-in-python

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

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

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

• 5 Free Books to Master Machine Learning

  Machine Learning is one of the most exciting fields in computer science today. In this article, we will take a look at the five best yet free books to learn machine learning in 2023.

  https://www.kdnuggets.com/5-free-books-to-master-machine-learning

• A Brief History of the Neural Networks

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

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

• 7 Steps to Mastering Natural Language Processing

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

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

• 30 Years of Data Science: A Review From a Data Science Practitioner

  A review from a data science practitioner.

  https://www.kdnuggets.com/30-years-of-data-science-a-review-from-a-data-science-practitioner

• Exploring Neural Networks

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

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

• Unveiling Neural Magic: A Dive into Activation Functions

  Cracking the code of activation functions: Demystifying their purpose, selection, and timing.

  https://www.kdnuggets.com/unveiling-neural-magic-a-dive-into-activation-functions

• Understanding Unsupervised Learning

  Explore the unsupervised learning paradigm. Familiarize yourself with the key concepts, techniques, and popular unsupervised learning algorithms.

  https://www.kdnuggets.com/unveiling-unsupervised-learning

• From Zero to Hero: Create Your First ML Model with PyTorch

  Learn the PyTorch basics by building a classification model from scratch.

  https://www.kdnuggets.com/from-zero-to-hero-create-your-first-ml-model-with-pytorch

• Want to Become a Data Scientist? Part 1: 10 Hard Skills You Need

  A quick 10-step hard skill guide on what you need to become a Data Scientist.

  https://www.kdnuggets.com/want-to-become-a-data-scientist-part-1-10-hard-skills-you-need

• An Excellent Resource To Learn The Foundations Of Everything Underneath ChatGPT

  In this article, you will learn the fundamentals of what constitutes the core of ChatGPT (and the Large Language Models).

  https://www.kdnuggets.com/023/08/excellent-resource-learn-foundations-everything-underneath-chatgpt.html

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

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

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

• 5 Mistakes I Made While Switching to Data Science Career

  Learn from my mistakes and avoid making the same mistakes.

  https://www.kdnuggets.com/2023/07/5-mistakes-made-switching-data-science-career.html

• Unlock the Secrets to Choosing the Perfect Machine Learning Algorithm!

  When working on a data science problem, one of the most important choices to make is selecting the appropriate machine learning algorithm.

  https://www.kdnuggets.com/2023/07/ml-algorithm-choose.html

• Exploring the Power and Limitations of GPT-4

  Unveiling GPT-4: Deciphering its impact on data science and exploring its strengths and boundaries.

  https://www.kdnuggets.com/2023/07/exploring-power-limitations-gpt4.html

• Making Predictions: A Beginner’s Guide to Linear Regression in Python

  Learn everything about the most popular Machine Learning algorithm, Linear Regression, with its Mathematical Intuition and Python implementation.

  https://www.kdnuggets.com/2023/06/making-predictions-beginner-guide-linear-regression-python.html

• Best Machine Learning Model For Sparse Data

  Sparse Data Survival Guide: Strategies for Success with Machine Learning.

  https://www.kdnuggets.com/2023/04/best-machine-learning-model-sparse-data.html

• How to Use ChatGPT to Improve Your Data Science Skills

  And How to Speed up your research of data science resources without wasting energy.

  https://www.kdnuggets.com/2023/03/chatgpt-improve-data-science-skills.html

• KDnuggets News, March 15: 4 Ways to Generate Passive Income Using ChatGPT • Simpson’s Paradox and its Implications in Data Science

  4 Ways to Generate Passive Income Using ChatGPT • Simpson's Paradox and its Implications in Data Science • ChatGPT vs Google Bard: A Comparison of the Technical Differences • Master the Power of Data Analytics: The Four Approaches to Analyzing Data • GitHub CLI for Data Science Cheat Sheet

  https://www.kdnuggets.com/2023/n10.html

• Free Data Management with Data Science Learning with CS639

  Learn Data Management with Data Science for FREE with CS639.

  https://www.kdnuggets.com/2023/01/free-data-management-data-science-learning-cs639.html

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

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

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

• Tuning Adam Optimizer Parameters in PyTorch

  Choosing the right optimizer to minimize the loss between the predictions and the ground truth is one of the crucial elements of designing neural networks.

  https://www.kdnuggets.com/2022/12/tuning-adam-optimizer-parameters-pytorch.html

• From Data to Verse: KDnuggets and ChatGPT in Conversation

  KDnuggets recently had the opportunity to sit down with newly-released acclaimed artificial intelligence ChatGTP from OpenAI. What we found during the course of conversation was both interesting and surprising. Read on to find out what ChatGPT knew about data science and much more.

