Search results for multilayer perceptron

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• 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

• 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 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• Popular Deep Learning Tools – a review

  Deep Learning is the hottest trend now in AI and Machine Learning. We review the popular software for Deep Learning, including Caffe, Cuda-convnet, Deeplearning4j, Pylearn2, Theano, and Torch.

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

• 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

• 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

• 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

• 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

• 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