Search results for Convolutional Neural Network

An Intuitive Explanation of Convolutional Neural Networks
By Ujjwal Karn. What are Convolutional Neural Networks and why are they important? Convolutional Neural Networks (ConvNets or CNNs) are a category of Neural Networks that have proven very effective in areas such as image recognition and classification. ConvNets have been successful in identifying…https://www.kdnuggets.com/2016/11/intuitiveexplanationconvolutionalneuralnetworks.html

Research Guide for Neural Architecture Search
...pturnedreality forms the basis of this guide. We’ll explore a range of research papers that have sought to solve the challenging task of automating neural network design. In this guide, we assume that the reader has been involved in the process of designing neural networks from scratch using one...https://www.kdnuggets.com/2019/10/researchguideneuralarchitecturesearch.html

Understanding Deep Convolutional Neural Networks with a practical usecase in Tensorflow and Keras">Understanding Deep Convolutional Neural Networks with a practical usecase in Tensorflow and Keras
...: link Text classification at character level: link References Here’s a list of some references I used to learn about neural nets and convnets: neuralnetworksanddeeplearning.com : By far the best notes on neural networks and deep learning. I highly recommend this website to anyone who want to...https://www.kdnuggets.com/2017/11/understandingdeepconvolutionalneuralnetworkstensorflowkeras.html

7 Types of Artificial Neural Networks for Natural Language Processing">7 Types of Artificial Neural Networks for Natural Language Processing
...ble to learn to perform tasks like classification, prediction, decisionmaking, visualization, and others just by considering examples. An artificial neural network consists of artificial neurons or processing elements and is organized in three interconnected layers: input, hidden that may include...https://www.kdnuggets.com/2017/10/7typesartificialneuralnetworksnaturallanguageprocessing.html

Understanding Convolutional Neural Networks for NLP
When we hear about Convolutional Neural Network (CNNs), we typically think of Computer Vision. CNNs were responsible for major breakthroughs in Image Classification and are the core of most Computer Vision systems today, from Facebook’s automated photo tagging to selfdriving cars. More recently…https://www.kdnuggets.com/2015/11/understandingconvolutionalneuralnetworksnlp.html

The 8 Neural Network Architectures Machine Learning Researchers Need to Learn">The 8 Neural Network Architectures Machine Learning Researchers Need to Learn
...adings in a nuclear power plant Prediction: Future stock prices or currency exchange rates, Which movies will a person like What are Neural Networks? Neural networks are a class of models within the general machine learning literature. So for example, if you took a Coursera course on machine...https://www.kdnuggets.com/2018/02/8neuralnetworkarchitecturesmachinelearningresearchersneedlearn.html

Convolutional Neural Networks: A Python Tutorial Using TensorFlow and Keras">Convolutional Neural Networks: A Python Tutorial Using TensorFlow and Keras
comments Convolutional Neural Networks are a part of what made Deep Learning reach the headlines so often in the last decade. Today we’ll train an image classifier to tell us whether an image contains a dog or a cat, using TensorFlow’s eager API. Artificial Neural Networks have disrupted several...https://www.kdnuggets.com/2019/07/convolutionalneuralnetworkspythontutorialtensorflowkeras.html

Research Guide for Video Frame Interpolation with Deep Learning
... Video Frame Interpolation via Adaptive Separable Convolution (ICCV, 2017) In this paper, the authors propose a deep fully convolutional neural network that’s fed with two input frames and estimates pairs of 1D kernels for all pixels. The method is capable of estimating kernels and...https://www.kdnuggets.com/2019/10/researchguidevideoframeinterpolationdeeplearning.html

The 10 Deep Learning Methods AI Practitioners Need to Apply
...e not yet modeled. Neural networks are one type of model for machine learning; they have been around for at least 50 years. The fundamental unit of a neural network is a node, which is loosely based on the biological neuron in the mammalian brain. The connections between neurons are also modeled on...https://www.kdnuggets.com/2017/12/10deeplearningmethodsaipractitionersneedapply.html

First Steps of Learning Deep Learning: Image Classification in Keras
...al is not to teach neural networks by itself, but to provide an overview and to point to didactically useful resources. Don’t be afraid of artificial neural networks – it is easy to start! In fact, my biggest regret is delaying learning it, because of the perceived difficulty. To start, all...https://www.kdnuggets.com/2017/08/firststepslearningdeeplearningimageclassificationkeras.html

How Convolutional Neural Networks Accomplish Image Recognition?
…lions of videos and photos uploaded by the users that appear daily on social media. One way to solve this problem would be through the utilization of neural networks. We can make use of conventional neural networks for analyzing images in theory, but in practice, it will be highly expensive from a…https://www.kdnuggets.com/2017/08/convolutionalneuralnetworksimagerecognition.html

