Search results for Convolutional Neural Network

Found 432 documents, 5930 searched:

• Building a Convolutional Neural Network with PyTorch

  This blog post provides a tutorial on constructing a convolutional neural network for image classification in PyTorch, leveraging convolutional and pooling layers for feature extraction as well as fully-connected layers for prediction.

  https://www.kdnuggets.com/building-a-convolutional-neural-network-with-pytorch

• 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

• Image Classification with Convolutional Neural Networks (CNNs)

  In this article, we’ll look at what Convolutional Neural Networks are and how they work.

  https://www.kdnuggets.com/2022/05/image-classification-convolutional-neural-networks-cnns.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

• Convolutional Neural Network for Breast Cancer Classification

  See how Deep Learning can help in solving one of the most commonly diagnosed cancer in women.

  https://www.kdnuggets.com/2019/10/convolutional-neural-network-breast-cancer-classification.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

• Predict Age and Gender Using Convolutional Neural Network and OpenCV">Predict Age and Gender Using Convolutional Neural Network and OpenCV

  Age and gender estimation from a single face image are important tasks in intelligent applications. As such, let's build a simple age and gender detection model in this detailed article.

  https://www.kdnuggets.com/2019/04/predict-age-gender-using-convolutional-neural-network-opencv.html

• Using Topological Data Analysis to Understand the Behavior of Convolutional Neural Networks

  Neural Networks are powerful but complex and opaque tools. Using Topological Data Analysis, we can describe the functioning and learning of a convolutional neural network in a compact and understandable way.

  https://www.kdnuggets.com/2018/06/topological-data-analysis-convolutional-neural-networks.html

• Detecting Sarcasm with Deep Convolutional Neural Networks">Detecting Sarcasm with Deep Convolutional Neural Networks

  Detection of sarcasm is important in other areas such as affective computing and sentiment analysis because such expressions can flip the polarity of a sentence.

  https://www.kdnuggets.com/2018/06/detecting-sarcasm-deep-convolutional-neural-networks.html

• Building Convolutional Neural Network using NumPy from Scratch">Building Convolutional Neural Network using NumPy from Scratch

  In this article, CNN is created using only NumPy library. Just three layers are created which are convolution (conv for short), ReLU, and max pooling.

  https://www.kdnuggets.com/2018/04/building-convolutional-neural-network-numpy-scratch.html

• Derivation of Convolutional Neural Network from Fully Connected Network Step-By-Step

  What are the advantages of ConvNets over FC networks in image analysis? How is ConvNet derived from FC networks? Where the term convolution in CNNs came from? These questions are to be answered in this article.

  https://www.kdnuggets.com/2018/04/derivation-convolutional-neural-network-fully-connected-step-by-step.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

• How Convolutional Neural Networks Accomplish Image Recognition?

  Image recognition is very interesting and challenging field of study. Here we explain concepts, applications and techniques of image recognition using Convolutional Neural Networks.

  https://www.kdnuggets.com/2017/08/convolutional-neural-networks-image-recognition.html

• Visualizing Convolutional Neural Networks with Open-source Picasso

  Toolkits for standard neural network visualizations exist, along with tools for monitoring the training process, but are often tied to the deep learning framework. Could a general, easy-to-setup tool for generating standard visualizations provide a sanity check on the learning process?

  https://www.kdnuggets.com/2017/08/visualizing-convolutional-neural-networks-open-source-picasso.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 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

• How Convolutional Neural Networks Work

  Get an overview of what is going on inside convolutional neural networks, and what it is that makes them so effective.

  https://www.kdnuggets.com/2016/08/brohrer-convolutional-neural-networks-explanation.html

• Understanding Convolutional Neural Networks for NLP

  Dive into the world of Convolution Neural Networks (CNN), learn how they work, how to apply them for NLP, and how to tune CNN hyperparameters for best performance.

  https://www.kdnuggets.com/2015/11/understanding-convolutional-neural-networks-nlp.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

• Exploring Neural Networks

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

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

• Neural Networks and Deep Learning: A Textbook (2nd Edition)