  https://www.kdnuggets.com/2022/12/kdnuggets-chatgpt-conversation.html

• 3 Free Machine Learning Courses for Beginners

  Begin your machine learning career with free courses by Georgia Tech, Stanford, and Fast AI.

  https://www.kdnuggets.com/2022/12/3-free-machine-learning-courses-beginners.html

• What Google Recommends You do Before Taking Their Machine Learning or Data Science Course

  First steps to learning data science & machine learning are the foundations.

  https://www.kdnuggets.com/2021/10/google-recommends-before-machine-learning-data-science-course.html

• Comparing Linear and Logistic Regression

  Discussion on an entry-level data science interview question.

  https://www.kdnuggets.com/2022/11/comparing-linear-logistic-regression.html

• The Inescapable Conclusion: Machine Learning Is Not Like Your Brain

  The final article in this nine-part series summarizes the many reasons why Machine Learning is not like your brain - along with a few similarities.

  https://www.kdnuggets.com/2022/11/inescapable-conclusion-machine-learning-like-brain.html

• How Much Math Do You Need in Data Science?

  There exist so many great computational tools available for Data Scientists to perform their work. However, mathematical skills are still essential in data science and machine learning because these tools will only be black-boxes for which you will not be able to ask core analytical questions without a theoretical foundation.

  https://www.kdnuggets.com/2020/06/math-data-science.html

• Top 5 Machine Learning Practices Recommended by Experts

  This article is intended to help beginners improve their model structure by listing the best practices recommended by machine learning experts.

  https://www.kdnuggets.com/2022/09/top-5-machine-learning-practices-recommended-experts.html

• Data Transformation: Standardization vs Normalization

  Increasing accuracy in your models is often obtained through the first steps of data transformations. This guide explains the difference between the key feature scaling methods of standardization and normalization, and demonstrates when and how to apply each approach.

  https://www.kdnuggets.com/2020/04/data-transformation-standardization-normalization.html

• Calculus for Data Science

  In this article, we discuss the importance of calculus in data science and machine learning.

  https://www.kdnuggets.com/2022/07/calculus-data-science.html

• 12 Most Challenging Data Science Interview Questions

  The simple but tricky data science questions that most people struggle to answer.

  https://www.kdnuggets.com/2022/07/12-challenging-data-science-interview-questions.html

• How Does Logistic Regression Work?

  Logistic regression is a machine learning classification algorithm that is used to predict the probability of certain classes based on some dependent variables

  https://www.kdnuggets.com/2022/07/logistic-regression-work.html

• Boosting Machine Learning Algorithms: An Overview

  The combination of several machine learning algorithms is referred to as ensemble learning. There are several ensemble learning techniques. In this article, we will focus on boosting.

  https://www.kdnuggets.com/2022/07/boosting-machine-learning-algorithms-overview.html

• Machine Learning Is Not Like Your Brain Part 4: The Neuron’s Limited Ability to Represent Precise Values

  In the fourth installment, we focus on a fundamental issue: it is difficult to represent numerical values in neurons and impractical to represent them with precision.

  https://www.kdnuggets.com/2022/06/machine-learning-like-brain-part-4-neuron-limited-ability-represent-precise-values.html

• Deep Learning Key Terms, Explained

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

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

• How Activation Functions Work in Deep Learning

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

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

• Machine Learning Is Not Like Your Brain Part 3: Fundamental Architecture

  Part three of this series examines the fundamental architecture underlying machine learning and the brain.

  https://www.kdnuggets.com/2022/06/machine-learning-like-brain-part-3-fundamental-architecture.html

• Data Science, Statistics and Machine Learning Dictionary

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

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

• Logistic Regression for Classification

  Deep dive into Logistic Regression with practical examples.

  https://www.kdnuggets.com/2022/04/logistic-regression-classification.html

• Calculus: The hidden building block of machine learning

  Unless you have a basic knowledge of calculus, you cannot understand how machine learning algorithms are developed. Calculus for Machine Learning is designed for developers to get you up to speed on the calculus that you need for applied machine learning. The book has more math than our other books and over 85 code examples to help you understand the concepts.

  https://www.kdnuggets.com/2022/02/mlm-hidden-building-block-machine-learning.html

• Top 5 Free Machine Learning Courses

  Give a boost to your career and learn job-ready machine learning skills by taking the best free online courses.

  https://www.kdnuggets.com/2022/02/top-5-free-machine-learning-courses.html

• How to Learn Math for Machine Learning

  So how much math do you need to know in order to work in the data science industry? The answer: Not as much as you think.

  https://www.kdnuggets.com/2022/02/learn-math-machine-learning.html

• An Overview of Logistic Regression

  Logistic regression is an extension of linear regression to solve classification problems. Read more on the specifics of this algorithm here.

