Search results for gradient descent

Nothing but NumPy: Understanding & Creating Neural Networks with Computational Graphs from Scratch">Nothing but NumPy: Understanding & Creating Neural Networks with Computational Graphs from Scratch
...to adjust the bias by moving in the negative direction of the gradient(recall the curve of the Loss function from before). This is technically called gradient descent, as we are “descending” away from the sloping region to a flat region using the direction of the gradient. Let’s do that. Fig 24....https://www.kdnuggets.com/2019/08/numpyneuralnetworkscomputationalgraphs.html

10 Gradient Descent Optimisation Algorithms + Cheat Sheet
...commonly used in deep learning models to update the weights of the neural network through backpropagation. In this post, I will summarise the common gradient descent optimisation algorithms used in the popular deep learning frameworks (e.g. TensorFlow, Keras, PyTorch, Caffe). The purpose of this...https://www.kdnuggets.com/2019/06/gradientdescentalgorithmscheatsheet.html

An Intuitive Introduction to Gradient Descent
...n in which the cost function reduces. By repeating this step thousands of times we’ll continually minimize our cost function. Pseudocode for Gradient Descent Gradient descent is used to minimize a cost function J(w) parametrized by model parameters w. The gradient (or derivative) tells us the...https://www.kdnuggets.com/2018/06/intuitiveintroductiongradientdescent.html

Enabling the Deep Learning Revolution
...et by adjusting the weights of the connections. But how does it do this automatically? The answer lies in the technique called – backpropagation with gradient descent. Gradient Descent The idea is to construct a cost function (or loss function) which measures the difference between...https://www.kdnuggets.com/2019/12/enablingdeeplearningrevolution.html

Neural Network Foundations, Explained: Updating Weights with Gradient Descent & Backpropagation
...long it takes to reach the optimal value, how many steps it takes to get there, and how direct or indirect our journey is. So, what about stochastic gradient descent (SGD)? The process of gradient descent is very formulaic, in that it takes the entirety of a dataset's forward pass and cost...https://www.kdnuggets.com/2017/10/neuralnetworkfoundationsexplainedgradientdescent.html

The Gentlest Introduction to Tensorflow – Part 2
...as well as the cost. Calculate prediction (y) & cost using a single datapoint To get better W, b, we perform gradient descent using TF’stf.train.GradientDescentOptimizer [B] to reduce the cost. In nontechnical terms: given the current cost, and based on the graph of how cost varies with other...https://www.kdnuggets.com/2016/08/gentlestintroductiontensorflowpart2.html

Learning to Learn by Gradient Descent by Gradient Descent
Learning to learn by gradient descent by gradient descent, Andrychowicz et al., NIPS 2016 One of the things that strikes me when I read these NIPS papers is just how short some of them are – between the introduction and the evaluation sections you might find only one or two pages! A general form...https://www.kdnuggets.com/2017/02/learninglearngradientdescent.html

Keep it simple! How to understand Gradient Descent algorithm">Keep it simple! How to understand Gradient Descent algorithm
…s are optimization problems and one of the most used algorithms to do the same is the Gradient Descent Algorithm. Now, for a starter, the name itself Gradient Descent Algorithm may sound intimidating, well, hopefully after going though this post,that might change. Lets take the example of…https://www.kdnuggets.com/2017/04/simpleunderstandgradientdescentalgorithm.html

Lagrange multipliers with visualizations and code
...ximizing) and keep doing this until we do get to a point where it is zero and hence, there is no where else to go (this optimization method is called gradient descent). Note that we don’t have to move exactly along the gradient. As long as we move in a direction that has a positive projection...https://www.kdnuggets.com/2019/08/lagrangemultipliersvisualizationscode.html

Artificial Neural Networks (ANN) Introduction, Part 2
...added to existing audio clips to generate new audio samples. Background noises used should already be present in our dataset. Technique 2: Minibatch Gradient Descent (Shorten Training Time) In our previous tutorial, we learned that an artificial neural network comprises neurons, and their...https://www.kdnuggets.com/2016/12/artificialneuralnetworksintropart2.html

The Gentlest Introduction to Tensorflow – Part 1
...it linear model that minimizes the cost function. Besides randomly trying different values, is there a better way to explore the W, b values quickly? Gradient Descent If you are on an expansive plateau in the mountains, when trying to descent to the lowest point, your viewpoint looks like this. The...https://www.kdnuggets.com/2016/08/gentlestintroductiontensorflowpart1.html

