Search results for cost function

Found 965 documents, 5922 searched:

• 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

• Interpreting Model Performance with Cost Functions

  Cost functions are critical for the correct assessment of performance of data mining and predictive models. This series goes deep into the statistical properties and mathematical understanding of each cost function and explores their similarities and differences.

  https://www.kdnuggets.com/2014/01/salford-interpreting-model-performance-with-cost-functions.html

• HuggingChat Python API: Your No-Cost Alternative

  HuggingChat is a free and open source alternative to commercial chat offerings such as ChatGPT. The unofficial Python API gives you immediate access, without signup, for free.

  https://www.kdnuggets.com/2023/05/huggingchat-python-api-alternative.html

• How a Level System can Help Forecast AI Costs

  To forecast costs for AI systems, it can be useful to talk about their “level” just like SAE has levels for self-driving cars. Adopting a level system can help organizations plan and prepare for AI systems that scale in complexity over time.

  https://www.kdnuggets.com/2022/03/level-system-help-forecast-ai-costs.html

• Top Five SQL Window Functions You Should Know For Data Science Interviews

  Focusing on the important concepts for data scientists.

  https://www.kdnuggets.com/2022/01/top-five-sql-window-functions-know-data-science-interviews.html

• Reducing the High Cost of Training NLP Models With SRU++

  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.

  https://www.kdnuggets.com/2021/03/reducing-high-cost-training-nlp-models-sru.html

• Choosing an Error Function

  The error function expresses how much we care about a deviation of a certain size. The choice of error function depends entirely on how our model will be used.

  https://www.kdnuggets.com/2019/06/choosing-error-function.html

• Cartoon: Halloween Costume for Big Data.

  We revisit KDnuggets cartoon looking at the appropriate Halloween costume for Big Data and its companion, No Privacy.

  https://www.kdnuggets.com/2018/10/cartoon-halloween-big-data-no-privacy.html

• Using Confusion Matrices to Quantify the Cost of Being Wrong

  The terms ‘true condition’ (‘positive outcome’) and ‘predicted condition’ (‘negative outcome’) are used when discussing Confusion Matrices. This means that you need to understand the differences (and eventually the costs associated) with Type I and Type II Errors.

  https://www.kdnuggets.com/2018/10/confusion-matrices-quantify-cost-being-wrong.html

• Understanding Objective Functions in Neural Networks

  This blog post is targeted towards people who have experience with machine learning, and want to get a better intuition on the different objective functions used to train neural networks.

  https://www.kdnuggets.com/2017/11/understanding-objective-functions-neural-networks.html

• 7 Ways to Get High-Quality Labeled Training Data at Low Cost

  Having labeled training data is needed for machine learning, but getting such data is not simple or cheap. We review 7 approaches including repurposing, harvesting free sources, retrain models on progressively higher quality data, and more.

  https://www.kdnuggets.com/2017/06/acquiring-quality-labeled-training-data.html

• The Costs of Misclassifications

  Importance of correct classification and hazards of misclassification are subjective or we can say varies on case-to-case. Lets see how cost of misclassification is measured from monetary perspective.

  https://www.kdnuggets.com/2016/12/salford-costs-misclassifications.html

• The High Cost of Maintaining Machine Learning Systems

  Google researchers warn of the massive ongoing costs for maintaining machine learning systems. We examine how to minimize the technical debt.

  https://www.kdnuggets.com/2015/01/high-cost-machine-learning-technical-debt.html

• 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

• 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

• K-Means in Real Life: Clustering Workout Sessions

  By using the within-cluster sum of squares as cost function, data points in the same cluster will be similar to each other, whereas data points in different clusters will have a lower level of similarity.

  https://www.kdnuggets.com/2018/08/k-means-real-life-clustering-workout-sessions.html

• WTF is Regularization and What is it For?

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

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

• 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

• 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

• Unveiling 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

• 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

• What Is ChatGPT Doing and Why Does It Work?