  https://www.kdnuggets.com/2022/02/overview-logistic-regression.html

• Interpretable Neural Networks with PyTorch

  Learn how to build feedforward neural networks that are interpretable by design using PyTorch.

  https://www.kdnuggets.com/2022/01/interpretable-neural-networks-pytorch.html

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

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

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

• 5 Key Skills Needed To Become a Great Data Scientist">5 Key Skills Needed To Become a Great Data Scientist

  Based on 10 years of my experience (learn to build those skills).

  https://www.kdnuggets.com/2021/12/5-key-skills-needed-become-great-data-scientist.html

• 10 Simple Things to Try Before Neural Networks

  Below are 10 simple things you should remember to try first before throwing in the towel and jumping straight to neural networks.

  https://www.kdnuggets.com/2021/12/10-simple-things-try-neural-networks.html

• A First Principles Theory of Generalization

  Some new research from University of California, Berkeley shades some new light into how to quantify neural networks knowledge.

  https://www.kdnuggets.com/2021/11/first-principles-theory-generalization.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

• Learning Data Science and Machine Learning: First Steps After The Roadmap">Learning Data Science and Machine Learning: First Steps After The Roadmap

  Just getting into learning data science may seem as daunting as (if not more than) trying to land your first job in the field. With so many options and resources online and in traditional academia to consider, these pre-requisites and pre-work are recommended before diving deep into data science and AI/ML.

  https://www.kdnuggets.com/2021/08/learn-data-science-machine-learning.html

• How Can You Distinguish Yourself from Hundreds of Other Data Science Candidates?">How Can You Distinguish Yourself from Hundreds of Other Data Science Candidates?

  A few easy (and not-so-easy) ways to prove to employers that your skills and attitudes place you in a higher bracket.

  https://www.kdnuggets.com/2021/07/distinguish-yourself-hundreds-other-data-science-candidates.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

• A Comprehensive Guide to Ensemble Learning – Exactly What You Need to Know

  This article covers ensemble learning methods, and exactly what you need to know in order to understand and implement them.

  https://www.kdnuggets.com/2021/05/comprehensive-guide-ensemble-learning.html

• What is Adversarial Neural Cryptography?

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

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

• 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

• The Ultimate Guide to Acing Coding Interviews for Data Scientists">The Ultimate Guide to Acing Coding Interviews for Data Scientists

  This article covers understanding the 4 types of coding interview questions and preparing for them effectively.

  https://www.kdnuggets.com/2021/03/ultimate-guide-acing-coding-interviews-data-scientists.html

• 10 Statistical Concepts You Should Know For Data Science Interviews

  Data Science is founded on time-honored concepts from statistics and probability theory. Having a strong understanding of the ten ideas and techniques highlighted here is key to your career in the field, and also a favorite topic for concept checks during interviews.

  https://www.kdnuggets.com/2021/02/10-statistical-concepts-data-science-interviews.html

• Saving and loading models in TensorFlow — why it is important and how to do it

  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.

  https://www.kdnuggets.com/2021/02/saving-loading-models-tensorflow.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

• Popular Machine Learning Interview Questions">Popular Machine Learning Interview Questions

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

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

• All Machine Learning Algorithms You Should Know in 2021">All Machine Learning Algorithms You Should Know in 2021

  Many machine learning algorithms exits that range from simple to complex in their approach, and together provide a powerful library of tools for analyzing and predicting patterns from data. If you are learning for the first time or reviewing techniques, then these intuitive explanations of the most popular machine learning models will help you kick off the new year with confidence.

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

• Optimization Algorithms in Neural Networks">Optimization Algorithms in Neural Networks

  This article presents an overview of some of the most used optimizers while training a neural network.

  https://www.kdnuggets.com/2020/12/optimization-algorithms-neural-networks.html

• MLOps – “Why is it required?” and “What it is”?

  Creating an model that works well is only a small aspect of delivering real machine learning solutions. Learn about the motivation behind MLOps, the framework and its components that will help you get your ML model into production, and its relation to DevOps from the world of traditional software development.

  https://www.kdnuggets.com/2020/12/mlops-why-required-what-is.html

• 20 Core Data Science Concepts for Beginners">20 Core Data Science Concepts for Beginners

  With so much to learn and so many advancements to follow in the field of data science, there are a core set of foundational concepts that remain essential. Twenty of these ideas are highlighted here that are key to review when preparing for a job interview or just to refresh your appreciation of the basics.

  https://www.kdnuggets.com/2020/12/20-core-data-science-concepts-beginners.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

• Know-How to Learn Machine Learning Algorithms Effectively

  The takeaway from the story is that machine learning is way beyond a simple fit and predict methods. The author shares their approach to actually learning these algorithms beyond the surface.