Using Topological Data Analysis to Understand the Behavior of Convolutional Neural Networks
...y of adversarial behavior. For these reasons, it is very desirable to develop methods for developing some understanding of the internal states of the neural networks. Because of the very large number of nodes (or neurons) in the networks, this becomes a problem in data analysis, specifically for...https://www.kdnuggets.com/2018/06/topologicaldataanalysisconvolutionalneuralnetworks.html

7 Steps to Understanding Deep Learning
...earch resurgence, and has been shown to deliver state of the art results in numerous applications. In essence, deep learning is the implementation of neural networks with more than a single hidden layer of neurons. This is, however, a very simplistic view of deep learning, and not one that is...https://www.kdnuggets.com/2016/01/sevenstepsdeeplearning.html

A Beginner’s Guide To Understanding Convolutional Neural Networks Part 1">A Beginner’s Guide To Understanding Convolutional Neural Networks Part 1
...1.jpg http://i.stack.imgur.com/vYju7.png https://upload.wikimedia.org/wikipedia/commons/thumb/c/c9/Simple_mouse.svg/479pxSimple_mouse.svg.png http://neuralnetworksanddeeplearning.com/images/tikz44.png http://cs231n.github.io/assets/cnnvis/filt1.jpeg http://i.stack.imgur.com/Nskzd.png...https://www.kdnuggets.com/2016/09/beginnersguideunderstandingconvolutionalneuralnetworkspart1.html

9 Key Deep Learning Papers, Explained">9 Key Deep Learning Papers, Explained
...oduction In this post, we’ll go into summarizing a lot of the new and important developments in the field of computer vision and convolutional neural networks. We’ll look at some of the most important papers that have been published over the last 5 years and discuss why they’re so important....https://www.kdnuggets.com/2016/09/9keydeeplearningpapersexplained.html

Knowing Your Neighbours: Machine Learning on Graphs">Knowing Your Neighbours: Machine Learning on Graphs
...) model for two papers in Cora. Each paper also has an attribute representing the subject of the paper. Each paper has one of seven subjects, such as Neural Networks, Probabilistic, Theory, etc. The dataset has 2708 papers (nodes) and 5429 citations (edges). Figure 5: The BagofWords (BoW)...https://www.kdnuggets.com/2019/08/neighboursmachinelearninggraphs.html

7 Steps to Mastering Deep Learning with Keras">7 Steps to Mastering Deep Learning with Keras
...work frameworks, libraries, and APIs available to anyone interested in getting started with deep learning. So… Why Keras? Keras is a highlevel neural network API, helping lead the way to the commoditization of deep learning and artificial intelligence. It runs on top of a number of...https://www.kdnuggets.com/2017/10/sevenstepsdeeplearningkeras.html

Peeking Inside Convolutional Neural Networks
By Audun M. Øygard, Schibsted Media Group. Convolutional neural networks are used extensively for a number of image related tasks these days. Despite being very successful, they’re mostly seen as “black box” models, since it’s hard to understand what happens inside the...https://www.kdnuggets.com/2016/06/peekinginsideconvolutionalneuralnetworks.html

Derivation of Convolutional Neural Network from Fully Connected Network StepByStep
comments In image analysis, convolutional neural networks (CNNs or ConvNets for short) are time and memory efficient than fully connected (FC) networks. But why? What are the advantages of ConvNets over FC networks in image analysis? How is ConvNet derived from FC networks? Where the term...https://www.kdnuggets.com/2018/04/derivationconvolutionalneuralnetworkfullyconnectedstepbystep.html

ResNets, HighwayNets, and DenseNets, Oh My!
...asn’t just consisted of greater depth for depths sake. For many applications, the most prominent of which being object classification, the deeper the neural network, the better the performance. That is, provided they can be properly trained! In this post I would like to walk through the logic...https://www.kdnuggets.com/2016/12/resnetshighwaynetsdensenetsohmy.html

Solve any Image Classification Problem Quickly and Easily
...le to solve any image classification problem quickly and easily. The article has been organised in the following way: Transfer learning Convolutional neural networks Repurposing a pretrained model Transfer learning process Classifiers on top of deep convolutional neural networks Example Summary...https://www.kdnuggets.com/2018/12/solveimageclassificationproblemquicklyeasily.html

A Beginner’s Guide To Understanding Convolutional Neural Networks Part 2
...that gets 100% or 99% on the training set, but only 50% on the test data. Dropout Layers Now, dropout layers have a very specific function in neural networks. In the last section, we discussed the problem of overfitting, where after training, the weights of the network are so tuned to the...https://www.kdnuggets.com/2016/09/beginnersguideunderstandingconvolutionalneuralnetworkspart2.html