  In this article, we will explain how ChatGPT works and why it is able to produce coherent and diverse conversations.

  https://www.kdnuggets.com/2023/04/chatgpt-work.html

• 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

• Key Issues Associated with Classification Accuracy

  In this blog, we will unfold the key problems associated with classification accuracies, such as imbalanced classes, overfitting, and data bias, and proven ways to address those issues successfully.

  https://www.kdnuggets.com/2023/03/key-issues-associated-classification-accuracy.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

• 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

• 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

• 5 Free Courses to Master Calculus

  Calculus is one of the foundational pillars of understanding the mathematics behind machine learning algorithms. The post shares five free courses to help you master calculus and learn its real-world applications.

  https://www.kdnuggets.com/2022/10/5-free-courses-master-calculus.html

• Dimensionality Reduction Techniques in Data Science

  Dimensionality reduction techniques are basically a part of the data pre-processing step, performed before training the model.

  https://www.kdnuggets.com/2022/09/dimensionality-reduction-techniques-data-science.html

• Machine Learning Metadata Store

  In this article, we will learn about metadata stores, the need for them, their components, and metadata store management.

  https://www.kdnuggets.com/2022/08/machine-learning-metadata-store.html

• 7 Techniques to Handle Imbalanced Data

  This blog post introduces seven techniques that are commonly applied in domains like intrusion detection or real-time bidding, because the datasets are often extremely imbalanced.

  https://www.kdnuggets.com/2017/06/7-techniques-handle-imbalanced-data.html

• How to Avoid Overfitting

  Overfitting is when a statistical model fits exactly against its training data. This leads to the model failing to predict future observations accurately.

  https://www.kdnuggets.com/2022/08/avoid-overfitting.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

• 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

• 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

• Linear vs Logistic Regression: A Succinct Explanation

  Linear Regression and Logistic Regression are two well-used Machine Learning Algorithms that both branch off from Supervised Learning. Linear Regression is used to solve Regression problems whereas Logistic Regression is used to solve Classification problems. Read more here.

  https://www.kdnuggets.com/2022/03/linear-logistic-regression-succinct-explanation.html

• Essential Machine Learning Algorithms: A Beginner’s Guide

  Machine Learning as a technology, ensures that our current gadgets and their software get smarter by the day. Here are the algorithms that you ought to know about to understand Machine Learning’s varied and extensive functionalities and their effectiveness.

  https://www.kdnuggets.com/2021/05/essential-machine-learning-algorithms-beginners.html

• Decision Tree Algorithm, Explained

  All you need to know about decision trees and how to build and optimize decision tree classifier.

  https://www.kdnuggets.com/2020/01/decision-tree-algorithm-explained.html

• 30 Most Asked Machine Learning Questions Answered

  There is always a lot to learn in machine learning. Whether you are new to the field or a seasoned practitioner and ready for a refresher, understanding these key concepts will keep your skills honed in the right direction.

  https://www.kdnuggets.com/2021/08/30-machine-learning-questions-answered.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

• 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

• 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

• MathWorks Deep learning workflow: tips, tricks, and often forgotten steps

  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.

  https://www.kdnuggets.com/2020/09/mathworks-deep-learning-workflow.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

• 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

• Announcing PyCaret 1.0.0

  An open source low-code machine learning library in Python. 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.

  https://www.kdnuggets.com/2020/04/announcing-pycaret.html

• Exploring TensorFlow Quantum, Google’s New Framework for Creating Quantum Machine Learning Models

  TensorFlow Quantum allow data scientists to build machine learning models that work on quantum architectures.

  https://www.kdnuggets.com/2020/03/tensorflow-quantum-framework-quantum-machine-learning-models.html

• A Top Machine Learning Algorithm Explained: Support Vector Machines (SVM)">A Top Machine Learning Algorithm Explained: Support Vector Machines (SVM)

  Support Vector Machines (SVMs) are powerful for solving regression and classification problems. You should have this approach in your machine learning arsenal, and this article provides all the mathematics you need to know -- it's not as hard you might think.

  https://www.kdnuggets.com/2020/03/machine-learning-algorithm-svm-explained.html

• Generate Realistic Human Face using GAN

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

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

• Data Science Curriculum for self-study

  Are you asking the question, "how do I become a Data Scientist?" This list recommends the best essential topics to gain an introductory understanding for getting started in the field. After learning these basics, keep in mind that doing real data science projects through internships or competitions is crucial to acquiring the core skills necessary for the job.

