Search results for Recurrent Neural Network

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

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

Deep Learning Key Terms, Explained">Deep Learning Key Terms, Explained
...inly to its incredible successes in a number of different areas, deep learning is the process of applying deep neural network technologies  that is, neural network architectures with multiple hidden layers  to solve problems. Deep learning is a process, like data mining, which employs deep neural...https://www.kdnuggets.com/2016/10/deeplearningkeytermsexplained.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

A Guide For Time Series Prediction Using Recurrent Neural Networks (LSTMs)
...rtterm memory model (LSTMs). We asked a data scientist, Neelabh Pant, to tell you about his experience of forecasting exchange rates using recurrent neural networks. As an Indian guy living in the US, I have a constant flow of money from home to me and vice versa. If the USD is stronger in the...https://www.kdnuggets.com/2017/10/guidetimeseriespredictionrecurrentneuralnetworkslstms.html

Deep Learning for NLP: ANNs, RNNs and LSTMs explained!">Deep Learning for NLP: ANNs, RNNs and LSTMs explained!
...s regarding what it has been told. It is nowhere near to Siri’s or Alexa’s capabilities, but it illustrates very well how even using very simple deep neural network structures, amazing results can be obtained. In this post we will learn about Artificial Neural Networks, Deep Learning, Recurrent...https://www.kdnuggets.com/2019/08/deeplearningnlpexplained.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

Recurrent Neural Networks Tutorial, Introduction
Recurrent Neural Networks (RNNs) are popular models that have shown great promise in many NLP tasks. But despite their recent popularity I’ve only found a limited number of resources that thoroughly explain how RNNs work, and how to implement them. That’s what this tutorial is about. It’s a…https://www.kdnuggets.com/2015/10/recurrentneuralnetworkstutorial.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

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 you...https://www.kdnuggets.com/2017/08/firststepslearningdeeplearningimageclassificationkeras.html

Sequence Modeling with Neural Networks – Part I
...Introduction to Deep Learning, we saw how to use Neural Networks to model a dataset of many examples. The good news is that the basic architecture of Neural Networks is quite generic whatever the application: a stacking of several perceptrons to compose complex hierarchical models and their...https://www.kdnuggets.com/2018/10/sequencemodelingneuralnetworkspart1.html

7 Steps to Mastering Deep Learning with Keras">7 Steps to Mastering Deep Learning with Keras
...network 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

Understanding Convolutional Neural Networks for NLP
…w other examples. To understand more about how convolutions work I also recommend checking out Chris Olah’s post on the topic. What are Convolutional Neural Networks? Now you know what convolutions are. But what about CNNs? CNNs are basically just several layers of convolutions with nonlinear…https://www.kdnuggets.com/2015/11/understandingconvolutionalneuralnetworksnlp.html

9 Key Deep Learning Papers, Explained">9 Key Deep Learning Papers, Explained
...d as one of the most influential publications in the field. Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton created a “large, deep convolutional neural network” that was used to win the 2012 ILSVRC (ImageNet LargeScale Visual Recognition Challenge). For those that aren’t familiar, this...https://www.kdnuggets.com/2016/09/9keydeeplearningpapersexplained.html

Attention and Memory in Deep Learning and NLP
...resolution” while perceiving the surrounding image in “low resolution”, and then adjusting the focal point over time. Attention in Neural Networks has a long history, particularly in image recognition. Examples include Learning to combine foveal glimpses with a thirdorder...https://www.kdnuggets.com/2016/01/attentionmemorydeeplearningnlp.html

Building, Training, and Improving on Existing Recurrent Neural Networks
By Matthew Rubashkin & Matt Mollison, Silicon Valley Data Science. On the deep learning R&D team at SVDS, we have investigated Recurrent Neural Networks (RNN) for exploring time series and developing speech recognition capabilities. Many products today rely on deep neural networks that...https://www.kdnuggets.com/2017/05/buildingtrainingimprovingexistingrecurrentneuralnetworks.html

