Search results for perceptron

Found 104 documents, 5928 searched:

• Machine Learning Is Not Like Your Brain Part Two: Perceptrons vs Neurons

  An ML system requiring thousands of tagged samples is fundamentally different from the mind of a child, which can learn from just a few experiences of untagged data.

  https://www.kdnuggets.com/2022/05/machine-learning-like-brain-part-two-perceptrons-neurons.html

• 6 Steps To Write Any Machine Learning Algorithm From Scratch: Perceptron Case Study">6 Steps To Write Any Machine Learning Algorithm From Scratch: Perceptron Case Study

  Writing a machine learning algorithm from scratch is an extremely rewarding learning experience. We highlight 6 steps in this process.

  https://www.kdnuggets.com/2018/09/6-steps-write-machine-learning-algorithm.html

• 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

• 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

• 3 Data Science Projects Guaranteed to Land You That Job

  Imagine you’re allowed to do only three data science projects. Which should you choose to guarantee you get the job? Here’s my choice!

  https://www.kdnuggets.com/3-data-science-projects-guaranteed-to-land-you-that-job

• 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

• GPT-4 Details Have Been Leaked!

  What has OpenAI been keeping in the woodwork about GPT-4?

  https://www.kdnuggets.com/2023/07/gpt4-details-leaked.html

• 5 Free Books on Natural Language Processing to Read in 2023

  Large language models are getting released left right and center, and if you want to understand them better you need to know about NLP. Here are 5 Free books to help you.

  https://www.kdnuggets.com/2023/06/5-free-books-natural-language-processing-read-2023.html

• A Comprehensive Guide to Convolutional Neural Networks

  Artificial Intelligence has been witnessing monumental growth in bridging the gap between the capabilities of humans and machines. Researchers and enthusiasts alike, work on numerous aspects of the field to make amazing things happen. One of many such areas is the domain of Computer Vision.

  https://www.kdnuggets.com/2023/06/comprehensive-guide-convolutional-neural-networks.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

• 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

• 15 More Free Machine Learning and Deep Learning Books

  Check out this second list of 15 FREE ebooks for learning machine learning and deep learning.

  https://www.kdnuggets.com/2022/11/15-free-machine-learning-deep-learning-books.html

• Everything You’ve Ever Wanted to Know About Machine Learning

  Putting the fun in fundamentals! A collection of short videos to amuse beginners and experts alike.

  https://www.kdnuggets.com/2022/09/everything-youve-ever-wanted-to-know-about-machine-learning.html

• Machine Learning Is Not Like Your Brain Part 6: The Importance of Precise Synapse Weights and the Ability to Set Them Quickly

  In Part Six, I’ll show how limitations in synapses are even more of a problem. Precise synapse weights and the ability to set them quickly to a specific value are crucial to ML and biological neurons offer neither.

  https://www.kdnuggets.com/2022/08/machine-learning-like-brain-part-6-importance-precise-synapse-weights-ability-set-quickly.html

• Machine Learning Is Not Like Your Brain Part 5: Biological Neurons Can’t Do Summation of Inputs

  See why biological neurons can’t do the most fundamental process of the artificial perceptron, the summation of inputs.

  https://www.kdnuggets.com/2022/07/machine-learning-like-brain-part-5-biological-neurons-cant-summation-inputs.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

• 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

• KDnuggets News, May 25: The 6 Python Machine Learning Tools Every Data Scientist Should Know About; The Complete Collection of Data Science Books

  The 6 Python Machine Learning Tools Every Data Scientist Should Know About; The Complete Collection of Data Science Books - Part 1; Finding the Best IDE Software; 5 Ways to Double Your Income with Data Science; Operationalizing Machine Learning from PoC to Production

  https://www.kdnuggets.com/2022/n21.html

• Machine Learning Is Not Like Your Brain Part One: Neurons Are Slow, Slow, Slow

  Artificial intelligence is not all that intelligent. While today’s AI can do some extraordinary things, the functionality underlying its accomplishments has very little to do with the way in which a human brain works to achieve the same tasks.

  https://www.kdnuggets.com/2022/04/machine-learning-like-brain-part-one-neurons-slow-slow-slow.html

• How Long Does It Take to Learn Data Science Fundamentals?