  https://www.kdnuggets.com/2020/02/data-science-curriculum-self-study.html

• Exoplanet Hunting Using Machine Learning

  Search for exoplanets — those planets beyond our own solar system — using machine learning, and implement these searches in Python.

  https://www.kdnuggets.com/2020/01/exoplanet-hunting-machine-learning.html

• Fighting Overfitting in Deep Learning

  This post outlines an attack plan for fighting overfitting in neural networks.

  https://www.kdnuggets.com/2019/12/fighting-overfitting-deep-learning.html

• The Ultimate Guide to Model Retraining

  Once you have deployed your machine learning model into production, differences in real-world data will result in model drift. So, retraining and redeploying will likely be required. In other words, deployment should be treated as a continuous process. This guide defines model drift and how to identify it, and includes approaches to enable model training.

  https://www.kdnuggets.com/2019/12/ultimate-guide-model-retraining.html

• 5 Techniques to Prevent Overfitting in Neural Networks

  In this article, I will present five techniques to prevent overfitting while training neural networks.

  https://www.kdnuggets.com/2019/12/5-techniques-prevent-overfitting-neural-networks.html

• Enabling the Deep Learning Revolution

  Deep learning models are revolutionizing the business and technology world with jaw-dropping performances in one application area after another. Read this post on some of the numerous composite technologies which allow deep learning its complex nonlinearity.

  https://www.kdnuggets.com/2019/12/enabling-deep-learning-revolution.html

• Explainability: Cracking open the black box, Part 1

  What is Explainability in AI and how can we leverage different techniques to open the black box of AI and peek inside? This practical guide offers a review and critique of the various techniques of interpretability.

  https://www.kdnuggets.com/2019/12/explainability-black-box-part1.html

• Designing Your Neural Networks

  Check out this step-by-step walk through of some of the more confusing aspects of neural nets to guide you to making smart decisions about your neural network architecture.

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

• Build an Artificial Neural Network From Scratch: Part 1

  This article focused on building an Artificial Neural Network using the Numpy Python library.

  https://www.kdnuggets.com/2019/11/build-artificial-neural-network-scratch-part-1.html

• Intro to Adversarial Machine Learning and Generative Adversarial Networks

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

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

• Writing Your First Neural Net in Less Than 30 Lines of Code with Keras

  Read this quick overview of neural networks and learn how to implement your first in very few lines using Keras.

  https://www.kdnuggets.com/2019/10/writing-first-neural-net-less-30-lines-code-keras.html

• Introduction to Artificial Neural Networks

  In this article, we’ll try to cover everything related to Artificial Neural Networks or ANN.

  https://www.kdnuggets.com/2019/10/introduction-artificial-neural-networks.html

• There is No Free Lunch in Data Science">There is No Free Lunch in Data Science

  There is no such thing as a free lunch in life or data science. Here, we'll explore some science philosophy and discuss the No Free Lunch theorems to find out what they mean for the field of data science.

  https://www.kdnuggets.com/2019/09/no-free-lunch-data-science.html

• Nothing but NumPy: Understanding & Creating Neural Networks with Computational Graphs from Scratch">Nothing but NumPy: Understanding & Creating Neural Networks with Computational Graphs from Scratch

  Entirely implemented with NumPy, this extensive tutorial provides a detailed review of neural networks followed by guided code for creating one from scratch with computational graphs.

  https://www.kdnuggets.com/2019/08/numpy-neural-networks-computational-graphs.html

• Why Machine Learning is vulnerable to adversarial attacks and how to fix it

  Machine learning can process data imperceptible to humans to produce expected results. These inconceivable patterns are inherent in the data but may make models vulnerable to adversarial attacks. How can developers harness these features to not lose control of AI?

  https://www.kdnuggets.com/2019/06/machine-learning-adversarial-attacks.html

• How Optimization Works

  Optimization problems are naturally described in terms of costs - money, time, resources - rather than benefits. In math it's convenient to make all your problems look the same before you work out a solution, so that you can just solve it the one time.

  https://www.kdnuggets.com/2019/04/how-optimization-works.html

• How can quantum computing be useful for Machine Learning

  We investigate where quantum computing and machine learning could intersect, providing plenty of use cases, examples and technical analysis.