  The second edition of the book Neural Networks and Deep Learning is now available. This book covers both classical and modern models in deep learning. The book is intended to be a textbook for universities, and it covers the theoretical and algorithmic aspects of deep learning. The second edition is significantly expanded and covers many modern topics such as graph neural networks, adversarial learning, attention mechanisms, transformers, and large language models.

  https://www.kdnuggets.com/2023/07/aggarwal-neural-networks-deep-learning-textbook-2nd-edition.html

• Building and Training Your First Neural Network with TensorFlow and Keras

  Learn how to build and train your first Image Classification model with Keras and TensorFlow using Convolutional Neural Network.

  https://www.kdnuggets.com/2023/05/building-training-first-neural-network-tensorflow-keras.html

• A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability

  We give a taxonomy of the trustworthy GNNs in privacy, robustness, fairness, and explainability. For each aspect, we categorize existing works into various categories, give general frameworks in each category, and more.

  https://www.kdnuggets.com/2022/05/comprehensive-survey-trustworthy-graph-neural-networks-privacy-robustness-fairness-explainability.html

• Learn Deep Learning by Building 15 Neural Network Projects in 2022

  Here are 15 neural network projects you can take on in 2022 to build your skills, your know-how, and your portfolio.

  https://www.kdnuggets.com/2022/01/15-neural-network-projects-build-2022.html

• Generating Beautiful Neural Network Visualizations">Generating Beautiful Neural Network Visualizations

  If you are looking to easily generate visualizations of neural network architectures, PlotNeuralNet is a project you should check out.

  https://www.kdnuggets.com/2020/12/generating-beautiful-neural-network-visualizations.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

• A Friendly Introduction to Graph Neural Networks

  Despite being what can be a confusing topic, graph neural networks can be distilled into just a handful of simple concepts. Read on to find out more.

  https://www.kdnuggets.com/2020/11/friendly-introduction-graph-neural-networks.html

• 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

• Batch Normalization in Deep Neural Networks

  Batch normalization is a technique for training very deep neural networks that normalizes the contributions to a layer for every mini batch.

  https://www.kdnuggets.com/2020/08/batch-normalization-deep-neural-networks.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

• Recreating Fingerprints using Convolutional Autoencoders

  The article gets you started working with fingerprints using Deep Learning.

  https://www.kdnuggets.com/2020/03/recreating-fingerprints-using-convolutional-autoencoders.html

• Deep Neural Networks

  We examine the features and applications of a deep neural network.

  https://www.kdnuggets.com/2020/02/deep-neural-networks.html

• Semi-supervised learning with Generative Adversarial Networks

  The paper discussed in this post, Semi-supervised learning with Generative Adversarial Networks, utilizes a GAN architecture for multi-label classification.

  https://www.kdnuggets.com/2020/01/semi-supervised-learning-generative-adversarial-networks.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

• Can Neural Networks Develop Attention? Google Thinks they Can

  Google recently published some work about modeling attention mechanisms in deep neural networks.

  https://www.kdnuggets.com/2019/11/neural-networks-develop-attention-google.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

• Can graph machine learning identify hate speech in online social networks?

  Online hate speech is a complex subject. Follow this demonstration using state-of-the-art graph neural network models to detect hateful users based on their activities on the Twitter social network.

  https://www.kdnuggets.com/2019/09/graph-machine-learning-hate-speech-social-networks.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

• 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

• Random Forests® vs Neural Networks: Which is Better, and When?">Random Forests® vs Neural Networks: Which is Better, and When?