A 2019 Guide to Speech Synthesis with Deep Learning
...eNet on Mel Spectrogram Predictions WaveNet: A Generative Model for Raw Audio The authors of this paper are from Google. They present a neural network for generating raw audio waves. Their model is fully probabilistic and autoregressive, and it generates stateoftheart...https://www.kdnuggets.com/2019/09/2019guidespeechsynthesisdeeplearning.html

Inside Deep Learning: Computer Vision With Convolutional Neural Networks
...ning more complex features out of raw pixel values, we’re going to use a special kind of neural network called a convolutional network. Convolutional neural networks, popularized by Yann LeCun and others in 1998 with LeNet are behind many of the successes of Deep Learning that have been reported...https://www.kdnuggets.com/2015/04/insidedeeplearningcomputervisionconvolutionalneuralnetworks.html

20+ hottest research papers on Computer Vision, Machine Learning
...(i.e., is this a dog or a table) directly from images. The world of computer vision is changing fast has changed. We now have powerful convolutional neural networks that are able to extract just about anything directly from images. So if your input is an image (or set of images), then...https://www.kdnuggets.com/2016/01/iccv201521hottestpapers.html

Research Guide for Depth Estimation with Deep Learning
...hoto editing tools. Unsupervised Monocular Depth Estimation with LeftRight Consistency (CVPR 2017) This paper proposes a convolutional neural network that’s trained to perform single image depth estimation without groundtruth depth data. The authors propose a network architecture...https://www.kdnuggets.com/2019/11/researchguidedepthestimationdeeplearning.html

Recreating Fingerprints using Convolutional Autoencoders
...onvolutional Autoencoder After having an overview of the fingerprint, its features, it is time to utilize our newly developed skill to build a Neural network that is capable of recreating or reconstructing fingerprint images. So, first of all, we’ll explore the dataset including what kind of...https://www.kdnuggets.com/2020/03/recreatingfingerprintsusingconvolutionalautoencoders.html

Inside the Mind of a Neural Network with Interactive Code in Tensorflow
...nbsp; Bio: Jae Duk Seo is a fourth year computer scientist at Ryerson University. Original. Reposted with permission. Related: Building Convolutional Neural Network using NumPy from Scratch How I Used CNNs and Tensorflow and Lost a Silver Medal in Kaggle Challenge Using Tensorflow Object Detection...https://www.kdnuggets.com/2018/06/insidemindneuralnetworkinteractivecodetensorflow.html

A 2019 Guide to Object Detection
...mentation Strategies for Object Detection RCNN Model This technique combines two main approaches: applying highcapacity convolutional neural networks to bottomup region proposals so as to localize and segment objects; and supervised pretraining for auxiliary tasks. Rich feature...https://www.kdnuggets.com/2019/08/2019guideobjectdetection.html

Visualizing Convolutional Neural Networks with Opensource Picasso
...intended classification task. To understand what we mean, consider this (possibly apocryphal) story [1]: Once upon a time, the US Army wanted to use neural networks to automatically detect camouflaged enemy tanks. The researchers trained a neural net on 50 photos of camouflaged tanks in trees, and...https://www.kdnuggets.com/2017/08/visualizingconvolutionalneuralnetworksopensourcepicasso.html

A 2019 Guide for Automatic Speech Recognition
...n, they use the clipped rectified linear (ReLU) function. At its core, this architecture is similar to Deep Speech 1. The architecture is a recurrent neural network trained to ingest speech spectrograms and output text transcriptions. The model is trained using the CTC loss function. source ...https://www.kdnuggets.com/2019/09/2019guideautomaticspeechrecognition.html

How Convolutional Neural Networks Work
pdf [2MB] ppt [6MB] Nine times out of ten, when you hear about deep learning breaking a new technological barrier, Convolutional Neural Networks are involved. Also called CNNs or ConvNets, these are the workhorse of the deep neural network field. They have learned to sort images into categories...https://www.kdnuggets.com/2016/08/brohrerconvolutionalneuralnetworksexplanation.html

Top 20 Deep Learning Papers, 2018 Edition">Top 20 Deep Learning Papers, 2018 Edition
...numbers when this article was published. In this list of papers more than 75% refer to deep learning and neural networks, specifically Convolutional Neural Networks (CNN). Almost 50% of them refer to pattern recognition applications in the field of computer vision. I believe tools like TensorFlow,...https://www.kdnuggets.com/2018/03/top20deeplearningpapers2018.html