A Concise Overview of Standard Modelfitting Methods
...following approaches: Solving the model parameters analytically (closedform equations) Using an optimization algorithm (Gradient Descent, Stochastic Gradient Descent, Newton's Method, Simplex Method, etc.) 1) Normal Equations (closedform solution) The closedform solution may (should) be...https://www.kdnuggets.com/2016/05/conciseoverviewmodelfittingmethods.html

How Optimization Works
...tually a physical bowl, then we could easily find the bottom by dropping a marble in and letting it roll until it stops. This is the intuition behind gradient descent  literally "going downhill". To use gradient descent we start at an arbitrary temperature. Before beginning, we don't know anything...https://www.kdnuggets.com/2019/04/howoptimizationworks.html

Top KDnuggets tweets, Feb 0107: Learning to Learn by Gradient Descent by Gradient Descent
Most popular @KDnuggets tweets for Feb 0107 were Most Retweeted, Favorited, Viewed, and Clicked Learning to Learn by Gradient Descent by Gradient Descent https://t.co/IHRzaEJZNl #MachineLearning @adriancolyer https://t.co/7Cf7jCOJnL Top 10 most engaging Tweets Learning to Learn by Gradient...https://www.kdnuggets.com/2017/02/toptweetsfeb0107.html

The 10 Deep Learning Methods AI Practitioners Need to Apply
...f a function, which has the form as a function composition (as in Neural Nets). When you solve an optimization problem using a gradientbased method (gradient descent is just one of them), you want to compute the function gradient at each iteration. For a Neural Nets, the objective function has the...https://www.kdnuggets.com/2017/12/10deeplearningmethodsaipractitionersneedapply.html

Getting Started with PyTorch Part 1: Understanding How Automatic Differentiation Works
...ral network is trained, we need to compute gradients of the loss function, with respect to every weight and bias, and then update these weights using gradient descent. With neural networks hitting billions of weights, doing the above step efficiently can make or break the feasibility of training....https://www.kdnuggets.com/2018/04/gettingstartedpytorchunderstandingautomaticdifferentiation.html

A Summary of DeepMind’s Protein Folding Upset at CASP13">A Summary of DeepMind’s Protein Folding Upset at CASP13
...ried a ‘fancier’ strategy involving fragment assembly using Generative Adversarial Networks (GANs), but in the end, the best results were obtained by gradient descent optimization. Gradient descent was applied to a combination of scores from their deep learning model as well as molecular modeling...https://www.kdnuggets.com/2019/07/deepmindproteinfoldingupset.html

XGBoost: A Concise Technical Overview">XGBoost: A Concise Technical Overview
...ingle, distributed systems and outofcore computation Parallelization Working In order to understand XGBoost, we must first understand Gradient Descent and Gradient Boosting. a) Gradient Descent: A cost function measures how close the predicted values are, to the corresponding actual...https://www.kdnuggets.com/2017/10/xgboostconcisetechnicaloverview.html

Recent Advances for a Better Understanding of Deep Learning">Recent Advances for a Better Understanding of Deep Learning
...your own from scratch and walked away feeling bad about yourself because you couldn’t get it to perform. I don’t think it’s your fault. I think it’s gradient descent’s fault. Stated Ali Rahimi with a provocative tone in his talk at NIPS. Stochastic Gradient Descent (SGD) is indeed the cornerstone...https://www.kdnuggets.com/2018/10/recentadvancesdeeplearning.html

Neural Networks with Numpy for Absolute Beginners — Part 2: Linear Regression
...e see that as cost converges to the minima, the parameter p reaches a specific value called the optimal value. Let’s say the optimum value of p is a. Gradient Descent w.r.t p. [Source Link] You can make a few observations from this graph. It is clear from the graph, that as p moves towards a, the...https://www.kdnuggets.com/2019/03/neuralnetworksnumpyabsolutebeginnerspart2linearregression.html

KDnuggets™ News 17:n17, May 3: Learn Machine Learning… in 10 Days?!? Gradient Descent, Simplified
...ata The Analytics of Emotion and Depression News Top Stories, Apr 2430: Guerrilla Guide to Machine Learning with Python; Understand the Gradient Descent Algorithm The 2017 Data Scientist Report is now available MIT Researcher New MetaAnalysis Method To Help Settle Unresolved Debates...https://www.kdnuggets.com/2017/n17.html