  Random Forests and Neural Network are the two widely used machine learning algorithms. What is the difference between the two approaches? When should one use Neural Network or Random Forest?

  https://www.kdnuggets.com/2019/06/random-forest-vs-neural-network.html

• Graduating in GANs: Going From Understanding Generative Adversarial Networks to Running Your Own

  Read how generative adversarial networks (GANs) research and evaluation has developed then implement your own GAN to generate handwritten digits.

  https://www.kdnuggets.com/2019/04/graduating-gans-understanding-generative-adversarial-networks.html

• Generative Adversarial Networks – Key Milestones and State of the Art

  We provide an overview of Generative Adversarial Networks (GANs), discuss challenges in GANs learning, and examine two promising GANs: the RadialGAN, designed for numbers, and the StyleGAN, which does style transfer for images.

  https://www.kdnuggets.com/2019/04/future-generative-adversarial-networks.html

• The Rise of Generative Adversarial Networks

  A comprehensive overview of Generative Adversarial Networks, covering its birth, different architectures including DCGAN, StyleGAN and BigGAN, as well as some real-world examples.

  https://www.kdnuggets.com/2019/04/rise-generative-adversarial-networks.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

• Artificial Neural Network Implementation using NumPy and Image Classification">Artificial Neural Network Implementation using NumPy and Image Classification

  This tutorial builds artificial neural network in Python using NumPy from scratch in order to do an image classification application for the Fruits360 dataset

  https://www.kdnuggets.com/2019/02/artificial-neural-network-implementation-using-numpy-and-image-classification.html

• A comprehensive survey on graph neural networks

  This article summarizes a paper which presents us with a broad sweep of the graph neural network landscape. It’s a survey paper, so you’ll find details on the key approaches and representative papers, as well as information on commonly used datasets and benchmark performance on them.

  https://www.kdnuggets.com/2019/02/comprehensive-survey-graph-neural-networks.html

• Generative Adversarial Networks – Paper Reading Road Map

  To help the others who want to learn more about the technical sides of GANs, I wanted to share some papers I have read in the order that I read them.

  https://www.kdnuggets.com/2018/10/generative-adversarial-networks-paper-reading-road-map.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

• Neural Networks and Deep Learning: A Textbook">Neural Networks and Deep Learning: A Textbook

  This book covers both classical and modern models in deep learning. The book is intended to be a textbook for universities, and it covers the theoretical and algorithmic aspects of deep learning.

  https://www.kdnuggets.com/2018/09/aggarwal-neural-networks-textbook.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

• 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

• Inside the Mind of a Neural Network with Interactive Code in Tensorflow

  Understand the inner workings of neural network models as this post covers three related topics: histogram of weights, visualizing the activation of neurons, and interior / integral gradients.

  https://www.kdnuggets.com/2018/06/inside-mind-neural-network-interactive-code-tensorflow.html

• Batch Normalization in Neural Networks

  This article explains batch normalization in a simple way. I wrote this article after what I learned from Fast.ai and deeplearning.ai.

  https://www.kdnuggets.com/2018/06/batch-normalization-neural-networks.html

• On the contribution of neural networks and word embeddings in Natural Language Processing

  In this post I will try to explain, in a very simplified way, how to apply neural networks and integrate word embeddings in text-based applications, and some of the main implicit benefits of using neural networks and word embeddings in NLP.

  https://www.kdnuggets.com/2018/05/contribution-neural-networks-word-embeddings-natural-language-processing.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

• 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

• Building an Audio Classifier using Deep Neural Networks

  Using a deep convolutional neural network architecture to classify audio and how to effectively use transfer learning and data-augmentation to improve model accuracy using small datasets.

  https://www.kdnuggets.com/2017/12/audio-classifier-deep-neural-networks.html

• Estimating an Optimal Learning Rate For a Deep Neural Network

  This post describes a simple and powerful way to find a reasonable learning rate for your neural network.

  https://www.kdnuggets.com/2017/11/estimating-optimal-learning-rate-deep-neural-network.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

• A Guide For Time Series Prediction Using Recurrent Neural Networks (LSTMs)

  Looking at the strengths of a neural network, especially a recurrent neural network, I came up with the idea of predicting the exchange rate between the USD and the INR.

  https://www.kdnuggets.com/2017/10/guide-time-series-prediction-recurrent-neural-networks-lstms.html

• Keras Tutorial: Recognizing Tic-Tac-Toe Winners with Neural Networks

  In this tutorial, we will build a neural network with Keras to determine whether or not tic-tac-toe games have been won by player X for given endgame board configurations. Introductory neural network concerns are covered.