A 2019 Guide to Human Pose Estimation
...son Pose Estimation and Instance Segmentation with a BottomUp, PartBased, Geometric Embedding Model DeepPose: Human Pose Estimation via Deep Neural Networks (CVPR, 2014) This paper proposes using deep neural networks(DNNs) to tackle this ML task. The authors of this paper are...https://www.kdnuggets.com/2019/08/2019guidehumanposeestimation.html

DeepMind Relational Reasoning Networks Demystified">DeepMind Relational Reasoning Networks Demystified
…ead phrases that are often misleading. For example, its new paper on relational reasoning networks has futurism reporting it like DeepMind Develops a Neural Network That Can Make Sense of Objects Around It. This is not only misleading, but it also makes the everyday non PhD person intimidated. In…https://www.kdnuggets.com/2017/08/deepmindrelationalreasoningnetworksdemystified.html

Predict Age and Gender Using Convolutional Neural Network and OpenCV">Predict Age and Gender Using Convolutional Neural Network and OpenCV
...o the tremendous leaps in performance recently reported for the related task of face recognition. — Age and Gender Classification using Convolutional Neural Networks Introduction Age and gender, two of the key facial attributes, play a very foundational role in social interactions,...https://www.kdnuggets.com/2019/04/predictagegenderusingconvolutionalneuralnetworkopencv.html

Top /r/MachineLearning Posts, June: Neural Network Generated Images, Free Data Science Books, Super Mario World
...Research’s neural nets, 16 great free books on data science, a machine learning system that can play Super Mario World, a tutorial to implement neural networks in Python, and a video stream that visualizes terms provided by the audience made it to the top. 1. Image generated by a...https://www.kdnuggets.com/2015/07/topmachinelearningpostsjune.html

Deep Learning for NLP: An Overview of Recent Trends">Deep Learning for NLP: An Overview of Recent Trends
...applying deep learning in NLP. Some topics include: The rise of distributed representations (e.g., word2vec) Convolutional, recurrent, and recursive neural networks Applications in reinforcement learning Recent development in unsupervised sentence representation learning Combining deep learning...https://www.kdnuggets.com/2018/09/deeplearningnlpoverviewrecenttrends.html

Don’t Use Dropout in Convolutional Networks
...lutional architectures. Dropout If you are reading this, I assume that you have some understanding of what dropout is, and its roll in regularizing a neural network. If you want a refresher, read this post by Amar Budhiraja. Srivastava, Nitish, et al. ”Dropout: a simple way to prevent neural...https://www.kdnuggets.com/2018/09/dropoutconvolutionalnetworks.html

How to Implement a YOLO (v3) Object Detector from Scratch in PyTorch: Part 1">How to Implement a YOLO (v3) Object Detector from Scratch in PyTorch: Part 1
...thresholding and Nonmaximum suppression Part 5 : Designing the input and the output pipelines Prerequisites You should understand how convolutional neural networks work. This also includes knowledge of Residual Blocks, skip connections, and Upsampling. What is object detection, bounding box...https://www.kdnuggets.com/2018/05/implementyolov3objectdetectorpytorchpart1.html

A 2019 Guide to Semantic Segmentation
...ntic Image Segmentation (ICCV, 2015) This paper proposes a solution to the challenge of dealing with weaklylabeled data in deep convolutional neural networks (CNNs), as well as a combination of data that’s welllabeled and data that’s not properly labeled. In the paper, a combination of...https://www.kdnuggets.com/2019/08/2019guidesemanticsegmentation.html

Deep Learning for Object Detection: A Comprehensive Review">Deep Learning for Object Detection: A Comprehensive Review
...nto its ancestry. RCNN RCNN is the granddaddy of Faster RCNN. In other words, RCNN reallykicked things off. RCNN, or Regionbased Convolutional Neural Network, consisted of 3 simple steps: Scan the input image for possible objects using an algorithm called Selective Search, generating ~2000...https://www.kdnuggets.com/2017/10/deeplearningobjectdetectioncomprehensivereview.html

Overview and benchmark of traditional and deep learning models in text classification
...ut these: Logistic regression with word ngrams Logistic regression with character ngrams Logistic regression with word and character ngrams Recurrent neural network (bidirectional GRU) without pretrained embeddings Recurrent neural network (bidirectional GRU) with GloVe pretrained embeddings...https://www.kdnuggets.com/2018/07/overviewbenchmarkdeeplearningmodelstextclassification.html