A 2019 Guide for Automatic Speech Recognition
...peech recognition system developed using endtoend deep learning. Our architecture is… The major building block of Deep Speech is a recurrent neural network that has been trained to ingest speech spectrograms and generate English text transcriptions. The purpose of the RNN is to convert an...https://www.kdnuggets.com/2019/09/2019guideautomaticspeechrecognition.html

How to Build a Recurrent Neural Network in TensorFlow
By Erik Hallström, Deep Learning Research Engineer. In this tutorial I’ll explain how to build a simple working Recurrent Neural Network in TensorFlow. This is the first in a series of seven parts where various aspects and techniques of building Recurrent Neural Networks in TensorFlow are covered....https://www.kdnuggets.com/2017/04/buildrecurrentneuralnetworktensorflow.html

Attention Craving RNNS: Building Up To Transformer Networks
...NN review. Short sequence to sequence model review. Attention in RNN's. Improvements to attention. Transformer network introduction. Recurrent Neural Networks (RNN) RNNs let us model sequences in neural networks. While there are other ways of modeling sequences, RNNs are particularly...https://www.kdnuggets.com/2019/04/attentioncravingrnnbuildingtransformernetworks.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

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

Keras Cheat Sheet: Deep Learning in Python
...l idea how deep learning techniques work by using, for example, the Keras package. This package is ideal for beginners, as it offers you a highlevel neural networks API with which you can develop and evaluate deep learning models easily and quickly. Nevertheless, doubts may always arise and when...https://www.kdnuggets.com/2017/09/datacampkerascheatsheetdeeplearningpython.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

Introduction to Recurrent Networks in TensorFlow
...ou couldn’t follow. Bio: Danijar Hafner is a Python and C++ developer from Berlin interested in Machine Intelligence research. He recently released a neural networks library, but he likes creating new things in general. Original. Reposted with permission. Related: Recurrent Neural Networks...https://www.kdnuggets.com/2016/05/introrecurrentnetworkstensorflow.html

Exploring Recurrent Neural Networks
comments By Packtpub. In this tutorial, taken from Handson Deep Learning with Theano by Dan Van Boxel, we’ll be exploring recurrent neural networks. We’ll start off by looking at the basics, before looking at RNNs through a motivating weather modeling problem. We’ll also implement and train an...https://www.kdnuggets.com/2017/12/exploringrecurrentneuralnetworks.html

Using Genetic Algorithm for Optimizing Recurrent Neural Networks
...l see how to apply a Genetic Algorithm (GA) for finding an optimal window size and a number of units in Long ShortTerm Memory (LSTM) based Recurrent Neural Network (RNN). For this purpose, we will train and evaluate models for timeseries prediction problem using Keras. For GA, a python package...https://www.kdnuggets.com/2018/01/geneticalgorithmoptimizingrecurrentneuralnetwork.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

Going deeper with recurrent networks: Sequence to Bag of Words Model
…oints through measurements and survey results. Here’s a glimpse into how we achieve this at MarianaIQ. Going deeper with recurrent networks Recurrent neural network (RNN) is a network containing neural layers that have a temporal feedback loop. A neuron in this layer receives the current inputs as…https://www.kdnuggets.com/2017/08/deeperrecurrentnetworkssequencebagwordsmodel.html

Deep Learning for Natural Language Processing (NLP) – using RNNs & CNNs
...in. The main driver behind this sciencefictionturnedreality phenomenon is the advancement of Deep Learning techniques, specifically, the Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) architectures. Let’s look at a few of the Natural Language Processing tasks and...https://www.kdnuggets.com/2019/02/deeplearningnlprnncnn.html

Top /r/MachineLearning Posts, May: Unreasonable Effectiveness of Recurrent Neural Networks, TimeLapse Mining
...part of a new Deep Learning book, Kaggle's R tutorial, and a list of free ebooks for machine learning. 1. The Unreasonable Effectiveness of Recurrent Neural Networks +181 In this post, the author introduces the concept of a recurrent neural network, then dives into what makes them so effective. He...https://www.kdnuggets.com/2015/06/topmachinelearningpostsmay.html