  https://www.kdnuggets.com/2017/09/neural-networks-tic-tac-toe-keras.html

• Neural Network Foundations, Explained: Activation Function

  This is a very basic overview of activation functions in neural networks, intended to provide a very high level overview which can be read in a couple of minutes. This won't make you an expert, but it will give you a starting point toward actual understanding.

  https://www.kdnuggets.com/2017/09/neural-network-foundations-explained-activation-function.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

• Mind Reading: Using Artificial Neural Nets to Predict Viewed Image Categories From EEG Readings

  This post outlines the approach taken at a recent deep learning hackathon, hosted by YCombinator-backed startup DeepGram. The dataset: EEG readings from a Stanford research project that predicted which category of images their test subjects were viewing using linear discriminant analysis.

  https://www.kdnuggets.com/2017/08/mind-reading-using-artificial-neural-nets.html

• Building, Training, and Improving on Existing Recurrent Neural Networks

  In this post, we’ll provide a short tutorial for training a RNN for speech recognition, including code snippets throughout.

  https://www.kdnuggets.com/2017/05/building-training-improving-existing-recurrent-neural-networks.html

• Artificial Neural Networks (ANN) Introduction, Part 1

  This intro to ANNs will look at how we can train an algorithm to recognize images of handwritten digits. We will be using the images from the famous MNIST (Mixed National Institute of Standards and Technology) database.

  https://www.kdnuggets.com/2016/12/artificial-neural-networks-intro-part-1.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

• 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

• Deep Residual Networks for Image Classification with Python + NumPy

  This post outlines the results of an innovative Deep Residual Network implementation for Image Classification using Python and NumPy.

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

• Must Know Tips for Deep Learning Neural Networks

  Deep learning is white hot research topic. Add some solid deep learning neural network tips and tricks from a PhD researcher.

  https://www.kdnuggets.com/2016/03/must-know-tips-deep-learning-part-1.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

• Top /r/MachineLearning Posts, September: Implement a neural network from scratch in C++

  Neural network in C++ for beginners, Chinese character handwriting recognition beats humans, a handy machine learning algorithm cheat sheet, neural nets versus functional programming, and a neural nets paper repository.

  https://www.kdnuggets.com/2015/10/top-reddit-machine-learning-september.html

• Data Science 101: Preventing Overfitting in Neural Networks

  Overfitting is a major problem for Predictive Analytics and especially for Neural Networks. Here is an overview of key methods to avoid overfitting, including regularization (L2 and L1), Max norm constraints and Dropout.

  https://www.kdnuggets.com/2015/04/preventing-overfitting-neural-networks.html

• Geoff Hinton AMA: Neural Networks, the Brain, and Machine Learning

  In a wide-ranging Q&A, Geoff Hinton addresses the future of deep learning, its biological inspirations, and his research philosophy.

  https://www.kdnuggets.com/2014/12/geoff-hinton-ama-neural-networks-brain-machine-learning.html

• Diving into the Pool: Unraveling the Magic of CNN Pooling Layers

  A Beginner's Guide to Max, Average, and Global Pooling in Convolutional Neural Networks.

  https://www.kdnuggets.com/diving-into-the-pool-unraveling-the-magic-of-cnn-pooling-layers

• KDnuggets News, May 11: SQL Notes for Professionals; How To Structure a Data Science Project

  SQL Notes for Professionals: The Free eBook Review; How To Structure a Data Science Project: A Step-by-Step Guide; Everything You Need to Know About Tensors; Free University Data Science Resources; Image Classification with Convolutional Neural Networks (CNNs)

  https://www.kdnuggets.com/2022/n19.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

• 2011: DanNet triggers deep CNN revolution

  In 2021, we are celebrating the 10-year anniversary of DanNet, which, in 2011, was the first pure deep convolutional neural network (CNN) to win computer vision contests. Read about its history here.

  https://www.kdnuggets.com/2021/02/dannet-triggers-deep-cnn-revolution.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

