Search results for vanishing gradient

Found 50 documents, 5930 searched:

• Vanishing Gradient Problem: Causes, Consequences, and Solutions

  This blog post aims to describe the vanishing gradient problem and explain how use of the sigmoid function resulted in it.

  https://www.kdnuggets.com/2022/02/vanishing-gradient-problem.html

• 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

• Top KDnuggets Post of 2022: Is Data Science a Dying Career?

  Also: The Complete Collection of Data Science Cheat Sheets • 15 Python Coding Interview Questions You Must Know For Data Science • Free Python for Data Science Course • The High Paying Side Hustles for Data Scientists • Vanishing Gradient Problem, Explained

  https://www.kdnuggets.com/2022/12/top-posts-2022.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

• 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

• KDnuggets News, June 28: 10 ChatGPT Plugins for Data Science Cheat Sheet • The ChatGPT Plugin That Automates Data Analysis

  10 ChatGPT Plugins for Data Science Cheat Sheet • Noteable Plugin: The ChatGPT Plugin That Automates Data Analysis • 3 Ways to Access Claude AI for Free • What are Vector Databases and Why Are They Important for LLMs? • A Data Scientist’s Essential Guide to Exploratory Data Analysis

  https://www.kdnuggets.com/2023/n23.html

• Ten Years of AI in Review

  From image classification to chatbot therapy.

  https://www.kdnuggets.com/2023/06/ten-years-ai-review.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

• KDnuggets™ News 22:n09, Mar 2: Telling a Great Data Story: A Visualization Decision Tree; SQL vs. Object-Relational Mapping (ORM)

  Telling a Great Data Story: A Visualization Decision Tree; What Is the Difference Between SQL and Object-Relational Mapping (ORM)?; Top 7 YouTube Courses on Data Analytics ; How Much Do Data Scientists Make in 2022?; Design Patterns in Machine Learning for MLOps

  https://www.kdnuggets.com/2022/n09.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

• 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

• 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

• 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

• 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

• PyTorch LSTM: Text Generation Tutorial

  Key element of LSTM is the ability to work with sequences and its gating mechanism.

  https://www.kdnuggets.com/2020/07/pytorch-lstm-text-generation-tutorial.html

• The Unreasonable Progress of Deep Neural Networks in Natural Language Processing (NLP)

  Natural language processing has made incredible advances through advanced techniques in deep learning. Learn about these powerful models, and find how close (or far away) these approaches are to human-level understanding.

  https://www.kdnuggets.com/2020/06/unreasonable-progress-deep-neural-networks-nlp.html

• 3 Reasons Why We Are Far From Achieving Artificial General Intelligence

  How far we are from achieving Artificial General Intelligence? We answer this through the study of three limitations of current machine learning.

  https://www.kdnuggets.com/2020/04/3-reasons-far-from-artificial-general-intelligence.html

• Illustrating the Reformer

  In this post, we will try to dive into the Reformer model and try to understand it with some visual guides.

  https://www.kdnuggets.com/2020/02/illustrating-reformer.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

• 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

• 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

• Secrets to a Successful Data Science Interview

  Are you puzzled as to what to prepare for data science interviews? That you are reading this document is a reflection of your seriousness in being a successful data scientist.

  https://www.kdnuggets.com/2019/07/secrets-data-science-interview.html

• Checklist for Debugging Neural Networks

  Check out these tangible steps you can take to identify and fix issues with training, generalization, and optimization for machine learning models.

  https://www.kdnuggets.com/2019/03/checklist-debugging-neural-networks.html

• An Introduction to AI">An Introduction to AI

  We provide an introduction to AI key terminologies and methodologies, covering both Machine Learning and Deep Learning, with an extensive list including Narrow AI, Super Intelligence, Classic Artificial Intelligence, and more.

  https://www.kdnuggets.com/2018/11/an-introduction-ai.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

• Don’t Use Dropout in Convolutional Networks

  If you are wondering how to implement dropout, here is your answer - including an explanation on when to use dropout, an implementation example with Keras, batch normalization, and more.

  https://www.kdnuggets.com/2018/09/dropout-convolutional-networks.html

• Deep Learning for NLP: An Overview of Recent Trends">Deep Learning for NLP: An Overview of Recent Trends

  A new paper discusses some of the recent trends in deep learning based natural language processing (NLP) systems and applications. The focus is on the review and comparison of models and methods that have achieved state-of-the-art (SOTA) results on various NLP tasks and some of the current best practices for applying deep learning in NLP.

  https://www.kdnuggets.com/2018/09/deep-learning-nlp-overview-recent-trends.html

• Improving the Performance of a Neural Network

  There are many techniques available that could help us achieve that. Follow along to get to know them and to build your own accurate neural network.

  https://www.kdnuggets.com/2018/05/improving-performance-neural-network.html

• Detecting Breast Cancer with Deep Learning

  Breast cancer is the most common invasive cancer in women, and the second main cause of cancer death in women, after lung cancer. In this article I will build a WideResNet based neural network to categorize slide images into two classes, one that contains breast cancer and other that doesn’t using Deep Learning Studio.

  https://www.kdnuggets.com/2018/05/detecting-breast-cancer-deep-learning.html

• Data Science Interview Guide

  Traditionally, Data Science would focus on mathematics, computer science and domain expertise. While I will briefly cover some computer science fundamentals, the bulk of this blog will mostly cover the mathematical basics one might either need to brush up on (or even take an entire course).

  https://www.kdnuggets.com/2018/04/data-science-interview-guide.html

• Is ReLU After Sigmoid Bad?

