Search results for vanishing gradient

Enabling the Deep Learning Revolution
...utput the value itself, if the input is negative the output would be zero. The function doesn’t saturate in the positive region, thereby avoiding the vanishing gradient problem to a large extent. Furthermore, the process of ReLu function evaluation is computationally efficient as it does not...https://www.kdnuggets.com/2019/12/enablingdeeplearningrevolution.html

Designing Your Neural Networks
...igible when they reach the first layers. This means the weights of the first layers aren’t updated significantly at each step. This is the problem of vanishing gradients. (A similar problem of exploding gradients occurs when the gradients for certain layers get progressively larger, leading to...https://www.kdnuggets.com/2019/11/designingneuralnetworks.html

Checklist for Debugging Neural Networks
...h. For example, the magnitude of the updates to the parameters (weights and biases) should be 1e3. There is a phenomenon called the ‘Dying ReLU’ or ‘vanishing gradient problem’ where the ReLU neurons will output a zero after learning a large negative bias term for its weights. Those neurons will...https://www.kdnuggets.com/2019/03/checklistdebuggingneuralnetworks.html

Deep Learning Best Practices – Weight Initialization
...ing standard normal distribution (np.random.randn(size_l, size_l1) in Python) while working with a (deep) network can potentially lead to 2 issues — vanishing gradients or exploding gradients. a) Vanishing gradients — In case of deep networks, for any activation function, abs(dW) will get smaller...https://www.kdnuggets.com/2018/06/deeplearningbestpracticesweightinitialization.html

Sequence Modeling with Neural Networks – Part I
...r away” steps become zero, and the state at those steps doesn’t contribute to what you are learning: you end up not learning longrange dependencies. Vanishing gradients aren’t exclusive to RNNs. They also happen in deep Feedforward Neural Networks. It’s just that RNNs tend to be very deep (as deep...https://www.kdnuggets.com/2018/10/sequencemodelingneuralnetworkspart1.html

37 Reasons why your Neural Network is not working">37 Reasons why your Neural Network is not working
…to use Adam or plain SGD with momentum. Check this excellent post by Sebastian Ruder to learn more about gradient descent optimizers. 35. Exploding / Vanishing gradients Check layer updates, as very large values can indicate exploding gradients. Gradient clipping may help. Check layer activations….https://www.kdnuggets.com/2017/08/37reasonsneuralnetworknotworking.html

Deep Learning Key Terms, Explained">Deep Learning Key Terms, Explained
...asured by a smaller cost function). Backpropagation (backward propagation of errors) is the method used to dole these updates out to the network. 12. Vanishing Gradient Problem Backpropagation uses the chain rule to compute gradients (by differentiation), in that layers toward the "front" (input)...https://www.kdnuggets.com/2016/10/deeplearningkeytermsexplained.html

Recurrent Neural Networks (RNN): Deep Learning for Sequential Data
...value in comparison to and autoregressive process. Disadvantages of an RNN Vanishing Gradient Problem Not suited for predicting long horizons Vanishing Gradient Problem As more layers containing activation functions are added, the gradient of the loss function approaches zero. The...https://www.kdnuggets.com/2020/07/rnndeeplearningsequentialdata.html

Implementing ResNet with MXNET Gluon and Comet.ml for Image Classification
...optimizer. Nesterov accelerated gradient uses a “gamble, correct” approach to updating gradients where it uses momentum and the previously calculated gradient to make an informed update to the next gradient that can be corrected later. You can read more about the Nesterov accelerated gradient here....https://www.kdnuggets.com/2018/12/implementingresnetmxnetgluoncometmlimageclassification.html

ResNets, HighwayNets, and DenseNets, Oh My!
...sNets, HighwayNets, and DenseNets. Residual Network A Residual Network, or ResNet is a neural network architecture which solves the problem of vanishing gradients in the simplest way possible. If there is trouble sending the gradient signal backwards, why not provide the network with a...https://www.kdnuggets.com/2016/12/resnetshighwaynetsdensenetsohmy.html

The Unreasonable Progress of Deep Neural Networks in Natural Language Processing (NLP)
...etworks are a subclass of RNNs that have fixed recurrent connections. Using static recurrent connections avoids the difficulty of training them with vanishing gradients, and in many early applications of RNNs echo state networks outperformed RNNs trained with backpropagation. A simple learning...https://www.kdnuggets.com/2020/06/unreasonableprogressdeepneuralnetworksnlp.html

The 8 Neural Network Architectures Machine Learning Researchers Need to Learn">The 8 Neural Network Architectures Machine Learning Researchers Need to Learn
...Short Term Memory: Make the RNN out of little modules that are designed to remember values for a long time. Hessian Free Optimization: Deal with the vanishing gradients problem by using a fancy optimizer that can detect directions with a tiny gradient but even smaller curvature. Echo State...https://www.kdnuggets.com/2018/02/8neuralnetworkarchitecturesmachinelearningresearchersneedlearn.html