Using the TensorFlow API: An Introductory Tutorial Series
...e API documentation to identify and implement said changes. Schematic of a RNN processing sequential data over time. Part 1: How to build a Recurrent Neural Network in TensorFlow In this tutorial I’ll explain how to build a simple working Recurrent Neural Network in TensorFlow. This is the first in...https://www.kdnuggets.com/2017/06/usingtensorflowapitutorialseries.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

20+ hottest research papers on Computer Vision, Machine Learning
...alBased Approach to Answering Questions About Images Mateusz Malinowski, Marcus Rohrbach, Mario Fritz We propose a novel approach based on recurrent neural networks for the challenging task of answering of questions about images. It combines a CNN with a LSTM into an endtoend architecture that...https://www.kdnuggets.com/2016/01/iccv201521hottestpapers.html

Age of AI Conference 2018 – Day 1 Highlights
...at contain objects of a certain class. Examples include: robot vision, autonomous driving and medical imaging. To perform semantic segmentation using Neural Networks, the traditional feature extraction is redundant as it builds a ‘deep representation’ from the whole image and is even detrimental...https://www.kdnuggets.com/2018/02/ageaiconference2018day1.html

Deep Learning Research Review: Natural Language Processing">Deep Learning Research Review: Natural Language Processing
...imization technique. Bonus: Another cool word vector initialization method: GloVe (Combines the ideas of coocurence matrices with Word2Vec) Recurrent Neural Networks (RNNs) Okay, so now that we have our word vectors, let’s see how they fit into recurrent neural networks. RNNs are the goto...https://www.kdnuggets.com/2017/01/deeplearningreviewnaturallanguageprocessing.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

Research Guide for Transformers
...some context from the previous sentence. This is quite critical so as not to lose any important context between sentences. Until recently, recurrent neural networks (RNNs) and convolutional neural networks (CNNs) have been used to tackle this challenge. The problem with these is that they aren’t...https://www.kdnuggets.com/2019/10/researchguidetransformers.html

Recurrent Neural Net describes images like Taylor Swift or Romantic Novel
...Taylor Swift). neuralstoryteller neuralstoryteller is a recently published experiment by Ryan Kiros (University of Toronto). It combines recurrent neural networks (RNN), skipthoughts vectors and other techniques to generate little stories about images. Neuralstoryteller’s outputs are creative...https://www.kdnuggets.com/2015/11/samimrecurrentneuralnetdescribeimagestaylorswift.html

Introduction to Deep Learning with Keras
...rior knowledge of machine learning packages such as scikitlearnand other scientific packages such as Pandas and Numpy. Training an Artificial Neural Network Training an artificial neural network involves the following steps: Weights are randomly initialized to numbers that are near...https://www.kdnuggets.com/2018/10/introductiondeeplearningkeras.html

Top /r/MachineLearning Posts, October: Machine learning video course, neural nets evaluate selfies
...e theoretical. I checked out a crossvalidation videos out of curiousity and was impressed. Disclaimer: bring your probability skills. 2. What a Deep Neural Network Thinks About Your #selfie +204 Andrej Karpathy trained a deep neural network to recognize good and bad selfies, with the ultimate...https://www.kdnuggets.com/2015/11/topredditmachinelearningoctober.html

The Unreasonable Reputation of Neural Networks
...s, watching neural networks show off their endless accumulation of new tricks. There are, as I see it, at least two good reasons to be impressed: (1) Neural networks can learn to model many natural functions well, from weak priors. The idea of marrying hierarchical, distributed representations with...https://www.kdnuggets.com/2016/01/unreasonablereputationneuralnetworks.html

Resurgence of AI During 19832010
...created. In this article, we first briefly discuss supervised learning, unsupervised learning and reinforcement learning, as well as shallow and deep neural networks, which became quite popular during this period. Next, we will discuss the following six reasons that helped AI research and...https://www.kdnuggets.com/2018/02/resurgenceai19832010.html

Getting started with NLP using the PyTorch framework
...Cell torch.nn.GRUCell Understanding these classes, their parameters, their inputs and their outputs are key to getting started with building your own neural networks for Natural Language Processing (NLP) in Pytorch. If you have started your NLP journey, chances are that you have encountered a...https://www.kdnuggets.com/2019/04/nlppytorch.html