  This article discusses 2 levels of data science learning, and the amount of time that will need to go into each. From 6 months to 4 years, this write-up covers a number of skills and how long it takes to acquire them.

  https://www.kdnuggets.com/2022/03/long-take-learn-data-science-fundamentals.html

• AutoML: An Introduction Using Auto-Sklearn and Auto-PyTorch

  AutoML is a broad category of techniques and tools for applying automated search to your automated search and learning to your learning. In addition to Auto-Sklearn, the Freiburg-Hannover AutoML group has also developed an Auto-PyTorch library. We’ll use both of these as our entry point into AutoML in the following simple tutorial.

  https://www.kdnuggets.com/2021/10/automl-introduction-auto-sklearn-auto-pytorch.html

• Teaching AI to Classify Time-series Patterns with Synthetic Data">Teaching AI to Classify Time-series Patterns with Synthetic Data

  How to build and train an AI model to identify various common anomaly patterns in time-series data.

  https://www.kdnuggets.com/2021/10/teaching-ai-classify-time-series-patterns-synthetic-data.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

• Geometric foundations of Deep Learning">Geometric foundations of Deep Learning

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

  https://www.kdnuggets.com/2021/07/geometric-foundations-deep-learning.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

• Essential Linear Algebra for Data Science and Machine Learning">Essential Linear Algebra for Data Science and Machine Learning

  Linear algebra is foundational in data science and machine learning. Beginners starting out along their learning journey in data science--as well as established practitioners--must develop a strong familiarity with the essential concepts in linear algebra.

  https://www.kdnuggets.com/2021/05/essential-linear-algebra-data-science-machine-learning.html

• Deep Learning Recommendation Models (DLRM): A Deep Dive

  The currency in the 21st century is no longer just data. It's the attention of people. This deep dive article presents the architecture and deployment issues experienced with the deep learning recommendation model, DLRM, which was open-sourced by Facebook in March 2019.

  https://www.kdnuggets.com/2021/04/deep-learning-recommendation-models-dlrm-deep-dive.html

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

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

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

• 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

• Essential Data Science Tips: How to Use One-Vs-Rest and One-Vs-One for Multi-Class Classification

  Classification, as a predictive model, involves aligning each class label to examples. Algorithms designed for binary classification cannot be applied to multi-class classification problems. For such situations, heuristic methods come in handy.

  https://www.kdnuggets.com/2020/08/one-vs-rest-one-multi-class-classification.html

• Deep Learning in Finance: Is This The Future of the Financial Industry?

  Get a handle on how deep learning is affecting the finance industry, and identify resources to further this understanding and increase your knowledge of the various aspects.

  https://www.kdnuggets.com/2020/07/deep-learning-finance-future-financial-industry.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

• 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

• Interactive Machine Learning Experiments

  Dive into experimenting with machine learning techniques using this open-source collection of interactive demos built on multilayer perceptrons, convolutional neural networks, and recurrent neural networks. Each package consists of ready-to-try web browser interfaces and fully-developed notebooks for you to fine tune the training for better performance.

  https://www.kdnuggets.com/2020/05/interactive-machine-learning-experiments.html

• Complex logic at breakneck speed: Try Julia for data science

  We show a comparative performance benchmarking of Julia with an equivalent Python code to show why Julia is great for data science and machine learning.

  https://www.kdnuggets.com/2020/05/complex-logic-breakneck-speed-julia-data-science.html

• Evidence Counterfactuals for explaining predictive models on Big Data

  Big Data generated by people -- such as, social media posts, mobile phone GPS locations, and browsing history -- provide enormous prediction value for AI systems. However, explaining how these models predict with the data remains challenging. This interesting explanation approach considers how a model would behave if it didn't have the original set of data to work with.

  https://www.kdnuggets.com/2020/05/evidence-counterfactuals-predictive-models-big-data.html

• Dive Into Deep Learning: The Free eBook

  This freely available text on deep learning is fully interactive and incredibly thorough. Check out "Dive Into Deep Learning" now and increase your neural networks theoretical understanding and practical implementation skills.

  https://www.kdnuggets.com/2020/04/dive-deep-learning-book.html

• 3 Reasons to Use Random Forest® Over a Neural Network: Comparing Machine Learning versus Deep Learning