Machine Learning Crash Course: Part 1
...errors, and divided by N, which is the number of data points we have, which is just the average of the squared errors. Hence, the mean squared error. Gradient Descent When we graph the cost function (with only two variables) it will look something like this: Now, it’s pretty evident where...https://www.kdnuggets.com/2017/05/machinelearningcrashcoursepart1.html

Introduction to Deep Learning
...g rates could be used to adapt the learning rate value for each iteration of the gradient. For more detailed explanation please read this overview of gradient descent optimization algorithms by Sebastian Ruder. To compute the gradient of the loss function in respect of a given vector of weights, we...https://www.kdnuggets.com/2018/09/introductiondeeplearning.html

Checklist for Debugging Neural Networks
...lly called minibatch) —You want the batch size to be large enough to have accurate estimates of the error gradient, but small enough that stochastic gradient descent (SGD) can regularize your network. Small batch sizes will result in a learning process that converges quickly at the cost of noise...https://www.kdnuggets.com/2019/03/checklistdebuggingneuralnetworks.html

Data Scientist’s Dilemma: The Cold Start Problem – Ten Machine Learning Examples
...ance. We then know whether to continue making model parameter adjustments in the same direction or in the opposite direction. This is called gradient descent. Gradient descent methods basically find the slope (i.e., the gradient) of the performance error curve as we progress from one model to the...https://www.kdnuggets.com/2019/01/datascientistdilemmacoldstartmachinelearning.html

Designing Your Neural Networks
...imizers (most variants of SGD) and works with most network architectures. Also, see the section on learning rate scheduling below. 3. Momentum Gradient Descent takes tiny, consistent steps towards the local minima and when the gradients are tiny it can take a lot of time to converge....https://www.kdnuggets.com/2019/11/designingneuralnetworks.html

The Backpropagation Algorithm Demystified
...ror function of any given error function and an artificial neural network while taking into account the different weights within that neural network. Gradient Descent: How To Train Your Dragon Picture this. You want to be the best basketball player in the world. That means you want to score every...https://www.kdnuggets.com/2019/01/backpropagationalgorithmdemystified.html

Extracting Knowledge from Knowledge Graphs Using Facebook’s PytorchBigGraph
...lculus to find the parameters — embeddings, which optimize our loss function. Stochastic gradient descent The essence of the stochastic gradient descent is to gradually adjust the parameters of the loss function in such a way that the loss function is getting gradually decreased. To...https://www.kdnuggets.com/2019/05/extractingknowledgegraphsfacebookpytorchbiggraph.html

10 New Things I Learnt from fast.ai Course V3
...ity. Contents: 10 New Things I Learnt The Universal Approximation Theorem Neural Networks: Design & Architecture Understanding the Loss Landscape Gradient Descent Optimisers Loss Functions Training Regularisation Tasks Model Interpretability Appendix: Jeremy Howard on Model Complexity &...https://www.kdnuggets.com/2019/06/thingslearntfastaicourse.html

XGBoost Algorithm: Long May She Reign
...of the interview process by deploying a more dynamic evaluation process. Gradient Boosting: A special case of boosting where errors are minimized by gradient descent algorithm e.g. the strategy consulting firms leverage by using case interviews to weed out less qualified candidates. XGBoost: Think...https://www.kdnuggets.com/2019/05/xgboostalgorithm.html

Train your Deep Learning model faster and sharper: Snapshot Ensembling — M models for the cost of 1">Train your Deep Learning model faster and sharper: Snapshot Ensembling — M models for the cost of 1
…the performance. Original. Reposted with permission. Bio: Harshvardhan Gupta writes at HackerNoon. Related: MustKnow: What is the idea behind ensemble learning? Optimization in Machine Learning: Robust or global minimum? Learning to Learn by Gradient Descent by Gradient Descent…https://www.kdnuggets.com/2017/08/traindeeplearningfastersnapshotensembling.html

37 Reasons why your Neural Network is not working">37 Reasons why your Neural Network is not working
…ample, different prime numbers for each dimension) and check how they propagate through the network. 26. Explore Gradient checking If you implemented Gradient Descent by hand, gradient checking makes sure that your backpropagation works like it should. More info: 1 2 3. IV. Training issues Credit:…https://www.kdnuggets.com/2017/08/37reasonsneuralnetworknotworking.html