Understanding Backpropagation as Applied to LSTM
...onsimplernnlstmfeataidangomezc7f286ba973d, https://medium.com/@aidangomez/letsdothisf9b699de31d9, http://www.wildml.com/2015/10/recurrentneuralnetworkstutorialpart3backpropagationthroughtimeandvanishinggradients/, https://arxiv.org/abs/1610.02583,...https://www.kdnuggets.com/2019/05/understandingbackpropagationappliedlstm.html

Beyond the Fence, and the Advent of the Creative Machines
...ments in the computational visual arts, Google's Inceptionism and Deep Dream have likely helped solidify the current widespread infatuation with deep neural networks and computer vision among a new wave of theorists and practitioners alike. Algorithmic music composition has been undertaken in the...https://www.kdnuggets.com/2016/01/beyondfenceadventcreativemachines.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

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

An introduction to explainable AI, and why we need it
comments By Patrick Ferris. The Black Box  a metaphor that represents the unknown inner mechanics of functions like neural networks Neural networks (and all of their subtypes) are increasingly being used to build programs that can predict and classify in a myriad of different settings. Examples...https://www.kdnuggets.com/2019/04/introductionexplainableai.html

In Deep Learning, Architecture Engineering is the New Feature Engineering
...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 contest in 2012. The model produced and documented by Alex Krizhevsky, Ilya...https://www.kdnuggets.com/2016/07/deeplearningarchitectureengineeringfeatureengineering.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

Top 5 arXiv Deep Learning Papers, Explained
...ted to arXiv: 28 Jul 2015 Abstract (excerpt): We introduce the "NoBackTrack" algorithm to train the parameters of dynamical systems such as recurrent neural networks. This algorithm works in an online, memoryless setting, thus requiring no backpropagation through time, and is scalable, avoiding the...https://www.kdnuggets.com/2015/10/toparxivdeeplearningpapersexplained.html

Semisupervised Feature Transfer: The Practical Benefit of Deep Learning Today?
...transferability during training. Also, these features tend to be manually engineered, rather than learned as part of the training/optimization. Deep neural network architectures are built of layers upon layers, and therefore can learn to compose hierarchical features where the inputs to one layer...https://www.kdnuggets.com/2016/07/semisupervisedfeaturetransferdeeplearning.html

The Birth of AI and The First AI Hype Cycle
...ing pulses, attempted to computationally model the behavior of a rat. In collaboration with physics graduate student Dean Edmonds, he built the first neural network machine called Stochastic Neural Analogy Reinforcement Computer (SNARC) [5]. Although primitive (consisting of about 300 vacuum tubes...https://www.kdnuggets.com/2018/02/birthaifirsthypecycle.html

Recursive (not Recurrent!) Neural Networks in TensorFlow
...lireza Nejati, University of Auckland. For the past few days I’ve been working on how to implement recursive neural networks in TensorFlow. Recursive neural networks (which I’ll call TreeNets from now on to avoid confusion with recurrent neural nets) can be used for learning treelike structures...https://www.kdnuggets.com/2016/06/recursiveneuralnetworkstensorflow.html

Deep Learning Transcends the Bag of Words
...ts on difficult classification tasks. Convolutional neural networks demonstrate an unprecedented ability to recognize objects in images. A variety of neural networks have similarly revolutionized the field of speech recognition. In machine learning parlance, these models are typically...https://www.kdnuggets.com/2015/12/deeplearningoutgrowsbagwordsrecurrentneuralnetworks.html

Are Deep Neural Networks Creative?
...uced by Ian Goodfellow, are capable of synthesizing novel images by modeling the distribution of seen images. Additionally, characterlevel recurrent neural network (RNN) language models now permeate the internet, appearing to hallucinate passages of Shakespeare, Linux source code, and even Donald...https://www.kdnuggets.com/2016/05/deepneuralnetworkscreativedeeplearningart.html