  Recently [we] were analyzing how different activation functions interact among themselves, and we found that using relu after sigmoid in the last two layers worsens the performance of the model.

  https://www.kdnuggets.com/2018/03/relu-after-sigmoid-bad.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

• 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

• An Intuitive Guide to Deep Network Architectures

  How and why do different Deep Learning models work? We provide an intuitive explanation for 3 very popular DL models: Resnet, Inception, and Xception.

  https://www.kdnuggets.com/2017/08/intuitive-guide-deep-network-architectures.html

• 37 Reasons why your Neural Network is not working">37 Reasons why your Neural Network is not working

  Over the course of many debugging sessions, I’ve compiled my experience along with the best ideas around in this handy list. I hope they would be useful to you.

  https://www.kdnuggets.com/2017/08/37-reasons-neural-network-not-working.html

• ResNets, HighwayNets, and DenseNets, Oh My!

  This post walks through the logic behind three recent deep learning architectures: ResNet, HighwayNet, and DenseNet. Each make it more possible to successfully trainable deep networks by overcoming the limitations of traditional network design.

  https://www.kdnuggets.com/2016/12/resnets-highwaynets-densenets-oh-my.html

• Deep Learning Reading Group: Deep Residual Learning for Image Recognition

  Published in 2015, today's paper offers a new architecture for Convolution Networks, one which has since become a staple in neural network implementation. Read all about it here.

  https://www.kdnuggets.com/2016/09/deep-learning-reading-group-deep-residual-learning-image-recognition.html

• Deep Learning Reading Group: Deep Networks with Stochastic Depth

  An concise overview of a recent paper which introduces a new way to perturb networks during training in order to improve their performance, stochastic depth networks.

  https://www.kdnuggets.com/2016/09/deep-learning-reading-group-stochastic-depth-networks.html

• A Beginner’s Guide To Understanding Convolutional Neural Networks Part 2

  This is the second part of a thorough introductory treatment of convolutional neural networks. Have a look after reading the first part.

  https://www.kdnuggets.com/2016/09/beginners-guide-understanding-convolutional-neural-networks-part-2.html

• A Beginner’s Guide To Understanding Convolutional Neural Networks Part 1">A Beginner’s Guide To Understanding Convolutional Neural Networks Part 1

  Interested in better understanding convolutional neural networks? Check out this first part of a very comprehensive overview of the topic.

  https://www.kdnuggets.com/2016/09/beginners-guide-understanding-convolutional-neural-networks-part-1.html

• What is the Difference Between Deep Learning and “Regular” Machine Learning?">What is the Difference Between Deep Learning and “Regular” Machine Learning?

  Another concise explanation of a machine learning concept by Sebastian Raschka. This time, Sebastian explains the difference between Deep Learning and "regular" machine learning.

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

• Attention and Memory in Deep Learning and NLP

  An overview of attention mechanisms and memory in deep neural networks and why they work, including some specific applications in natural language processing and beyond.

  https://www.kdnuggets.com/2016/01/attention-memory-deep-learning-nlp.html

• Recurrent Neural Networks Tutorial, Introduction

  Recurrent Neural Networks (RNNs) are popular models that have shown great promise in NLP and many other Machine Learning tasks. Here is a much-needed guide to key RNN models and a few brilliant research papers.

  https://www.kdnuggets.com/2015/10/recurrent-neural-networks-tutorial.html

• Comparing Natural Language Processing Techniques: RNNs, Transformers, BERT

  RNN, Transformers, and BERT are popular NLP techniques with tradeoffs in sequence modeling, parallelization, and pre-training for downstream tasks.

  https://www.kdnuggets.com/comparing-natural-language-processing-techniques-rnns-transformers-bert

• Exploring Neural Networks

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

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

• 6 Lessons Learned in 6 Months as a Data Scientist

  When transitioning into a Data Science career, a new mindset toward collaboration, data, and reporting is required. Learn from these recommendations on approaches you should consider to successfully develop into your dream job.

  https://www.kdnuggets.com/2020/10/6-lessons-6-months-data-scientist.html

• Understanding Backpropagation as Applied to LSTM

  Backpropagation is one of those topics that seem to confuse many once you move past feed-forward neural networks and progress to convolutional and recurrent neural networks. This article gives you and overall process to understanding back propagation by giving you the underlying principles of backpropagation.

  https://www.kdnuggets.com/2019/05/understanding-backpropagation-applied-lstm.html

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

  Have a look at this follow-up collection of free machine learning and data science courses to give you some winter study ideas.

  https://www.kdnuggets.com/2018/12/10-more-free-must-see-courses-machine-learning-data-science.html