Building Convolutional Neural Network using NumPy from Scratch">Building Convolutional Neural Network using NumPy from Scratch
...ter control and understanding, you should try to implement them yourself. This article shows how a CNN is implemented just using NumPy. Convolutional neural network (CNN) is the stateofart technique for analyzing multidimensional signals such as images. There are different libraries that already...https://www.kdnuggets.com/2018/04/buildingconvolutionalneuralnetworknumpyscratch.html

Using AI to Super Compress Images
…networks have taken the world by storm. They recent surge is due to several factors, including cheap and powerful hardware, and vast amounts of data. Neural Networks are currently the state of the art when it comes to ‘cognitive’ tasks like image recognition, natural language understanding , etc….https://www.kdnuggets.com/2017/08/aicompressimages.html

Medical Image Analysis with Deep Learning , Part 2
...image we have input with 3 dimensions — length, width and depth (which are the color channels) Source: http://xrds.acm.org/blog/2016/06/convolutionalneuralnetworkscnnsillustratedexplanation/ Filters or Kernels : As shown in the figure from RiverTrail below, a filter or kernel slides to every...https://www.kdnuggets.com/2017/04/medicalimageanalysisdeeplearningpart2.html

Top 5 Deep Learning Resources, January
...rom training examples, some results of which are shown in the above image. Says hardmaru: In this blog post, I will describe how to train a recurrent neural network to generate fake, but plausible Chinese characters, in vector .svg format. I created a tool called sketchrnn that would attempt to...https://www.kdnuggets.com/2016/01/deeplearningreadinglistjanuary.html

Top arXiv Papers, January: ConvNets Advances, Wide Instead of Deep, Adversarial Networks Win, Learning to Reinforcement Learn
...or each paper, you will also find some modest commentary, links, perhaps an image, and an excerpt from the abstract. Recent Advances in Convolutional Neural Networks Jiuxiang Gu, Zhenhua Wang, Jason Kuen, Lianyang Ma, Amir Shahroudy, Bing Shuai, Ting Liu, Xingxing Wang, Gang Wang...https://www.kdnuggets.com/2017/02/toparxivpapersjanuaryconvnetswideadversarial.html

What is the Difference Between Deep Learning and “Regular” Machine Learning?">What is the Difference Between Deep Learning and “Regular” Machine Learning?
...eral way. The tl;dr version of this is: Deep learning is essentially a set of techniques that help we to parameterize deep neural network structures, neural networks with many, many layers and parameters. And if we are interested, a more concrete example: Let’s start with multilayer...https://www.kdnuggets.com/2016/06/differencebetweendeeplearningregularmachinelearning.html

TensorFlow for ShortTerm Stocks Prediction
comments By Mattia Brusamento Summary In machine learning, a convolutional neural network (CNN, or ConvNet) is a class of neural networks that has successfully been applied to image recognition and analysis. In this project I’ve approached this class of models trying to apply it to stock...https://www.kdnuggets.com/2017/12/tensorflowshorttermstocksprediction.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
...n use as it lets me check both LR and SVM with a common interface. You can also train it on >RAM sized datasets using mini batches. 6. Feedforward Neural Networks These are basically multilayered Logistic Regression classifiers. Many layers of weights separated by nonlinearities (sigmoid, tanh,...https://www.kdnuggets.com/2018/04/10machinelearningalgorithmsdatascientist.html

A “Weird” Introduction to Deep Learning">A “Weird” Introduction to Deep Learning
...just created this timeline based on several papers and other timelines with the purpose of everyone seeing that Deep Learning is much more than just Neural Networks. There has been really theoretical advances, software and hardware improvements that were necessary for us to get to this day. If you...https://www.kdnuggets.com/2018/03/weirdintroductiondeeplearning.html

Mind Reading: Using Artificial Neural Nets to Predict Viewed Image Categories From EEG Readings
...of images their test subjects were viewing using linear discriminant analysis. Winning Kaggle competition teams have successfully applied artificial neural networks on EEG data (see first place winner of the graspandlift challenge and third place winner of seizure prediction competition). Could...https://www.kdnuggets.com/2017/08/mindreadingusingartificialneuralnets.html

An Intuitive Guide to Deep Network Architectures
…a streamlined version of the Xception architecture optimized for mobile applications. The remaining three, however, truly redefine the way we look at neural networks. This rest of this post will focus on the intuition behind the ResNet, Inception, and Xception architectures, and why they have…https://www.kdnuggets.com/2017/08/intuitiveguidedeepnetworkarchitectures.html

Deep Learning cleans podcast episodes from ‘ahem’ sounds
…sounds on the podcast Data Science at Home? Because we remove them. Actually not us. A neural network does. The ahem detector is a deep convolutional neural network trained on transformed audio signals to recognize ahem sounds. The network has been trained to detect such signals on the episodes of…https://www.kdnuggets.com/2016/11/deeplearningcleanspodcastahemsounds.html