  https://www.kdnuggets.com/2019/04/quantum-computing-machine-learning.html

• Understanding Gradient Boosting Machines">Understanding Gradient Boosting Machines

  However despite its massive popularity, many professionals still use this algorithm as a black box. As such, the purpose of this article is to lay an intuitive framework for this powerful machine learning technique.

  https://www.kdnuggets.com/2019/02/understanding-gradient-boosting-machines.html

• Data Scientist’s Dilemma: The Cold Start Problem – Ten Machine Learning Examples

  We present an array of examples showcasing the cold-start problems in data science where the algorithms and techniques of machine learning produce the good judgment in model progression toward the optimal solution.

  https://www.kdnuggets.com/2019/01/data-scientist-dilemma-cold-start-machine-learning.html

• Word Embeddings & Self-Supervised Learning, Explained

  There are many algorithms to learn word embeddings. Here, we consider only one of them: word2vec, and only one version of word2vec called skip-gram, which works well in practice.

  https://www.kdnuggets.com/2019/01/burkov-self-supervised-learning-word-embeddings.html

• The Backpropagation Algorithm Demystified

  A crucial aspect of machine learning is its ability to recognize error margins and to interpret data more precisely as rising numbers of datasets are fed through its neural network. Commonly referred to as backpropagation, it is a process that isn’t as complex as you might think.

  https://www.kdnuggets.com/2019/01/backpropagation-algorithm-demystified.html

• A Guide to Decision Trees for Machine Learning and Data Science">A Guide to Decision Trees for Machine Learning and Data Science

  What makes decision trees special in the realm of ML models is really their clarity of information representation. The “knowledge” learned by a decision tree through training is directly formulated into a hierarchical structure.

  https://www.kdnuggets.com/2018/12/guide-decision-trees-machine-learning-data-science.html

• Word Vectors in Natural Language Processing: Global Vectors (GloVe)

  A well-known model that learns vectors or words from their co-occurrence information is GlobalVectors (GloVe). While word2vec is a predictive model — a feed-forward neural network that learns vectors to improve the predictive ability, GloVe is a count-based model.

  https://www.kdnuggets.com/2018/08/word-vectors-nlp-glove.html

• An Introduction to t-SNE with Python Example

  In this post we’ll give an introduction to the exploratory and visualization t-SNE algorithm. t-SNE is a powerful dimension reduction and visualization technique used on high dimensional data.

  https://www.kdnuggets.com/2018/08/introduction-t-sne-python.html

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

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

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

• 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

• Getting Started with TensorFlow: A Machine Learning Tutorial

  A complete and rigorous introduction to Tensorflow. Code along with this tutorial to get started with hands-on examples.

  https://www.kdnuggets.com/2017/12/getting-started-tensorflow.html

• Understanding Deep Convolutional Neural Networks with a practical use-case in Tensorflow and Keras">Understanding Deep Convolutional Neural Networks with a practical use-case in Tensorflow and Keras

  We show how to build a deep neural network that classifies images to many categories with an accuracy of a 90%. This was a very hard problem before the rise of deep networks and especially Convolutional Neural Networks.

  https://www.kdnuggets.com/2017/11/understanding-deep-convolutional-neural-networks-tensorflow-keras.html

• Want to know how Deep Learning works? Here’s a quick guide for everyone">Want to know how Deep Learning works? Here’s a quick guide for everyone

  Once you’ve read this article, you will understand the basics of AI and ML. More importantly, you will understand how Deep Learning, the most popular type of ML, works.

  https://www.kdnuggets.com/2017/11/deep-learning-works-quick-guide-everyone.html

• XGBoost: A Concise Technical Overview">XGBoost: A Concise Technical Overview

  Interested in learning the concepts behind XGBoost, rather than just using it as a black box? Or, are you looking for a concise introduction to XGBoost? Then, this article is for you. Includes a Python implementation and links to other basic Python and R codes as well.

  https://www.kdnuggets.com/2017/10/xgboost-concise-technical-overview.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

• Machine Learning Exercises in Python: An Introductory Tutorial Series">Machine Learning Exercises in Python: An Introductory Tutorial Series

  This post presents a summary of a series of tutorials covering the exercises from Andrew Ng's machine learning class on Coursera. Instead of implementing the exercises in Octave, the author has opted to do so in Python, and provide commentary along the way.