  https://www.kdnuggets.com/2020/11/learn-machine-learning-algorithms-effectively.html

• Adversarial Examples in Deep Learning – A Primer

  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.

  https://www.kdnuggets.com/2020/11/adversarial-examples-deep-learning-primer.html

• Algorithms for Advanced Hyper-Parameter Optimization/Tuning

  In informed search, each iteration learns from the last, whereas in Grid and Random, modelling is all done at once and then the best is picked. In case for small datasets, GridSearch or RandomSearch would be fast and sufficient. AutoML approaches provide a neat solution to properly select the required hyperparameters that improve the model’s performance.

  https://www.kdnuggets.com/2020/11/algorithms-for-advanced-hyper-parameter-optimization-tuning.html

• Microsoft and Google Open Sourced These Frameworks Based on Their Work Scaling Deep Learning Training

  Google and Microsoft have recently released new frameworks for distributed deep learning training.

  https://www.kdnuggets.com/2020/11/microsoft-google-open-sourced-frameworks-scaling-deep-learning-training.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

• 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

• Your Guide to Linear Regression Models

  This article explains linear regression and how to program linear regression models in Python.

  https://www.kdnuggets.com/2020/10/guide-linear-regression-models.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

• Autograd: The Best Machine Learning Library You’re Not Using?">Autograd: The Best Machine Learning Library You’re Not Using?

  If there is a Python library that is emblematic of the simplicity, flexibility, and utility of differentiable programming it has to be Autograd.

  https://www.kdnuggets.com/2020/09/autograd-best-machine-learning-library-not-using.html

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

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

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

• 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

• Introduction to Federated Learning">Introduction to Federated Learning

  Federated learning means enabling on-device training, model personalization, and more. Read more about it in this article.

  https://www.kdnuggets.com/2020/08/introduction-federated-learning.html

• How Do Neural Networks Learn?

  With neural networks being so popular today in AI and machine learning development, they can still look like a black box in terms of how they learn to make predictions. To understand what is going on deep in these networks, we must consider how neural networks perform optimization.

  https://www.kdnuggets.com/2020/08/how-neural-networks-learn.html

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

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

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

• Foundations of Data Science: The Free eBook

  As has become tradition on KDnuggets, let's start a new week with a new eBook. This time we check out a survey style text with a variety of topics, Foundations of Data Science.

  https://www.kdnuggets.com/2020/07/foundations-data-science-free-ebook.html

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

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

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

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

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

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

• Graph Machine Learning in Genomic Prediction

  This work explores how genetic relationships can be exploited alongside genomic information to predict genetic traits with the aid of graph machine learning algorithms.

  https://www.kdnuggets.com/2020/06/graph-machine-learning-genomic-prediction.html

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

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

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

• Understanding Machine Learning: The Free eBook">Understanding Machine Learning: The Free eBook

  Time to get back to basics. This week we have a look at a book on foundational machine learning concepts, Understanding Machine Learning: From Theory to Algorithms.

  https://www.kdnuggets.com/2020/06/understanding-machine-learning-free-ebook.html

• Introduction to Convolutional Neural Networks

  The article focuses on explaining key components in CNN and its implementation using Keras python library.

  https://www.kdnuggets.com/2020/06/introduction-convolutional-neural-networks.html

• Linear algebra and optimization and machine learning: A textbook

  This book teaches linear algebra and optimization as the primary topics of interest, and solutions to machine learning problems as applications of these methods. Therefore, the book also provides significant exposure to machine learning.

  https://www.kdnuggets.com/2020/05/charu-linear-algebra-optimization-machine-learning-textbook.html

• Hyperparameter Optimization for Machine Learning Models

  Check out this comprehensive guide to model optimization techniques.

  https://www.kdnuggets.com/2020/05/hyperparameter-optimization-machine-learning-models.html

• Google Open Sources SimCLR, A Framework for Self-Supervised and Semi-Supervised Image Training

  The new framework uses contrastive learning to improve image analysis in unlabeled datasets.

  https://www.kdnuggets.com/2020/04/google-open-sources-simclr-self-supervised-semi-supervised-image-training.html

• Federated Learning: An Introduction

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

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

• Deep Learning Breakthrough: a sub-linear deep learning algorithm that does not need a GPU?

  Deep Learning sits at the forefront of many important advances underway in machine learning. With backpropagation being a primary training method, its computational inefficiencies require sophisticated hardware, such as GPUs. Learn about this recent breakthrough algorithmic advancement with improvements to the backpropgation calculations on a CPU that outperforms large neural network training with a GPU.

  https://www.kdnuggets.com/2020/03/deep-learning-breakthrough-sub-linear-algorithm-no-gpu.html