50 Deep Learning Software Tools and Platforms, Updated
...els of natural images (from Marc’Aurelio Ranzato). Nengo, a graphical and scripting based software package for simulating largescale neural systems. neuralnetworks, a Java based GPU library for deep learning algorithms. NVIDIA DIGITS is a new system for developing, training and visualizing deep...https://www.kdnuggets.com/2015/12/deeplearningtools.html

In Deep Learning, Architecture Engineering is the New Feature Engineering
...time a human being forces an architectural decision that couldn’t be learned, we’re essentially hard coding a feature. The Convolutional Neural Network (CNN) A major reason for the resurgence in popularity of neural networks were their impressive results from the ImageNet...https://www.kdnuggets.com/2016/07/deeplearningarchitectureengineeringfeatureengineering.html

Must Know Tips for Deep Learning Neural Networks
By XiuShen Wei, Nanjing University. Deep Neural Networks, especially Convolutional Neural Networks (CNN), allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the...https://www.kdnuggets.com/2016/03/mustknowtipsdeeplearningpart1.html

Convolutional Neural Network for Breast Cancer Classification
...ems. In this blog, I have demonstrated how to classify benign and malignant breast cancer from a collection of microscopic images using convolutional neural networks and transfer learning. References/Further Readings Transfer Learning for Image Classification in Keras One stop guide...https://www.kdnuggets.com/2019/10/convolutionalneuralnetworkbreastcancerclassification.html

Generate Realistic Human Face using GAN
...ology behind these kinds of AI is called a GAN, or “Generative Adversarial Network”. A GAN takes a different approach to learning than other types of neural networks. GANs algorithmic architectures that use two neural networks called a Generator and a Discriminator, which “compete” against one...https://www.kdnuggets.com/2020/03/generaterealistichumanfaceusinggan.html

Real World Deep Learning: Neural Networks for Smart Crops
...d information, which comes at a high cost. The advances in image classification, object detection, and semantic segmentation using deep Convolutional Neural Networks, which spawned the availability of open source tools such as Caffe and TensorFlow (to name a couple) to easily manipulate neural...https://www.kdnuggets.com/2017/11/realworlddeeplearningneuralnetworkssmartcrops.html

Predicting Popularity of Online Content
...by looking into the visual features such as the colors present in an image, edge and gradient distributions as well as outputs of deep convolutional neural networks – machine learning algorithms proved successful in image classification tasks such as ImageNet Challenge. The results show...https://www.kdnuggets.com/2016/05/predictingpopularityonlinecontent.html

Capsule Networks Are Shaking up AI – Here’s How to Use Them
...orks. I will show you how you can start using them today. Geoffrey Hinton is known as the father of “deep learning.” Back in the 50s the idea of deep neural networks began to surface and, in theory, could solve a vast amount of problems. However, nobody was able to figure out how to train them and...https://www.kdnuggets.com/2017/11/capsulenetworksshakingupai.html

Top /r/MachineLearning Posts, February: AlphaGo, Distributed TensorFlow, Neural Network Image Enhancement
...wakens fame), an image which shows a fuzzy, unclear original image sidebyside with a version of the image which has been enhanced via convolutional neural network. This paper from November, 2015, is responsible for the enhancement process used, with the paper’s abstract shown below: We...https://www.kdnuggets.com/2016/03/topredditmachinelearningfebrurary.html

Deep Learning in Neural Networks: An Overview
...problem (Minsky, 1963)…. The present survey will focus on the narrower, but now commercially important, subfield of Deep Learning (DL) in Artificial Neural Networks (NNs)… Learning or credit assignment is about finding weights that make the NN exhibit desired behaviour – such as driving a car....https://www.kdnuggets.com/2016/04/deeplearningneuralnetworksoverview.html

Deep Learning Finds What Makes a Good #selfie
...ore or less), and provides us with his personal recollection of the astonishment surrounding the ImageNet 2012 results. Under the hood, convolutional neural networks represent images with pixelbypixel arrays, and cover these image representations with convolutions, or filters. These filters then...https://www.kdnuggets.com/2015/10/karpathydeeplearninggoodselfie.html

Deep Learning and Artistic Style – Can art be quantified?
...” discussed here presents yet another interesting application of deep learning and a variation on the theme of computer vision. A convolutional neural network is employed to separate the style and content of different images and recombine these disparate sources into one cohesive piece....https://www.kdnuggets.com/2015/09/deeplearningartstyle.html