  https://www.kdnuggets.com/2017/07/machine-learning-exercises-python-introductory-tutorial-series.html

• Summary of Unintuitive Properties of Neural Networks

  Neural networks work really well on many problems, including language, image and speech recognition. However understanding how they work is not simple, and here is a summary of unusual and counter intuitive properties they have.

  https://www.kdnuggets.com/2017/07/unintuitive-properties-neural-networks.html

• Machine Learning Crash Course: Part 1

  This post, the first in a series of ML tutorials, aims to make machine learning accessible to anyone willing to learn. We’ve designed it to give you a solid understanding of how ML algorithms work as well as provide you the knowledge to harness it in your projects.

  https://www.kdnuggets.com/2017/05/machine-learning-crash-course-part-1.html

• Must-Know: How to evaluate a binary classifier

  Binary classification is a basic concept which involves classifying the data into two groups. Read on for some additional insight and approaches.

  https://www.kdnuggets.com/2017/04/must-know-evaluate-binary-classifier.html

• Building Regression Models in R using Support Vector Regression

  The article studies the advantage of Support Vector Regression (SVR) over Simple Linear Regression (SLR) models for predicting real values, using the same basic idea as Support Vector Machines (SVM) use for classification.

  https://www.kdnuggets.com/2017/03/building-regression-models-support-vector-regression.html

• 17 More Must-Know Data Science Interview Questions and Answers">17 More Must-Know Data Science Interview Questions and Answers

  17 new must-know Data Science Interview questions and answers include lessons from failure to predict 2016 US Presidential election and Super Bowl LI comeback, understanding bias and variance, why fewer predictors might be better, and how to make a model more robust to outliers.
   
   

  https://www.kdnuggets.com/2017/02/17-data-science-interview-questions-answers.html

• Deep Learning Research Review: Natural Language Processing">Deep Learning Research Review: Natural Language Processing

  This edition of Deep Learning Research Review explains recent research papers in Natural Language Processing (NLP). If you don't have the time to read the top papers yourself, or need an overview of NLP with Deep Learning, this post is for you.

  https://www.kdnuggets.com/2017/01/deep-learning-review-natural-language-processing.html

• Implementing a CNN for Human Activity Recognition in Tensorflow

  In this post, we will see how to employ Convolutional Neural Network (CNN) for HAR, that will learn complex features automatically from the raw accelerometer signal to differentiate between different activities of daily life.

  https://www.kdnuggets.com/2016/11/implementing-cnn-human-activity-recognition-tensorflow.html

• Artificial Intelligence, Deep Learning, and Neural Networks, Explained">Artificial Intelligence, Deep Learning, and Neural Networks, Explained

  This article is meant to explain the concepts of AI, deep learning, and neural networks at a level that can be understood by most non-practitioners, and can also serve as a reference or review for technical folks as well.

  https://www.kdnuggets.com/2016/10/artificial-intelligence-deep-learning-neural-networks-explained.html

• 9 Key Deep Learning Papers, Explained">9 Key Deep Learning Papers, Explained

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  https://www.kdnuggets.com/2016/09/9-key-deep-learning-papers-explained.html

• Urban Sound Classification with Neural Networks in Tensorflow

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  https://www.kdnuggets.com/2016/09/urban-sound-classification-neural-networks-tensorflow.html

• The Gentlest Introduction to Tensorflow – Part 2

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  https://www.kdnuggets.com/2016/08/gentlest-introduction-tensorflow-part-2.html

• 5 Deep Learning Projects You Can No Longer Overlook

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  https://www.kdnuggets.com/2016/07/five-deep-learning-projects-cant-overlook.html

• What is Softmax Regression and How is it Related to Logistic Regression?

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  https://www.kdnuggets.com/2016/07/softmax-regression-related-logistic-regression.html

• Regularization in Logistic Regression: Better Fit and Better Generalization?