The Search for the Fastest Keras Deep Learning Backend
...e: Multilayer Perceptron/Deep NN Datasets/Tasks: MNIST handwritten digit dataset Objective: Classify images into 10 classes/digits In a standard Deep neural network test using MNIST dataset, CNTK, TensorFlow and Theano achieve similar scores (2.5 – 2.7 s/epoch) but MXNet blows it out of the water...https://www.kdnuggets.com/2017/09/searchfastestkerasdeeplearningbackend.html

Deep Learning for Visual Question Answering
…er problem at the intersection of NLP and Vision. This post will present ways to model this problem using Neural Networks, exploring both Feedforward Neural Networks, and the much more exciting Recurrent Neural Networks (LSTMs, to be specific). If you do not know much about Neural Networks, then I…https://www.kdnuggets.com/2015/11/deeplearningvisualquestionanswering.html

The Top A.I. Breakthroughs of 2015
...g a fresh network for each game, this team combined deep multitask reinforcement learning with deeptransfer learning to be able to use the same deep neural network across different types of games. This leads not only to a single instance that can succeed in multiple different games, but to one...https://www.kdnuggets.com/2016/02/topartificalintelligencebreakthroughs2015.html

Taming LSTMs: Variablesized minibatches and why PyTorch is good for your health
...ldman Sachs, Bonobos, Columbia. Original. Reposted with permission. Related: PyTorch Tensor Basics Getting Started with PyTorch Part 1: Understanding How Automatic Differentiation Works A Guide For Time Series Prediction Using Recurrent Neural Networks (LSTMs)...https://www.kdnuggets.com/2018/06/taminglstmsvariablesizedminibatchespytorch.html

MetaMind Mastermind Richard Socher: Uncut Interview
...neering and invention on one side and mathematics and nature on the other. The topic of patents seems relevant here. We don't really patent math. But neural network techniques increasingly are patented now, like dropout for instance. Maybe you can comment on if machine learning is something that...https://www.kdnuggets.com/2015/10/metamindmastermindrichardsocherdeeplearninginterview.html

Top 10 Videos on Deep Learning in Python">Top 10 Videos on Deep Learning in Python
...organized, thoroughly explained ,concise yet easy to follow tutorial on Deep Learning in Python. It includes TensorFlow implementation of a Recurrent Neural Network and Convolutional Neural Network with the MNIST dataset. 3. Individual tutorial: TensorFlow tutorial 02: Convolutional Neural Network...https://www.kdnuggets.com/2017/11/top10videosdeeplearningpython.html

Deep Learning RNNaissance, an insightful, comprehensive, and entertaining overview
Here is an excellent, very comprehensive, and entertaining overview and history of deep learning and recurrent neural network, given at Berkeley in August 2014 by Professor Jurgen Schmidhuber of IDSIA, Switzerland. Abstract: Machine learning and pattern recognition are currently being...https://www.kdnuggets.com/2014/10/deeplearningrnnaissancerecurrentneuralnetworks.html

Deep Learning Papers Reading Roadmap">Deep Learning Papers Reading Roadmap
...ion 18.7 (2006): 15271554. [pdf] (Deep Learning Eve) [3] Hinton, Geoffrey E., and Ruslan R. Salakhutdinov. "Reducing the dimensionality of data with neural networks." Science 313.5786 (2006): 504507. [pdf] (Milestone, Show the promise of deep learning) 1.3 ImageNet Evolution（Deep Learning...https://www.kdnuggets.com/2017/06/deeplearningpapersreadingroadmap.html

Should We Be Rethinking Unsupervised Learning?
...going on. My own recent focus has been on building networks that solve computer vision tasks beyond object recognition, building hardwarefriendlier neural networks, and improving the training of networks (for example, by orthogonalizing weights and avoiding gradient vanishing problems). Can you...https://www.kdnuggets.com/2016/08/rethinkingunsupervisedlearning.html

Around the World in 60 Days: Getting Deep Speech to Work in Mandarin
...essary (see section 2.3) [11]. With the Deep Speech network, constructing a new lexicon in Mandarin is unnecessary. Deep Speech uses a deep recurrent neural network that directly maps variable length speech to characters using the connectionist temporal classification loss function [4]. There is no...https://www.kdnuggets.com/2016/02/gettingdeepspeechworkmandarinbaidu.html