• GANs Need Some Attention, Too

  Self-Attention Generative Adversarial Networks (SAGAN; Zhang et al., 2018) are convolutional neural networks that use the self-attention paradigm to capture long-range spatial relationships in existing images to better synthesize new images.

  https://www.kdnuggets.com/2019/03/gans-need-some-attention-too.html

• Overview and benchmark of traditional and deep learning models in text classification

  In this post, traditional and deep learning models in text classification will be thoroughly investigated, including a discussion into both Recurrent and Convolutional neural networks.

  https://www.kdnuggets.com/2018/07/overview-benchmark-deep-learning-models-text-classification.html

• KDnuggets™ News 18:n18, May 2: Blockchain Explained in 7 Python Functions; Data Science Dirty Secret; Choosing the Right Evaluation Metric

  Also: Building Convolutional Neural Network using NumPy from Scratch; Data Science Interview Guide; Implementing Deep Learning Methods and Feature Engineering for Text Data: The GloVe Model; Jupyter Notebook for Beginners: A Tutorial

  https://www.kdnuggets.com/2018/n18.html

• Top Stories, Apr 16-22: 7 Books to Grasp Mathematical Foundations of Data Science and Machine Learning; Python Regular Expressions Cheat Sheet

  Also: Key Algorithms and Statistical Models for Aspiring Data Scientists; Why Deep Learning is perfect for NLP; Derivation of Convolutional Neural Network from Fully Connected Network Step-By-Step; Top 8 Free Must-Read Books on Deep Learning

  https://www.kdnuggets.com/2018/04/top-news-week-0416-0422.html

• TensorFlow for Short-Term Stocks Prediction

  In this post you will see an application of Convolutional Neural Networks to stock market prediction, using a combination of stock prices with sentiment analysis.

  https://www.kdnuggets.com/2017/12/tensorflow-short-term-stocks-prediction.html

• Using Deep Learning To Extract Knowledge From Job Descriptions">Using Deep Learning To Extract Knowledge From Job Descriptions

  We present a deep learning approach to extract knowledge from a large amount of data from the recruitment space. A learning to rank approach is followed to train a convolutional neural network to generate job title and job description embeddings.

  https://www.kdnuggets.com/2017/05/deep-learning-extract-knowledge-job-descriptions.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

• Deep Learning cleans podcast episodes from ‘ahem’ sounds

  “3.5 mm audio jack… Ahem!!” where did you hear that? ;) Well, this post is not about Google Pixel vs iPhone 7, but how to remove ugly “Ahem” sound from a speech using deep convolutional neural network. I must say, very interesting read.

  https://www.kdnuggets.com/2016/11/deep-learning-cleans-podcast-ahem-sounds.html

• KDnuggets™ News 16:n32, Sep 7: Cartoon: Data Scientist was sexiest job until…; Up to Speed on Deep Learning

  Cartoon: Data Scientist - the sexiest job of the 21st century until...; Up to Speed on Deep Learning: July Update; How Convolutional Neural Networks Work; Learning from Imbalanced Classes; What is the Role of the Activation Function in a Neural Network?

  https://www.kdnuggets.com/2016/n32.html

• Facebook Open Sources deep-learning modules for Torch

  We review Facebook recently released Torch module for Deep Learning, which helps researchers train large scale convolutional neural networks for image recognition, natural language processing and other AI applications.

  https://www.kdnuggets.com/2015/02/facebook-open-source-deep-learning-torch.html

• The Ultimate Roadmap to Becoming Specialised in The Tech Industry

  There is more than one route that you can take to be a competitive tech professional.

  https://www.kdnuggets.com/the-ultimate-roadmap-to-becoming-specialised-in-the-tech-industry

• Unlock the Secrets of LLMs in 60-Minute with Andrej Karpathy

  Karpathy's talk provides a comprehensive yet accessible introduction to large language models, explaining their capabilities, future potential, and associated security risks in an engaging manner.

  https://www.kdnuggets.com/unlock-the-secrets-of-llms-in-a-60-minute-with-andrej-karpathy