  A discussion on regularization in logistic regression, and how its usage plays into better model fit and generalization.

  https://www.kdnuggets.com/2016/06/regularization-logistic-regression.html

• A Visual Explanation of the Back Propagation Algorithm for Neural Networks

  A concise explanation of backpropagation for neural networks is presented in elementary terms, along with explanatory visualization.

  https://www.kdnuggets.com/2016/06/visual-explanation-backpropagation-algorithm-neural-networks.html

• A Concise Overview of Standard Model-fitting Methods

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

• Troubleshooting Neural Networks: What is Wrong When My Error Increases?

  An overview of some of the things that could lead to an increased error rate in neural network implementations.

  https://www.kdnuggets.com/2016/05/troubleshooting-neural-network-error-increase.html

• How do Neural Networks Learn?

  Neural networks are generating a lot of excitement, while simultaneously posing challenges to people trying to understand how they work. Visualize how neural nets work from the experience of implementing a real world project.

  https://www.kdnuggets.com/2015/12/how-do-neural-networks-learn.html

• Deep Learning for Visual Question Answering

  Here we discuss about the Visual Question Answering problem, and I’ll also present neural network based approaches for same.

  https://www.kdnuggets.com/2015/11/deep-learning-visual-question-answering.html

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  https://www.kdnuggets.com/2014/01/index-old.html

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  https://www.kdnuggets.com/the-psychology-of-data-visualization-how-to-present-data-that-persuades

• 7 Steps to Mastering Large Language Model Fine-tuning

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  https://www.kdnuggets.com/7-steps-to-mastering-large-language-model-fine-tuning

• Getting Started with LLMOps: The Secret Sauce Behind Seamless Interactions

  Check out this beginner’s guide to understanding the role of Large Language Model Operations for seamless user experiences.

  https://www.kdnuggets.com/getting-started-with-llmops-the-secret-sauce-behind-seamless-interactions

• Getting Started With Claude 3 Opus That Just Destroyed GPT-4 and Gemini

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  https://www.kdnuggets.com/getting-started-with-claude-3-opus-that-just-destroyed-gpt-4-and-gemini

• 8 Built-in Python Decorators to Write Elegant Code

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  https://www.kdnuggets.com/8-built-in-python-decorators-to-write-elegant-code

• Navigating the Data Revolution: Exploring the Booming Trends in Data Science and Machine Learning

  Dive into transformative trends in data science, encompassing AI-powered automation, NLP, ethical considerations, decentralized computing, and interdisciplinary collaboration.

  https://www.kdnuggets.com/navigating-the-data-revolution-exploring-the-booming-trends-in-data-science-and-machine-learning

• A Roadmap For Your Data Career

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  https://www.kdnuggets.com/a-roadmap-for-your-data-career

• Top 5 AI Coding Assistants You Must Try

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  https://www.kdnuggets.com/top-5-ai-coding-assistants-you-must-try

• Data Maturity: The Cornerstone of AI-Enabled Innovation

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  https://www.kdnuggets.com/data-maturity-the-cornerstone-of-ai-enabled-innovation

• The Only Free Course You Need To Become a Professional Data Engineer

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  https://www.kdnuggets.com/the-only-free-course-you-need-to-become-a-professional-data-engineer

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  https://www.kdnuggets.com/the-top-8-cloud-container-management-solutions-of-2024

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  https://www.kdnuggets.com/running-mixtral-8x7b-on-google-colab-for-free

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  https://www.kdnuggets.com/enroll-in-a-4-year-computer-science-degree-program-for-free

• Evaluating Methods for Calculating Document Similarity

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  https://www.kdnuggets.com/evaluating-methods-for-calculating-document-similarity

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  https://www.kdnuggets.com/ai-automated-cybersecurity-what-to-automate

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

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

• Evolution in ETL: How Skipping Transformation Enhances Data Management

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  https://www.kdnuggets.com/evolution-in-etl-how-skipping-transformation-enhances-data-management

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  https://www.kdnuggets.com/7-reasons-why-you-shouldnt-become-a-data-scientist

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

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  https://www.kdnuggets.com/how-big-data-is-saving-lives-in-real-time-iov-data-analytics-helps-prevent-accidents

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  https://www.kdnuggets.com/7-essential-data-quality-checks-with-pandas

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  https://www.kdnuggets.com/an-honest-comparison-of-open-source-vector-databases

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  https://www.kdnuggets.com/novice-to-ninja-why-your-python-skills-matter-in-data-science