  Both the random forest algorithm and Neural Networks are different techniques that learn differently but can be used in similar domains. Why would you use one over the other?

  https://www.kdnuggets.com/2020/04/3-reasons-random-forest-neural-network-comparison.html

• Build an Artificial Neural Network From Scratch: Part 2

  The second article in this series focuses on building an Artificial Neural Network using the Numpy Python library.

  https://www.kdnuggets.com/2020/03/build-artificial-neural-network-scratch-part-2.html

• The ravages of concept drift in stream learning applications and how to deal with it

  Stream data processing has gained progressive momentum with the arriving of new stream applications and big data scenarios. These streams of data evolve generally over time and may be occasionally affected by a change (concept drift). How to handle this change by using detection and adaptation mechanisms is crucial in many real-world systems.

  https://www.kdnuggets.com/2019/12/ravages-concept-drift-stream-learning-applications.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

• Data Science Curriculum Roadmap">Data Science Curriculum Roadmap

  What follows is a set of broad recommendations, and it will inevitably require a lot of adjustments in each implementation. Given that caveat, here are our curriculum recommendations.

  https://www.kdnuggets.com/2019/12/data-science-curriculum-roadmap.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

• Beyond Word Embedding: Key Ideas in Document Embedding

  This literature review on document embedding techniques thoroughly covers the many ways practitioners develop rich vector representations of text -- from single sentences to entire books.

  https://www.kdnuggets.com/2019/10/beyond-word-embedding-document-embedding.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

• A 2019 Guide for Automatic Speech Recognition

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

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

• Order Matters: Alibaba’s Transformer-based Recommender System

  Alibaba, the largest e-commerce platform in China, is a powerhouse not only when it comes to e-commerce, but also when it comes to recommender systems research. Their latest paper, Behaviour Sequence Transformer for E-commerce Recommendation in Alibaba, is yet another publication that pushes the state of the art in recommender systems.

  https://www.kdnuggets.com/2019/08/order-matters-alibabas-transformer-based-recommender-system.html

• Knowing Your Neighbours: Machine Learning on Graphs">Knowing Your Neighbours: Machine Learning on Graphs

  Graph Machine Learning uses the network structure of the underlying data to improve predictive outcomes. Learn how to use this modern machine learning method to solve challenges with connected data.

  https://www.kdnuggets.com/2019/08/neighbours-machine-learning-graphs.html

• Deep Learning for NLP: ANNs, RNNs and LSTMs explained!">Deep Learning for NLP: ANNs, RNNs and LSTMs explained!

  Learn about Artificial Neural Networks, Deep Learning, Recurrent Neural Networks and LSTMs like never before and use NLP to build a Chatbot!

  https://www.kdnuggets.com/2019/08/deep-learning-nlp-explained.html

• Convolutional Neural Networks: A Python Tutorial Using TensorFlow and Keras">Convolutional Neural Networks: A Python Tutorial Using TensorFlow and Keras

  Different neural network architectures excel in different tasks. This particular article focuses on crafting convolutional neural networks in Python using TensorFlow and Keras.

  https://www.kdnuggets.com/2019/07/convolutional-neural-networks-python-tutorial-tensorflow-keras.html

• Training a Neural Network to Write Like Lovecraft">Training a Neural Network to Write Like Lovecraft

  In this post, the author attempts to train a neural network to generate Lovecraft-esque prose, known to be awkward and irregular at best. Did it end in success? If not, any suggestions on how it might have? Read on to find out.

  https://www.kdnuggets.com/2019/07/training-neural-network-write-like-lovecraft.html

• Another 10 Free Must-See Courses for Machine Learning and Data Science">Another 10 Free Must-See Courses for Machine Learning and Data Science

  Check out another follow-up collection of free machine learning and data science courses to give you some spring study ideas.

  https://www.kdnuggets.com/2019/04/another-10-free-must-see-courses-machine-learning-data-science.html

• Deep Compression: Optimization Techniques for Inference & Efficiency

  We explain deep compression for improved inference efficiency, mobile applications, and regularization as technology cozies up to the physical limits of Moore's law.