• Back to Basics Pathway

  Kickstart your 2024 with KDnuggets Back to Basics Data Science pathway!

  https://www.kdnuggets.com/back-to-basics-pathway

• 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

• 5 Super Cheat Sheets to Master Data Science

  The collection of super cheat sheets covers basic concepts of data science, probability & statistics, SQL, machine learning, and deep learning.

  https://www.kdnuggets.com/5-super-cheat-sheets-to-master-data-science

• Top 10 Kaggle Machine Learning Projects to Become Data Scientist in 2024

  Master Data Science with Top 10 Kaggle ML Projects to become a Data Scientist.

  https://www.kdnuggets.com/top-10-kaggle-machine-learning-projects-to-become-data-scientist-in-2024

• Back to Basics Week 4: Advanced Topics and Deployment

  Welcome back to Week 4 of KDnuggets’ "Back to Basics" series. This week, we will dive into more advanced topics such as neural networks and deployment.

  https://www.kdnuggets.com/back-to-basics-week-4-advanced-topics-and-deployment

• Top 7 Essential Cheat Sheets To Ace Your Data Science Interview

  The blog covers cheat sheets on SQL, statistics, pandas, data visualization, scikit-learn, Git, and theoretical data science concepts.

  https://www.kdnuggets.com/top-7-essential-cheat-sheets-to-ace-your-data-science-interview

• 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

• 5 Free Books to Master Data Science

  Want to break into data science? Check this list of free books for learning Python, statistics, linear algebra, machine learning and deep learning.

  https://www.kdnuggets.com/5-free-books-to-master-data-science

• Getting Started with PyTorch in 5 Steps

  This tutorial provides an in-depth introduction to machine learning using PyTorch and its high-level wrapper, PyTorch Lightning. The article covers essential steps from installation to advanced topics, offering a hands-on approach to building and training neural networks, and emphasizing the benefits of using Lightning.

  https://www.kdnuggets.com/5-steps-getting-started-pytorch

• KDnuggets News, September 27: ChatGPT Projects Cheat Sheet • Introduction to PyTorch & Lightning AI

  10 ChatGPT Projects Cheat Sheet • Introduction to Deep Learning Libraries: PyTorch and Lightning AI • Top 5 Free Alternatives to GPT-4 • Machine Learning Evaluation Metrics: Theory and Overview • Kick Ass Midjourney Prompts with Poe

  https://www.kdnuggets.com/2023/n35.html

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

  A review from a data science practitioner.

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

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

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

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

• The Best Courses for AI from Universities with YouTube Playlists

  Kickstart a new career or develop your current one with these YouTube playlists by trusted Universities!.

  https://www.kdnuggets.com/2023/08/best-courses-ai-universities-youtube-playlists.html

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

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

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

• Exploring the Latest Trends in AI/DL: From Metaverse to Quantum Computing

  The author discusses several emerging trends in Artificial Intelligence and Deep Learning such as Metaverse and Quantum Computing.

  https://www.kdnuggets.com/2023/07/exploring-latest-trends-aidl-metaverse-quantum-computing.html

• 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

• A Practical Guide to Transfer Learning using PyTorch

  In this article, we’ll learn to adapt pre-trained models to custom classification tasks using a technique called transfer learning. We will demonstrate it for an image classification task using PyTorch, and compare transfer learning on 3 pre-trained models, Vgg16, ResNet50, and ResNet152.

  https://www.kdnuggets.com/2023/06/practical-guide-transfer-learning-pytorch.html

• Calculate Computational Efficiency of Deep Learning Models with FLOPs and MACs

  In this article we will learn about its definition, differences and how to calculate FLOPs and MACs using Python packages.

  https://www.kdnuggets.com/2023/06/calculate-computational-efficiency-deep-learning-models-flops-macs.html

• Your Ultimate Guide to Chat GPT and Other Abbreviations

  Everyone seems to have gone crazy about ChatGPT, which has become a cultural phenomenon. If you’re not on the ChatGPT train yet, this article might help you better understand the context and excitement around this innovation.

  https://www.kdnuggets.com/2023/06/ultimate-guide-chat-gpt-abbreviations.html

• Ten Years of AI in Review

  From image classification to chatbot therapy.