An Inside Update on Natural Language Processing
...all that," but I'm skeptical that a generic model structure will learn all these things from the data that is available to it. Seth> So you're an neuralnetwork skeptic. Jason>No, they are a great set of tools and techniques that are providing large improvements for many tasks. But they...https://www.kdnuggets.com/2016/06/insideupdatenaturallanguageprocessing.html

5 Free Resources for Getting Started with Deep Learning for Natural Language Processing">5 Free Resources for Getting Started with Deep Learning for Natural Language Processing
...d how they have been applied to NLP. This is a more concise survey than the paper below, and does a good job at 1/5 the length. 3. A Primer on Neural Network Models for Natural Language Processing A thorough overview by Yoav Goldberg. From the abstract: This tutorial surveys neural network...https://www.kdnuggets.com/2017/07/5freeresourcesgettingstarteddeeplearningnlp.html

An Overview of Python Deep Learning Frameworks">An Overview of Python Deep Learning Frameworks
By Madison May, indico. I recently stumbled across an old Data Science Stack Exchange answer of mine on the topic of the “Best Python library for neural networks”, and it struck me how much the Python deep learning ecosystem has evolved over the course of the past 2.5 years. The library I...https://www.kdnuggets.com/2017/02/pythondeeplearningframeworksoverview.html

Machine Learning Translation and the Google Translate Algorithm
...ecognition), but despite their flexibility, they can be applied only for tasks where the input and target have fixed dimensionality. Recurrent Neural Networks Here is where Long ShortTerm Memory networks (LSTMs) come into play, helping us to work with sequences whose length we can’t...https://www.kdnuggets.com/2017/09/machinelearningtranslationgoogletranslatealgorithm.html

Awesome Deep Learning: Most Cited Deep Learning Papers">Awesome Deep Learning: Most Cited Deep Learning Papers
...n research domains. Background Before this list, there exist other awesome deep learning lists, for example, Deep Vision and Awesome Recurrent Neural Networks. Also, after this list comes out, another awesome list for deep learning beginners, called Deep Learning Papers Reading Roadmap, has...https://www.kdnuggets.com/2017/04/awesomedeeplearningmostcitedpapers.html

A Statistical View of Deep Learning
...deep and statistical methods can be combined. Kernel methods (in part 3) and deep learning can easily be combined by parameterising the kernel with a neural network, giving the best of both worlds. I have chosen to view dropout (in part 5) as a prior assumption that does not require inference, and...https://www.kdnuggets.com/2015/11/statisticalviewdeeplearning.html

TensorFlow vs PyTorch vs Keras for NLP">TensorFlow vs PyTorch vs Keras for NLP
...ks focusing on Natural Language Processing. 1. Types of RNNs available in both When looking for a Deep Learning solution to an NLP problem, Recurrent Neural Networks (RNNs) are the most popular goto architecture for the developers. Therefore, it makes sense to compare the frameworks from this...https://www.kdnuggets.com/2019/09/tensorflowpytorchkerasnlp.html

ODSC India Highlights: Deep Learning Revolution in Speech, AI Engineer vs Data Scientist, and Reinforcement Learning for Enterprise
...nt in WER was achieved by replacing DNNs with LSTMs which are now commonly used for speech recognition. More recently, sequence to sequence recurrent neural network models have been found to have simpler implementations and comparable accuracy to the traditional methods. He concluded his talk by...https://www.kdnuggets.com/2018/09/odscindiahighlights.html

Examining the Transformer Architecture – Part 2: A Brief Description of How Transformers Work
...nsorship of Exxact As we learned in Part 1, The GPT2 is based on the Transformer, which is being hailed as the new NLP standard, replacing Recurrent Neural Networks. Some commentators believe that the Transformer will become the dominant NLP deep learning architecture of 2019. Let’s now take a...https://www.kdnuggets.com/2019/07/transformerarchitecturepart2.html

Interspeech 2018: Highlights for Data Scientists
...hby started with overview of deep learning models and information theory. He covered information plane based analysis, described learning dynamics of neural networks and other models, and, finally, showed an impact of multiple layers on learning process. Although the tutorial is highly theoretical...https://www.kdnuggets.com/2018/12/interspeech2018highlightsdatascientists.html