Recurrent Neural Networks Tutorial, Introduction
…Ns trained with BPTT have difficulties learning longterm dependencies (e.g. dependencies between steps that are far apart) due to what is called the vanishing/exploding gradient problem. There exists some machinery to deal with these problems, and certain types of RNNs (like LSTMs) were…https://www.kdnuggets.com/2015/10/recurrentneuralnetworkstutorial.html

Deep Learning Reading Group: Deep Networks with Stochastic Depth
...ion that reaches the earliest layers is often too little to effectively train the network. Diminishing Feature Reuse: This is the same problem as the vanishing gradient, but in the forward direction. Features computed by early layers are washed out by the time they reach the final layers by the...https://www.kdnuggets.com/2016/09/deeplearningreadinggroupstochasticdepthnetworks.html

Deep Learning for NLP: An Overview of Recent Trends">Deep Learning for NLP: An Overview of Recent Trends
...meters as is the case for MVRNN. Recursive neural networks show flexibility and they have been coupled with LSTM units to deal with problems such as gradient vanishing. Recursive neural networks are used for various applications such as: Parsing Leveraging phraselevel representations for...https://www.kdnuggets.com/2018/09/deeplearningnlpoverviewrecenttrends.html

What is the Difference Between Deep Learning and “Regular” Machine Learning?">What is the Difference Between Deep Learning and “Regular” Machine Learning?
...ach weight to take a step into the opposite direction of the cost (or "error") gradient. Now, the problem with deep neural networks is the socalled "vanishing gradient"  the more layers we add, the harder it becomes to "update" our weights because the signal becomes weaker and weaker. Since our...https://www.kdnuggets.com/2016/06/differencebetweendeeplearningregularmachinelearning.html

Deep Learning in Neural Networks: An Overview
...blems. The reason for this was only fully understood in 1991, via Hochreiter’s diploma thesis: Typical deep NNs suffer from the now famous problem of vanishing or exploding gradients. With standard activation functions, cumulative backpropagated error signals either shrink rapidly, or grow out of...https://www.kdnuggets.com/2016/04/deeplearningneuralnetworksoverview.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
...e a non linearity, we will end up with a linear model that will fail in the classification task. They speed up the training process by preventing the vanishing gradient problem. Here's a visualization of what the ReLU layer does on an image example: Pooling layer The rectified feature maps...https://www.kdnuggets.com/2017/11/understandingdeepconvolutionalneuralnetworkstensorflowkeras.html

Deep Learning Specialization by Andrew Ng – 21 Lessons Learned">Deep Learning Specialization by Andrew Ng – 21 Lessons Learned
...a normalized and nonnormalized contour plot. Lesson 8: The importance of initialization Ng shows that poor initialization of parameters can lead to vanishing or exploding gradients. He demonstrates several procedure to combat these issues. The basic idea is to ensure that each layer’s weight...https://www.kdnuggets.com/2017/11/ngdeeplearningspecialization21lessons.html

Improving the Performance of a Neural Network
...ction helps your model to learn better. Nowadays, Rectified Linear Unit(ReLU) is the most widely used activation function as it solves the problem of vanishing gradients. Earlier Sigmoid and Tanh were the most widely used activation function. But, they suffered from the problem of vanishing...https://www.kdnuggets.com/2018/05/improvingperformanceneuralnetwork.html

Medical Image Analysis with Deep Learning , Part 2
...Hinton in his nature paper. ELUs Exponential linear units try to make the mean activations closer to zero which speeds up learning. ELUs also avoid a vanishing gradient via the identity for positive values. It has been shown that ELUs obtain higher classification accuracy than ReLUs. A very good...https://www.kdnuggets.com/2017/04/medicalimageanalysisdeeplearningpart2.html

Don’t Use Dropout in Convolutional Networks
...ork a resistance to vanishing gradient during training. This can decrease training time and result in better performance. Batch Normalization Combats Vanishing Gradient Keras Implementation To implement batch normalization in Keras, use the following: keras.layers.BatchNormalization() When...https://www.kdnuggets.com/2018/09/dropoutconvolutionalnetworks.html

Is ReLU After Sigmoid Bad?
...ients are not back propagated well. Either (sigmoid(output_2)*weigth_3 + bias_3) < 0 for most cases or sigmoid(output_2) is reaching the extremes (vanishing gradient). I am still doing experiments on these two. Suggest me something at twitter.com/nishantiam or create an issue on...https://www.kdnuggets.com/2018/03/reluaftersigmoidbad.html

Computer Vision by Andrew Ng  11 Lessons Learned">Computer Vision by Andrew Ng  11 Lessons Learned
...height. Lesson 6: Why ResNets works? For a plain network, the training error does not monotonically decrease as the number of layers increases due to vanishing and exploding gradients. These networks have feed forward skipped connections which allow you train extremely large networks without a drop...https://www.kdnuggets.com/2017/12/ngcomputervision11lessonslearnied.html

A Beginner’s Guide To Understanding Convolutional Neural Networks Part 2
...ot faster (because of the computational efficiency) without making a significant difference to the accuracy. It also helps to alleviate the vanishing gradient problem, which is the issue where the lower layers of the network train very slowly because the gradient decreases exponentially through the...https://www.kdnuggets.com/2016/09/beginnersguideunderstandingconvolutionalneuralnetworkspart2.html