  https://www.kdnuggets.com/2019/03/deep-compression-optimization-techniques-inference-efficiency.html

• Neural Networks with Numpy for Absolute Beginners: Introduction

  In this tutorial, you will get a brief understanding of what Neural Networks are and how they have been developed. In the end, you will gain a brief intuition as to how the network learns.

  https://www.kdnuggets.com/2019/03/neural-networks-numpy-absolute-beginners-introduction.html

• Neural Networks – an Intuition

  Neural networks are one of the most powerful algorithms used in the field of machine learning and artificial intelligence. We attempt to outline its similarities with the human brain and how intuition plays a big part in this.

  https://www.kdnuggets.com/2019/02/neural-networks-intuition.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

• Introduction to Named Entity Recognition

  Named Entity Recognition is a tool which invariably comes handy when we do Natural Language Processing tasks. Read on to find out how.

  https://www.kdnuggets.com/2018/12/introduction-named-entity-recognition.html

• Named Entity Recognition and Classification with Scikit-Learn">Named Entity Recognition and Classification with Scikit-Learn

  Named Entity Recognition and Classification is a process of recognizing information units like names, including person, organization and location names, and numeric expressions from unstructured text. The goal is to develop practical and domain-independent techniques in order to detect named entities with high accuracy automatically.

  https://www.kdnuggets.com/2018/10/named-entity-recognition-classification-scikit-learn.html

• Sequence Modeling with Neural Networks – Part I

  In the context of this post, we will focus on modeling sequences as a well-known data structure and will study its specific learning framework.

  https://www.kdnuggets.com/2018/10/sequence-modeling-neural-networks-part-1.html

• Top Stories, Sep 24-30: Machine Learning Cheat Sheets; Learning the Mathematics of Machine Learning

  Also: Math for Machine Learning; Introducing Path Analysis Using R; Introduction to Deep Learning; Essential Math for Data Science: Why and How; 6 Steps To Write Any Machine Learning Algorithm From Scratch: Perceptron Case Study

  https://www.kdnuggets.com/2018/10/top-news-week-0924-0930.html

• Introduction to Deep Learning

  I decided to begin to put some structure in my understanding of Neural Networks through this series of articles.

  https://www.kdnuggets.com/2018/09/introduction-deep-learning.html

• Beginners Ask “How Many Hidden Layers/Neurons to Use in Artificial Neural Networks?”">Beginners Ask “How Many Hidden Layers/Neurons to Use in Artificial Neural Networks?”

  By the end of this article, you could at least get the idea of how these questions are answered and be able to test yourself based on simple examples.

  https://www.kdnuggets.com/2018/07/beginners-ask-how-many-hidden-layers-neurons-neural-networks.html

• Ten Machine Learning Algorithms You Should Know to Become a Data Scientist">Ten Machine Learning Algorithms You Should Know to Become a Data Scientist

  It's important for data scientists to have a broad range of knowledge, keeping themselves updated with the latest trends. With that being said, we take a look at the top 10 machine learning algorithms every data scientist should know.

  https://www.kdnuggets.com/2018/04/10-machine-learning-algorithms-data-scientist.html

• Where AI is already rivaling humans

  Since 2011, AI hit hypergrowth, and researchers have created several AI solutions that are almost as good as – or better than – humans in several domains, including games, healthcare, computer vision and object recognition, speech to text conversion, speaker recognition, and improved robots and chat-bots for solving specific problems.

  https://www.kdnuggets.com/2018/02/domains-ai-rivaling-humans.html

• Resurgence of AI During 1983-2010

  We discuss supervised learning, unsupervised learning and reinforcement learning, neural networks, and 6 reasons that helped AI Research and Development to move ahead.

  https://www.kdnuggets.com/2018/02/resurgence-ai-1983-2010.html

• The Birth of AI and The First AI Hype Cycle

  A dazzling review of AI History, from Alan Turing and Turing Test, to Simon and Newell and Logic Theorist, to Marvin Minsky and Perceptron, birth of Rule-based systems and Machine Learning, Eliza - first chatbot, Robotics, and the bust which led to first AI Winter.

  https://www.kdnuggets.com/2018/02/birth-ai-first-hype-cycle.html

• The 8 Neural Network Architectures Machine Learning Researchers Need to Learn">The 8 Neural Network Architectures Machine Learning Researchers Need to Learn