Picking an Optimizer for Style Transfer
...ods. Firstorder methods minimize or maximize the function (in our case the loss function) using its gradient. Most widely used firstorder method is Gradient Descent and its variants, as illustrated here and explained in Excel(!). Secondorder method use the second derivative (Hessian) to minimize...https://www.kdnuggets.com/2017/07/pickingoptimizerstyletransfer.html

Deep Learning in H2O using R
...can be tuned to better match the labels and minimize the cost (difference between the predicted output and actual output for the record). Stochastic Gradient Descent uses random training set samples iteratively to minimize the cost. This is done by going backwards from the output layer towards the...https://www.kdnuggets.com/2018/01/deeplearningh2ousingr.html

Optimization in Machine Learning: Robust or global minimum?
…io: Nikolaos Vasiloglou is Organizing Committee Member at UAI 2017 and has several years experience in building/developing distributed machine learning systems. Related: Introducing DaskSearchCV: Distributed hyperparameter optimization with ScikitLearn Learning to Learn by Gradient Descent by…https://www.kdnuggets.com/2017/06/robustglobalminimum.html

Deep Learning Key Terms, Explained">Deep Learning Key Terms, Explained
...ng as would be possible; this would clearly be ideal. So, by what mechanism is the cost function adjusted, with a goal of minimizing it? 11. Gradient Descent Gradient descent is an optimization algorithm used for finding local minima of functions. While it does not guarantee a global minimum,...https://www.kdnuggets.com/2016/10/deeplearningkeytermsexplained.html

Unveiling Mathematics Behind XGBoost">Unveiling Mathematics Behind XGBoost
...tion. Source: Wikipedia The intuition is by fitting a base learner to the negative gradient at each iteration is essentially performing gradient descent on the loss function. The negative gradients are often called as pseudo residuals, as they indirectly help us to minimize the...https://www.kdnuggets.com/2018/08/unveilingmathematicsbehindxgboost.html

All Machine Learning Models Have Flaws
...which aren't single layer linear combinations is often difficult. Gradient Descent Methodology: Specify an architecture with free parameters and use gradient descent with respect to data to tune the parameters. What is right: Relatively computationally tractable due to (a) modularity of gradient...https://www.kdnuggets.com/2015/03/allmachinelearningmodelshaveflaws.html

The Two Phases of Gradient Descent in Deep Learning
...tion is extremely radical, where the authors write: Indeed, in neural networks, we almost always choose our model as the output of running stochastic gradient descent. Appealing to linear models, we analyze how SGD acts as an implicit regularizer. For linear models, SGD always converges to a...https://www.kdnuggets.com/2017/05/twophasesgradientdescentdeeplearning.html

Top Stories, Apr 2430: Guerrilla Guide to Machine Learning with Python; Understand the Gradient Descent Algorithm
...urses 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. Cartoon: Machine Learning – What They Think I Do, by Harrison Kinsley  Apr 29,...https://www.kdnuggets.com/2017/05/topnewsweek04240430.html

Vowpal Wabbit: Fast Learning on Big Data
…processing based method. This sampling is not done in the traditional way by sampling observations. Instead, they sample one feature at a time in the gradient descent – in what is stochastic gradient descent. So those in the machine learning community who criticize sampling also need to be aware…https://www.kdnuggets.com/2014/05/vowpalwabbitfastlearningonbigdata.html

Sequence Modeling with Neural Networks – Part I
...e set of weights theta that achieve the lowest loss. For that, as explained in the first article “Introduction to Deep Learning”, we we can apply the gradient descent algorithm with backpropagation (chain rule) at every timestep, thus taking into account the additional time dimension. W and U are...https://www.kdnuggets.com/2018/10/sequencemodelingneuralnetworkspart1.html

Custom Optimizer in TensorFlow
...updates the variables Before running the Tensorflow Session, one should initiate an Optimizer as seen below: # Gradient Descent optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) tf.train.GradientDescentOptimizer is an object of the class GradientDescentOptimizer...https://www.kdnuggets.com/2018/01/customoptimizertensorflow.html

Evolving Deep Neural Networks
...algorithms with reinforcement learning generally comes as an onlyweights implementation. As in general gradientbased algorithms, such as Stochastic Gradient Descent (SGD) constraint exploration to gradient following, their search space becomes somewhat linear and local minima becomes a problem...https://www.kdnuggets.com/2019/06/evolvingdeepneuralnetworks.html