Text Classification & Embeddings Visualization Using LSTMs, CNNs, and Pretrained Word Vectors
...ification Using LSTM and visualize Word Embeddings In this part, I build a neural network with LSTM and word embeddings were leaned while fitting the neural network on the classification problem. The network starts with an embedding layer. The layer lets the system expand each token to a more...https://www.kdnuggets.com/2018/07/textclassificationlstmcnnpretrainedwordvectors.html

Deep Residual Networks for Image Classification with Python + NumPy
...only) deep learning framework along with the residual model, and trained it on CIFAR10, MNIST and SFDDD. Results speak by themselves. Convolutional Neural Networks for Computer Vision On Monday, June 13rd, I graduated with a master’s degree in computer engineering, presenting a thesis on...https://www.kdnuggets.com/2016/07/deepresidualneworksimageclassificationpythonnumpy.html

Top /r/MachineLearning Posts, November: TensorFlow, Deep Convolutional Generative Adversarial Networks, and lolz
...rials for TensorFlow. The tutorials are based on these Theano tutorials. Topics covered include simple multiplication, Linear regression, feedforward neural networks, and convolutional neural networks, among others. 4. Jeff Dean Explains TensorFlow +179 Here we have a video of Googler Jeff Dean...https://www.kdnuggets.com/2015/12/topredditmachinelearningnovember.html

Detecting Facial Features Using Deep Learning
...u can see the overall architecture here: Well, fairly complicated, right? Deep Learning Instead, we can use a very simple convolutional neural network (CNN) and perform detection of keypoints on parts of images we expect to contain faces. For that we need to have a training dataset...https://www.kdnuggets.com/2017/09/detectingfacialfeaturesdeeplearning.html

Object Detection: An Overview in the Age of Deep Learning
...deep learning for object detection was OverFeat from NYU published in 2013. They proposed a multiscale sliding window algorithm using Convolutional Neural Networks (CNNs). RCNN Quickly after OverFeat, Regions with CNN features or RCNN from Ross Girshick, et al. at the UC Berkeley...https://www.kdnuggets.com/2017/09/objectdetectionoverviewdeeplearning.html

Up to Speed on Deep Learning: July Update, Part 2
...se efforts. Neural Networks for Machine Learning course by Geoffrey Hintonwill be offered again in September 2016 on Coursera. Learn about artificial neural networks and how they’re being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language...https://www.kdnuggets.com/2016/09/deeplearningjulyupdatepart2.html

Up to Speed on Deep Learning: August Update
...so let us know if there’s something we should add, or if you’re interested in discussing this area further. An Intuitive Explanation of Convolutional Neural Networks by Ujjwal Karn. An thorough overview of CNNs: what they do, why they’re important, how they work, some history, and their underlying...https://www.kdnuggets.com/2016/09/deeplearningaugustupdate.html

KDnuggets™ News 16:n32, Sep 7: Cartoon: Data Scientist was sexiest job until…; Up to Speed on Deep Learning
...nbsp; Top Tweets Top KDnuggets tweets, Aug 2430: #DataScientist – sexiest job of the 21st century until …; Activation Function in #NeuralNetworks. Top KDnuggets tweets, Aug 1723: Approaching (Almost) Any #MachineLearning Problem; #Database Nirvana – can one query language...https://www.kdnuggets.com/2016/n32.html

Audio Data Analysis Using Deep Learning with Python (Part 2)
...ious article by building an Artificial Neural Network(ANN) for the music genre classification. In this article, we are going to build a Convolutional Neural Network for music genre classification. Nowadays, deep learning is more and more used for Music Genre Classification: particularly...https://www.kdnuggets.com/2020/02/audiodataanalysisdeeplearningpythonpart2.html

Top Stories, Aug 29Sep 4: How Convolutional Neural Networks Work; Activation Functions in Neural Networks
...rview Questions and Answers How to Become a (Type A) Data Scientist Up to Speed on Deep Learning: July Update Most Shared Last Week How Convolutional Neural Networks Work, by Brandon Rohrer – Aug 31, 2016. What is the Role of the Activation Function in a Neural Network?, By Sebastian Raschka...https://www.kdnuggets.com/2016/09/topnewsweek08290904.html

Fixing a Major Weakness in Machine Learning of Images with Hinton’s Capsule Networks
...iew of the other. This sort of cognitive transformation is effortlessly intuitive for a human, but turns out to be very difficult for a convolutional neural network without explicit training examples. Limitations of Convolutional and Max Pooling Layers Standard convolutional neural networks are...https://www.kdnuggets.com/2019/05/machinelearningimageshintoncapsulenetworks.html