More Effective Transfer Learning for NLP
...— nonrecurrent models have shown competitive performance on a wide range of tasks. John Miller dives into this recent trend in his blog post, “When Recurrent Models Don’t Need to Be Recurrent”, suggesting that the infinite memory that LSTM’s have in theory may not actually be in practice. In...https://www.kdnuggets.com/2018/10/moreeffectivetransferlearningnlp.html

Deep Learning and Startups: Notes on Rework Conference, San Francisco
...ging DL) to gain 25% improvement of translating TED talks. Now developing representations of universal dependencies (universal across languages) with neural network based dependency parser. Key takeaway: Language is the way we transfer knowledge over time and space. If we start to unlock...https://www.kdnuggets.com/2016/02/deeplearningstartupsreworksanfrancisco.html

KDnuggets™ News 15:n03, Jan 28: Deep Learning Basics and “Flaws”; MetaMind; KDnuggets Pass to Strata
...in 2015; 8 Trends In Big Data For 2015. Top /r/MachineLearning posts, Jan 1824  Jan 26, 2015. Textbook Easter Eggs, issues with kmeans, recurrent neural networks, genetic algorithm challenges, and the implementation of machine learning pipelines are all in this week's top /r/MachineLearning...https://www.kdnuggets.com/2015/n03.html

The ICLR Experiment: Deep Learning Pioneers Take on Scientific Publishing
...nformally, it is often described as "the deep learning conference". The connection between "deep learning" and "representation learning" is that deep neural networks jointly learn to transform raw data into useful representations along with a classifier to separate examples into categories, while...https://www.kdnuggets.com/2016/02/iclrdeeplearningscientificpublishingexperiment.html

Juergen Schmidhuber AMA: The Principles of Intelligence and Machine Learning
...( unilearn.html, goedelmachine.html). (b) The principles of our less universal, but still rather general, very practical, programlearning recurrent neural networks can also be described by just a few lines of pseudocode, e.g., rnn.html, compressednetworksearch.html. General purpose quantum...https://www.kdnuggets.com/2015/03/juergenschmidhuberamaprinciplesintelligencemachinelearning.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

Top Data Science and Machine Learning Methods Used in 2017">Top Data Science and Machine Learning Methods Used in 2017
...on, 9% up, from 46.7% to 51.0% We also added new methods and here is their share in 2017: Gradient Boosted Machines, 20.4% Conv Nets, 15.8% Recurrent Neural Networks (RNN), 10.5% Hidden Markov Models (HMM), 4.6% Reinforcement Learning, 4.2% Markov Logic Networks, 2.5% Generative Adversarial...https://www.kdnuggets.com/2017/12/topdatasciencemachinelearningmethods.html

When not to use deep learning">When not to use deep learning
…ome brief mentions on how stochastic gradient descent works and what backpropagation is, the bulk of the explanation focuses on the rich landscape of neural network types (convolutional, recurrent, etc.). The optimization methods themselves receive little additional attention, which is unfortunate…https://www.kdnuggets.com/2017/07/whennotusedeeplearning.html

Great Data Scientists Don’t Just Think Outside the Box, They Redefine the Box
...the data. They turned the over 260+ variables into device performance “images.” Then once they created these “images,” the team leveraged a recurrent neural network to find “shapes” and repeatable patterns out of random pixels (see Figure 3). Figure 4: Pixelating Telemetry Data A recurrent neural...https://www.kdnuggets.com/2018/03/greatdatascientiststhinkoutsideredefinebox.html

Top Stories, Apr 2430: Guerrilla Guide to Machine Learning with Python; Understand the Gradient Descent Algorithm
...now Keep it simple! How to understand Gradient Descent algorithm Best Data Science Courses from Udemy (only $10 till Apr 29) How to Build a Recurrent Neural Network in TensorFlow Most Shared Last Week Keep it simple! How to understand Gradient Descent algorithm, by Jahnavi Mahanta  Apr 28, 2017....https://www.kdnuggets.com/2017/05/topnewsweek04240430.html