Illustrating the Reformer
...to what we see in Fig. 6 (left)). Residual networks(ResNets) — introduced in this paper— are powerful component used in NN architectures to help with vanishing gradient problem in deep networks (with many layers). However, memory consumption in ResNets is a bottleneck as one needs to store the...https://www.kdnuggets.com/2020/02/illustratingreformer.html

An Intuitive Guide to Deep Network Architectures
…x — to start learning from. This idea works astoundingly well in practice. Previously, deep neural nets often suffered from the problem of vanishing gradients, in which gradient signals from the error function decreased exponentially as they backpropogated to earlier layers. In essence, by the…https://www.kdnuggets.com/2017/08/intuitiveguidedeepnetworkarchitectures.html

Three Impactful Machine Learning Topics at ICML 2016
...weight initialization and batch normalization enable networks to train beyond ten layers. Weight Initialization Weight initialization reduces vanishing and exploding behavior in the forward and backward signals. For healthy propagation, one should force the product of all layers’ scaled...https://www.kdnuggets.com/2016/07/impactfulmachinelearningtopicsicml2016.html

Data Science Interview Guide
...sible for me to cover the intricate details on this blog, it is important to know the basic mechanisms as well as the concept of back propagation and vanishing gradient. It is also important to realize that a Neural Network is essentially a black box. If the case study require you to build an...https://www.kdnuggets.com/2018/04/datascienceinterviewguide.html

Using the TensorFlow API: An Introductory Tutorial Series
...pioneered by Jürgen Schmidhuber among others. One problem with the RNN when using long timedependencies (truncated_backprop_length is large) is the “vanishing gradient problem”. One way to counter this is using a state that is “protected” and “selective”. The RNNLSTM remembers, forgets and...https://www.kdnuggets.com/2017/06/usingtensorflowapitutorialseries.html

Attention and Memory in Deep Learning and NLP
...mselves have a much longer history. The hidden state of a standard Recurrent Neural Network is itself a type of internal memory. RNNs suffer from the vanishing gradient problem that prevents them from learning longrange dependencies. LSTMs improved upon this by using a gating mechanism that allows...https://www.kdnuggets.com/2016/01/attentionmemorydeeplearningnlp.html

The secret sauce for growing from a data analyst to a data scientist
...should aim to capture fundamental concepts which most models and algorithms address just in different ways, e.g. dropout layers in neural networks, vanishing gradient, signal/noise relationships. Gaining the ability to relate problems back to these fundamentals will make you a good applied data...https://www.kdnuggets.com/2019/08/secretsaucegrowingfromdataanalystdatascientist.html

Secrets to a Successful Data Science Interview
...rs expect you to know how you will improve the results when models have high bias or high variance, what you will do to avoid exploding gradients and vanishing gradients and how you will optimize memory during training etc. Why it is still a good idea to reverse a linked list? While...https://www.kdnuggets.com/2019/07/secretsdatascienceinterview.html

Understanding Backpropagation as Applied to LSTM
...ngomez/letsdothisf9b699de31d9, http://www.wildml.com/2015/10/recurrentneuralnetworkstutorialpart3backpropagationthroughtimeandvanishinggradients/, https://arxiv.org/abs/1610.02583, https://machinelearningmastery.com/gentleintroductionbackpropagationtime/, and...https://www.kdnuggets.com/2019/05/understandingbackpropagationappliedlstm.html

Should We Be Rethinking Unsupervised Learning?
...cognition, building hardwarefriendlier neural networks, and improving the training of networks (for example, by orthogonalizing weights and avoiding gradient vanishing problems). Can you expand on the idea that we need to rethink unsupervised learning? At ICLR 2015 last spring I was chatting with...https://www.kdnuggets.com/2016/08/rethinkingunsupervisedlearning.html

A Beginner’s Guide To Understanding Convolutional Neural Networks Part 1">A Beginner’s Guide To Understanding Convolutional Neural Networks Part 1
...ng layers as well as hyperparameters of the network such as filter sizes, stride, and padding. Topics like network architecture, batch normalization, vanishing gradients, dropout, initialization techniques, nonconvex optimization,biases, choices of loss functions, data augmentation,regularization...https://www.kdnuggets.com/2016/09/beginnersguideunderstandingconvolutionalneuralnetworkspart1.html

An Introduction to AI">An Introduction to AI
...tock market data, speech, signals from sensors and energy data have temporal dependencies. LSTMs are a more efficient type of RNN that alleviates the vanishing gradient problem, giving it an ability to remember both in the short term as well as far in the history. Restricted Boltzmann Machine...https://www.kdnuggets.com/2018/11/anintroductionai.html

Top /r/MachineLearning Posts, February: AlphaGo, Distributed TensorFlow, Neural Network Image Enhancement
...new parameters for additional convolutions. Albeit advantages, learning a DRCN is very hard with a standard gradient descent method due to exploding/vanishing gradients. To ease the difficulty of training, we propose two extensions: recursivesupervision and skipconnection. Our method outperforms...https://www.kdnuggets.com/2016/03/topredditmachinelearningfebrurary.html