KDnuggets Top Blogger: An Interview with Adit Deshpande, Deep Learning Aficionado
...s, all of which have been very informative and well received. Also, they have all been related to neural networks. What has attracted you to studying neural networks? Well, I think what most attracted me to this field was just the incredible progress and innovation that has been taking place. From...https://www.kdnuggets.com/2016/10/topbloggerinterviewaditdeshpande.html

Deep Compression: Optimization Techniques for Inference & Efficiency
...ration of top models runs the risk of rapid obsolescence. Deep Compression: Biologically Inspired Efficiency Improvements Deep learning convolutional neural networks, as their name suggests, are known for their depth and width, with some exceptional examples of resnets having about 1000 layers. At...https://www.kdnuggets.com/2019/03/deepcompressionoptimizationtechniquesinferenceefficiency.html

Using Deep Learning To Extract Knowledge From Job Descriptions">Using Deep Learning To Extract Knowledge From Job Descriptions
...escriptions in words and all words were transformed to lowercase. No feature engineering was done. Our model architecture consists of a convolutional neural network (CNN) that generates an embedding for a job description and a lookup table with job title embeddings: The cosine similarity between...https://www.kdnuggets.com/2017/05/deeplearningextractknowledgejobdescriptions.html

Top Stories, Apr 2329: Blockchain Explained in 7 Python Functions; Building Convolutional Neural Network using NumPy from Scratch
...Libraries and Platforms Most Shared Last Week Blockchain Explained in 7 Python Functions, by Tom Cusack – Apr 27, 2018. Building Convolutional Neural Network using NumPy from Scratch, by Ahmed Gad – Apr 26, 2018. Choosing the Right Metric for Evaluating Machine Learning Models –...https://www.kdnuggets.com/2018/04/topnewsweek04230429.html

Generative Adversarial Networks — Part II
...control the output systematically and reliably without having to randomly walk the space. This problem and solution will be explored in Part III. Original. Reposted with permission. Related: Can (Creative Adversarial Network)Explained Real World Deep Learning: Neural Networks for Smart Crops Top...https://www.kdnuggets.com/2017/11/generativeadversarialnetworkspart2.html

Top 20 Recent Research Papers on Machine Learning and Deep Learning">Top 20 Recent Research Papers on Machine Learning and Deep Learning
...Note that the second paper is only published last year. Read (or reread them) and learn about the latest advances. Dropout: a simple way to prevent neural networks from overfitting, by Hinton, G.E., Krizhevsky, A., Srivastava, N., Sutskever, I., & Salakhutdinov, R. (2014). Journal of Machine...https://www.kdnuggets.com/2017/04/top20papersmachinelearning.html

KDnuggets Exclusive: Interview with Yann LeCun, Deep Learning Expert, Director of Facebook AI Lab
...rception, mobile robotics, and computational neuroscience. He has published over 180 technical papers and book chapters on these topics as well as on neural networks, handwriting recognition, image processing and compression, and on dedicated circuits and architectures for computer perception. The...https://www.kdnuggets.com/2014/02/exclusiveyannlecundeeplearningfacebookailab.html

Top /r/MachineLearning Posts, January: Google Masters Go, Deep Learning Laughs, OpenAI AMA
...lorizing Black and White Photos with Deep Learning +346 This is a great post summarizing and showing examples from a project which used convolutional neural networks to colorize black and white photos. The description of the network architecture used is very good, as is the explanation of the...https://www.kdnuggets.com/2016/02/topredditmachinelearningjanuary.html

DeepSense: A unified deep learning framework for timeseries mobile sensing data processing
...ut actually we’re going to process slice by slice in the T dimension (one window at a time). Each d x 2f window slice is passed through a convolution neural network component comprising three stages as illustrated below: First we use 2D convolutional filters to capture interactions among...https://www.kdnuggets.com/2017/08/deepsenseunifieddeeplearningframeworktimeseriesmobile.html

Hands on Hyperparameter Tuning with Keras Tuner
...epo! Bio: Julie Prost (@JPro20) is a Data Scientist at Sicara. Original. Reposted with permission. Related: Automated Machine Learning Project Implementation Complexities Advanced Keras — Constructing Complex Custom Losses and Metrics Convolutional Neural Networks: A Python Tutorial Using...https://www.kdnuggets.com/2020/02/hyperparametertuningkerastuner.html

Deep Learning – important resources for learning and understanding
...5%) and the error rate fell to only 6.6% from 11.7%. Significantly, almost all of the entrants used a variant of an approach known as a convolutional neural network (ConvNet), an approach first refined in 1998 by Yann LeCun, NYU professor, recently hired to head Facebook AI Research Center. Here...https://www.kdnuggets.com/2014/08/deeplearningimportantresourceslearningunderstanding.html