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

• Advanced Feature Selection Techniques for Machine Learning Models

  Mastering Feature Selection: An Exploration of Advanced Techniques for Supervised and Unsupervised Machine Learning Models.

  https://www.kdnuggets.com/2023/06/advanced-feature-selection-techniques-machine-learning-models.html

• Multimodal Models Explained

  Unlocking the Power of Multimodal Learning: Techniques, Challenges, and Applications.

  https://www.kdnuggets.com/2023/03/multimodal-models-explained.html

• Free TensorFlow 2.0 Complete Course

  Are you a beginner python programmer aiming to make a career in Machine Learning? If yes, then you are at the right place! This FREE tutorial will give you a solid understanding of the foundations of Machine Learning and Neural Networks using TensorFlow 2.0.

  https://www.kdnuggets.com/2023/02/free-tensorflow-20-complete-course.html

• 10 Free Machine Learning Courses from Top Universities

  Learn the basics of machine learning, including classification, SVM, decision tree learning, neural networks, convolutional, neural networks, boosting, and K nearest neighbors.

  https://www.kdnuggets.com/2023/02/10-free-machine-learning-courses-top-universities.html

• 7 Best Libraries for Machine Learning Explained

  Learn about machine learning libraries for building and deploying machine learning models.

  https://www.kdnuggets.com/2023/01/7-best-libraries-machine-learning-explained.html

• 5 Free Data Science Books You Must Read in 2023

  Get your hands on these gems to learn Python, data analytics, machine learning, and deep learning.

  https://www.kdnuggets.com/2023/01/5-free-data-science-books-must-read-2023.html

• A Guide to Train an Image Classification Model Using Tensorflow

  Classify images at scale and with very high accuracy with the advent of machine learning and deep learning algorithms.

  https://www.kdnuggets.com/2022/12/guide-train-image-classification-model-tensorflow.html

• 7 Super Cheat Sheets You Need To Ace Machine Learning Interview

  Revise the concepts of machine learning algorithms, frameworks, and methodologies to ace the technical interview round.

  https://www.kdnuggets.com/2022/12/7-super-cheat-sheets-need-ace-machine-learning-interview.html

• YOLOv5 PyTorch Tutorial

  Learn and train object detection model using YOLOv5.

  https://www.kdnuggets.com/2022/12/yolov5-pytorch-tutorial.html

• Tuning Adam Optimizer Parameters in PyTorch

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

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

• Getting Started with PyTorch Lightning

  Introduction to PyTorch Lightning and how it can be used for the model building process. It also provides a brief overview of the PyTorch characteristics and how they are different from TensorFlow.

  https://www.kdnuggets.com/2022/12/getting-started-pytorch-lightning.html

• Research Papers for NLP Beginners

  Read research papers on neural models, word embedding, language modeling, and attention & transformers.

  https://www.kdnuggets.com/2022/11/research-papers-nlp-beginners.html

• The ABCs of NLP, From A to Z

  There is no shortage of tools today that can help you through the steps of natural language processing, but if you want to get a handle on the basics this is a good place to start. Read about the ABCs of NLP, all the way from A to Z.

  https://www.kdnuggets.com/2022/10/abcs-nlp-a-to-z.html

• KDnuggets News, July 13: Linear Algebra for Data Science; 10 Modern Data Engineering Tools

  Linear Algebra for Data Science; 10 Modern Data Engineering Tools; Python String Processing Cheatsheet; Simple Salary Guide for Tech Experts 2022; 16 Essential DVC Commands for Data Science

  https://www.kdnuggets.com/2022/n28.html

• Bounding Box Deep Learning: The Future of Video Annotation

  Bounding box deep learning has several benefits that make it well-suited for video annotation.

  https://www.kdnuggets.com/2022/07/bounding-box-deep-learning-future-video-annotation.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, 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