  In this blog post, I want to share the 8 neural network architectures from the course that I believe any machine learning researchers should be familiar with to advance their work.

  https://www.kdnuggets.com/2018/02/8-neural-network-architectures-machine-learning-researchers-need-learn.html

• Deep Learning in H2O using R

  This article is about implementing Deep Learning (DL) using the H2O package in R. We start with a background on DL, followed by some features of H2O's DL framework, followed by an implementation using R.

  https://www.kdnuggets.com/2018/01/deep-learning-h2o-using-r.html

• Is Learning Rate Useful in Artificial Neural Networks?

  This article will help you understand why we need the learning rate and whether it is useful or not for training an artificial neural network. Using a very simple Python code for a single layer perceptron, the learning rate value will get changed to catch its idea.

  https://www.kdnuggets.com/2018/01/learning-rate-useful-neural-network.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

• 7 Types of Artificial Neural Networks for Natural Language Processing">7 Types of Artificial Neural Networks for Natural Language Processing

  What is an artificial neural network? How does it work? What types of artificial neural networks exist? How are different types of artificial neural networks used in natural language processing? We will discuss all these questions in the following article.

  https://www.kdnuggets.com/2017/10/7-types-artificial-neural-networks-natural-language-processing.html

• New-Age Machine Learning Algorithms in Retail Lending">New-Age Machine Learning Algorithms in Retail Lending

  We review the application of new age Machine Learning algorithms for better Customer Analytics in Lending and Credit Risk Assessment.

  https://www.kdnuggets.com/2017/09/machine-learning-algorithms-lending.html

• Introduction to Neural Networks, Advantages and Applications">Introduction to Neural Networks, Advantages and Applications

  Artificial Neural Network (ANN) algorithm mimic the human brain to process information. Here we explain how human brain and ANN works.

  https://www.kdnuggets.com/2017/07/introduction-neural-networks-advantages-applications.html

• Deep Learning – Past, Present, and Future">Deep Learning – Past, Present, and Future

  There is a lot of buzz around deep learning technology. First developed in the 1940s, deep learning was meant to simulate neural networks found in brains, but in the last decade 3 key developments have unleashed its potential.

  https://www.kdnuggets.com/2017/05/deep-learning-big-deal.html

• 7 More Steps to Mastering Machine Learning With Python">7 More Steps to Mastering Machine Learning With Python

  This post is a follow-up to last year's introductory Python machine learning post, which includes a series of tutorials for extending your knowledge beyond the original.
   
   

  https://www.kdnuggets.com/2017/03/seven-more-steps-machine-learning-python.html

• What I Learned Implementing a Classifier from Scratch in Python

  In this post, the author implements a machine learning algorithm from scratch, without the use of a library such as scikit-learn, and instead writes all of the code in order to have a working binary classifier algorithm.

  https://www.kdnuggets.com/2017/02/learned-implementing-classifier-scratch-python.html

• Why Deep Learning is Radically Different From Machine Learning">Why Deep Learning is Radically Different From Machine Learning

  There is a lot of confusion these days about Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL), yet the distinction is very clear to practitioners in these fields. Are you able to articulate the difference?

  https://www.kdnuggets.com/2016/12/deep-learning-radically-different-machine-learning.html

• The hard thing about deep learning">The hard thing about deep learning

  It’s easy to optimize simple neural networks, let’s say single layer perceptron. But, as network becomes deeper, the optmization problem becomes crucial. This article discusses about such optimization problems with deep neural networks.

  https://www.kdnuggets.com/2016/12/hard-thing-about-deep-learning.html

• An Intuitive Explanation of Convolutional Neural Networks

  This article provides a easy to understand introduction to what convolutional neural networks are and how they work.

  https://www.kdnuggets.com/2016/11/intuitive-explanation-convolutional-neural-networks.html

• A Quick Introduction to Neural Networks

  This article provides a beginner level introduction to multilayer perceptron and backpropagation.

  https://www.kdnuggets.com/2016/11/quick-introduction-neural-networks.html

• A Beginner’s Guide to Neural Networks with Python and SciKit Learn 0.18!">A Beginner’s Guide to Neural Networks with Python and SciKit Learn 0.18!