Want to know how Deep Learning works? Here’s a quick guide for everyone">Want to know how Deep Learning works? Here’s a quick guide for everyone
...eurons. We could randomly change them until our cost function is low, but that’s not very efficient. Instead, we will use a technique called Gradient Descent. Gradient Descent is a technique that allows us to find the minimum of a function. In our case, we are looking for the minimum of the cost...https://www.kdnuggets.com/2017/11/deeplearningworksquickguideeveryone.html

Troubleshooting Neural Networks: What is Wrong When My Error Increases?
...obably something wrong in our code Scaling and Shuffling Next, we want to check if the data has been scaled appropriately. E.g., if we use stochastic gradient descent and initialized our weights to small random numbers around zero, let's make sure that the features are standardized accordingly...https://www.kdnuggets.com/2016/05/troubleshootingneuralnetworkerrorincrease.html

Deep Learning in a Nutshell – what it is, how it works, why care?
...t down the parabolic bowl. This is a pretty amazing result from calculus, and it gives us the name of this general strategy for training neural nets: gradient descent. Learning Rates and the Delta Rule In practice at each step of moving perpendicular to the contour, we need to determine how far we...https://www.kdnuggets.com/2015/01/deeplearningexplanationwhathowwhy.html

Data Science 101: Preventing Overfitting in Neural Networks
...y of encouraging the network to use all of its inputs a little rather than using only some of its inputs a lot. Of particular note is that during the gradient descent update, using the L2 regularization ultimately means that every weight is decayed linearly to zero. Because of this phenomenon, L2...https://www.kdnuggets.com/2015/04/preventingoverfittingneuralnetworks.html

MultiTask Learning – ERNIE 2.0: StateoftheArt NLP Architecture Intuitively Explained
...sk is to predict the next word in a sentence. The input is the string “I like New” and the correct output is the string “York”. The training process (gradient descent) can be visualized as a ball rolling down a hill: where the terrain is the loss function (otherwise known as cost/error function),...https://www.kdnuggets.com/2019/10/multitasklearningerniesotanlparchitecture.html

Neural Networks: Innumerable Architectures, One Fundamental Idea
...rameters to the error produced by it while learning and training itself to make predictions accurately. The process of minimizing the error is called gradient descent. Descending a gradient has two aspects: choosing the direction to go in (momentum) and choosing the size of the step (learning...https://www.kdnuggets.com/2017/10/neuralnetworksinnumerablearchitecturesonefundamentalidea.html

Understanding Learning Rates and How It Improves Performance in Deep Learning
...nverge — especially if we get stuck on a plateau region. The following formula shows the relationship. new_weight = existing_weight — learning_rate * gradient Gradient descent with small (top) and large (bottom) learning rates. Source: Andrew Ng’s Machine Learning course on Coursera Typically...https://www.kdnuggets.com/2018/02/understandinglearningratesimprovesperformancedeeplearning.html

XGBoost, a Top Machine Learning Method on Kaggle, Explained">XGBoost, a Top Machine Learning Method on Kaggle, Explained
...residuals of the previous prediction and then minimizes the loss when adding the latest prediction. So, in the end, you are updating your model using gradient descent and hence the name, gradient boosting. This is supported for both regression and classification problems. XGBoost specifically,...https://www.kdnuggets.com/2017/10/xgboosttopmachinelearningmethodkaggleexplained.html

7 Steps to Understanding Deep Learning
...k from the maths and read this Deep Learning Tutorial by Google research scientist Quoc Le. Gradient descent, visualized. Step 3: Backpropagation and Gradient Descent An important part of neural networks, including modern deep architectures, is the backward propagation of errors through a...https://www.kdnuggets.com/2016/01/sevenstepsdeeplearning.html

5 More arXiv Deep Learning Papers, Explained
...ral network architectures. Hugo's Two Cents (excerpt): This is one of my favorite papers of this year. While the method of unrolling several steps of gradient descent (100 iterations in the paper) makes it somewhat impractical for large networks (which is probably why they considered 3layer...https://www.kdnuggets.com/2016/01/morearxivdeeplearningpapersexplained.html

9 Key Deep Learning Papers, Explained">9 Key Deep Learning Papers, Explained
...the number of filters used. Used ReLUs for their activation functions, crossentropy loss for the error function, and trained using batch stochastic gradient descent. Trained on a GTX 580 GPU for twelve days. Developed a visualization technique named Deconvolutional Network, which helps to examine...https://www.kdnuggets.com/2016/09/9keydeeplearningpapersexplained.html