  This post outlines setting up a neural network in Python using Scikit-learn, the latest version of which now has built in support for Neural Network models.

  https://www.kdnuggets.com/2016/10/beginners-guide-neural-networks-python-scikit-learn.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

• What is the Role of the Activation Function in a Neural Network?

  Confused as to exactly what the activation function in a neural network does? Read this overview, and check out the handy cheat sheet at the end.

  https://www.kdnuggets.com/2016/08/role-activation-function-neural-network.html

• A Beginner’s Guide to Neural Networks with R!

  In this article we will learn how Neural Networks work and how to implement them with the R programming language! We will see how we can easily create Neural Networks with R and even visualize them. Basic understanding of R is necessary to understand this article.

  https://www.kdnuggets.com/2016/08/begineers-guide-neural-networks-r.html

• The Core of Data Science

  This post provides a simplifying framework, an ontology for Machine Learning and some important developments in dynamical machine learning. From first hand Data Science product experience, the author suggests how best to execute Data Science projects.

  https://www.kdnuggets.com/2016/08/core-data-science.html

• 5 Deep Learning Projects You Can No Longer Overlook

  There are a number of "mainstream" deep learning projects out there, but many more niche projects flying under the radar. Have a look at 5 such projects worth checking out.

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

• Deep Residual Networks for Image Classification with Python + NumPy

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  https://www.kdnuggets.com/2016/07/deep-residual-neworks-image-classification-python-numpy.html

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  The story starts in Paris in 1989, when I benchmarked neural networks against kernel methods, but the real invention of SVMs happened when Bernhard decided to implement Vladimir Vapnik algorithm.

  https://www.kdnuggets.com/2016/07/guyon-data-mining-history-svm-support-vector-machines.html

• Top Machine Learning Libraries for Javascript

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  https://www.kdnuggets.com/2016/06/top-machine-learning-libraries-javascript.html

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

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

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  https://www.kdnuggets.com/2016/06/difference-between-deep-learning-regular-machine-learning.html

• 5 Machine Learning Projects You Can No Longer Overlook

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  https://www.kdnuggets.com/2016/05/five-machine-learning-projects-cant-overlook.html

• Deep Learning for Visual Question Answering

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

• Popular Deep Learning Tools – a review

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  https://www.kdnuggets.com/2015/06/popular-deep-learning-tools.html

• Top 20 Python Machine Learning Open Source Projects

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  https://www.kdnuggets.com/2015/06/top-20-python-machine-learning-open-source-projects.html

• Will Deep Learning take over Machine Learning, make other algorithms obsolete?

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  https://www.kdnuggets.com/2014/10/deep-learning-make-machine-learning-algorithms-obsolete.html

• Deep Learning – important resources for learning and understanding

  New and fundamental resources for learning about Deep Learning - the hottest machine learning method, which is approaching human performance level.

  https://www.kdnuggets.com/2014/08/deep-learning-important-resources-learning-understanding.html

• KDnuggets Exclusive: Part 2 of the Interview with Yann LeCun

  We discuss how far AI is likely to go, how Data Science to Statistics is like Computer Science was to Math, Big Data hype and reality, and advice to beginning Data Scientists.

  https://www.kdnuggets.com/2014/02/exclusive-yann-lecun-deep-learning-facebook-ai-lab-part2.html

• Estimation and Forecasting Software

  commercial: | free 11Ants Model Builder, upgrades Microsoft Excel into a powerful, simple to use data mining / predictive analytics tool, with regression, classification and Read more »

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

• Neural Network Software for Classification

  Neural Network Sites Neural Network FAQ list, includes free and commercial software, maintained by Warren Sarle of SAS. Portal for Forecasting with neural networks, including Read more »

  https://www.kdnuggets.com/software/classification-neural.html

• Mikut Data Mining Tools Big List – Update

  An update of the Excel table describing 325 recent and historical data mining tools is now online (Excel format), 31 of them were added since the last update in November 2012. These new updated tools include new published tools and some well-established tools with a statistical background.

  https://www.kdnuggets.com/2013/09/mikut-data-mining-tools-big-list-update.html