Peeking Inside Convolutional Neural Networks
...a randomly initialized image, and doing gradient ascent on the image with regards to the activation of a specific unit. We also use blurring between gradient descent iterations (which is equivalent to regularization via a smoothness prior), and gradually reduce the "width" of the blur during...https://www.kdnuggets.com/2016/06/peekinginsideconvolutionalneuralnetworks.html

Contributing to PyTorch: By someone who doesn’t know a ton about PyTorch
...ng (that’s right kids, machine learning is calculus). You take the gradient of a tensor to help you figure out what you need to do to minimize error. Gradient descent is an algorithm that allows us to minimize error efficiently. The error is determined by our data. We have data that is properly...https://www.kdnuggets.com/2019/10/contributingpytorch.html

Implementing ResNet with MXNET Gluon and Comet.ml for Image Classification
...] For our model, we’re using the cifar_restnet20_v1 model architecture available from the MXNet gluoncv model zoo. We’ll use the Nesterov accelerated gradient descent algorithm for our optimizer. Nesterov accelerated gradient uses a “gamble, correct” approach to updating gradients where it uses...https://www.kdnuggets.com/2018/12/implementingresnetmxnetgluoncometmlimageclassification.html

Research Guide for Neural Architecture Search
...this paper relax the search space to be continuous. The architecture can, therefore, be optimized with respect to its validation set performances via gradient descent. The data efficiency of gradientbased optimization enables DARTS to achieve exemplary performance using fewer computation...https://www.kdnuggets.com/2019/10/researchguideneuralarchitecturesearch.html

Pytorch Cheat Sheet for Beginners and Udacity Deep Learning Nanodegree
...= optim.SGD(model.fc.parameters(), lr=0.001 , momentum=0.9) Check CUDA availability, set criterion to CrossEntropyLoss(), and optimizer to Stochastic Gradient Descent for example before starting to train. Note that the learning rate starts very small at about lr=0.001. Note that the training loop...https://www.kdnuggets.com/2019/08/pytorchcheatsheetbeginners.html

Deep Learning Research Review: Reinforcement Learning
...ween the true Q value (let’s just assume that it’s given to us for now) and the output of the approximate function. After we compute the loss, we use gradient descent to find the minimum value, at which point we will have our optimal W vector. This idea of function approximation is going to be very...https://www.kdnuggets.com/2016/11/deeplearningresearchreviewreinforcementlearning.html

A 2019 Guide for Automatic Speech Recognition
...ottleneck layer with 256 units, and an output layers with 32K units. Training consists of 14 passes of crossentropy followed by 1 pass of Stochastic Gradient Descent (SGD) sequence training using the boosted MMI (Maximum Mutual Information) criterion. This process is smoothed by adding the scaled...https://www.kdnuggets.com/2019/09/2019guideautomaticspeechrecognition.html

Deep Learning in Neural Networks: An Overview
...ation was described in 1981. For weightsharing FNNs or RNNs with activation spreading through some differentiable function ft, a single iteration of gradient descent through backpropagation computes changes of all the weights wi. Forward and backward passes are reiterated until sufficient...https://www.kdnuggets.com/2016/04/deeplearningneuralnetworksoverview.html

What is Softmax Regression and How is it Related to Logistic Regression?
...ass labels) and the O stands for output (the computed probability via softmax; notthe predicted class label). In order to learn our softmax model via gradient descent, we need to compute the derivative which we then use to update the weights in opposite direction of the gradient: for each class j....https://www.kdnuggets.com/2016/07/softmaxregressionrelatedlogisticregression.html

Introduction to PyTorch for Deep Learning
...imilar to the one above, but the difference is you’ll use torch.nn.Module to create the neural network. The other difference is the use of stochastic gradient descent optimizer instead of Adam. You can implement a custom nn module as shown below: Putting it all Together and Further Reading...https://www.kdnuggets.com/2018/11/introductionpytorchdeeplearning.html

Top 5 arXiv Deep Learning Papers, Explained
...h truncated backprop. I really hope they do in a future version of this work. Also, I don't think I buy their argument that the "theory of stochastic gradient descent applies". 2. SemiSupervised Learning with Ladder Network Authors: Antti Rasmus, Harri Valpola, Mikko Honkala, Mathias Berglund,...https://www.kdnuggets.com/2015/10/toparxivdeeplearningpapersexplained.html