# Search results for cost function

Found 49 documents, 10516 searched:

• ### Nothing but NumPy: Understanding & Creating Neural Networks with Computational Graphs from Scratch"> Nothing but NumPy: Understanding & Creating Neural Networks with Computational Graphs from Scratch

...-batches, similarly for stochastic gradient descent where a batch is just one example. Before we proceed further we need to define something called a Cost Function. Cost Function When we perform “batch gradient descent” we need to slightly change our Loss function to accommodate not just one...

https://www.kdnuggets.com/2019/08/numpy-neural-networks-computational-graphs.html

• ### Complete Guide to Build ConvNet HTTP-Based Application using TensorFlow and Flask RESTful Python API"> Complete Guide to Build ConvNet HTTP-Based Application using TensorFlow and Flask RESTful Python API

..., the number of dataset classes is used as another input argument to the create_CNN function. The fully connected layer is created using the fc_layer function. Such function accepts the flattened result of the dropout layer, the number of features in such flattened result, and number of output...

• ### Machine Learning Crash Course: Part 1

...his method becomes impractical when working with hundreds or thousands of variables, we’ll be using the method machine learning algorithms often use. Cost Functions   The key to determining what parameters to choose to best approximate the data is to find a way to characterize how “wrong” our...

https://www.kdnuggets.com/2017/05/machine-learning-crash-course-part-1.html

• ### Interpreting Model Performance with Cost Functions

...ly related to the performance of data mining and predictive models. We go deep into the statistical properties and mathematical understanding of each cost function and explore their similarities and differences. Cost functions are important because there are many ways to design a machine learning...

https://www.kdnuggets.com/2014/01/salford-interpreting-model-performance-with-cost-functions.html

• ### Choosing an Error Function

...erms, we can think of the error function as how much it costs us in dollars to be wrong by a certain amount. In fact, error functions are also called cost functions. The choice of error function depends entirely on how our model will be used.   Use case: squared deviation Imagine our...

https://www.kdnuggets.com/2019/06/choosing-error-function.html

• ### Enabling the Deep Learning Revolution

...matically? 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 the actual output and predicted output from the model. Then gradients of this...

https://www.kdnuggets.com/2019/12/enabling-deep-learning-revolution.html

• ### The Gentlest Introduction to Tensorflow – Part 1

...by changing its curvature and position. Cost Function To compare which model is a better-fit more rigorously, we define best-fit mathematically as a cost function that we need to minimize. An example of a cost function can simply be the absolute sum of the differences between the actual outcome...

https://www.kdnuggets.com/2016/08/gentlest-introduction-tensorflow-part-1.html

• ### Getting Started with TensorFlow: A Machine Learning Tutorial

...a widely used algorithm in the field of applied sciences. This algorithm allows adding in implementation two important concepts of machine learning: Cost function and the gradient descent method for finding the minimum of the function. A machine learning algorithm that is implemented using this...

https://www.kdnuggets.com/2017/12/getting-started-tensorflow.html

• ### Neural Networks with Numpy for Absolute Beginners — Part 2: Linear Regression

..._train by defining a function forward_prop. def forward_prop(X, m, b): y_pred = m * X + b return y_pred y_pred = forward_prop(X_train, m, b) comments Cost/Loss Function   As mentioned earlier, now that you have both the corresponding values for X_train and the predicted values for y_pred...

https://www.kdnuggets.com/2019/03/neural-networks-numpy-absolute-beginners-part-2-linear-regression.html

• ### The Gentlest Introduction to Tensorflow – Part 2

...te the underscore). ‘Training’ Illustrated   To find the best W, b values, we can initially start with any W, b values. We also need to define a cost function, which is a measure of the differencebetween the prediction (y) for given a feature value (x), and the actual outcome (y_) for that...

https://www.kdnuggets.com/2016/08/gentlest-introduction-tensorflow-part-2.html

• ### An Intuitive Introduction to Gradient Descent

...n to evaluate how good a particular model is, our learning problem reduces to that of finding a good set of weights for our model which minimizes the cost function. Gradient descent is an iterative method. We start with some set of values for our model parameters (weights and biases), and improve...

• ### The Costs of Misclassifications

…t to treat a patient whereas if the model predicts a false positive (upper right corner) then it costs \$2,000 more than it would otherwise. Using the Cost Matrix A cost matrix can be used to evaluate the cost of a model (or with the model’s loss function to build entirely new models that minimize…

https://www.kdnuggets.com/2016/12/salford-costs-misclassifications.html

• ### Neural Network Foundations, Explained: Updating Weights with Gradient Descent & Backpropagation

...agine that a cost function is used to determine our error (the difference between actual and predicted values), based on a given weight. Consider the cost function illustrated in Figure 2. Figure 2. Cost function (Source). Now, let's take as true the assertion that the lowest point on that cost...

• ### A Concise Overview of Standard Model-fitting Methods

...Using the Gradient Decent (GD) optimization algorithm, the weights are updated incrementally after each epoch (= pass over the training dataset). The cost function J(⋅), the sum of squared errors (SSE), can be written as: The magnitude and direction of the weight update is computed by taking a step...

https://www.kdnuggets.com/2016/05/concise-overview-model-fitting-methods.html

• ### Fighting Overfitting in Deep Learning

...the best in the test data. As a result, we can choose hyperparameters with cross-validation.   Regularization     Or, in other words: Cost function = Loss + Regularization term Where Loss function is usually a function defined on a data point, prediction, and label, and measures the...

https://www.kdnuggets.com/2019/12/fighting-overfitting-deep-learning.html

• ### The Backpropagation Algorithm Demystified

...s much error as possible. So what do you do? You train. Much like perfecting basketball, gradient descent is an algorithm meant to minimize a certain cost function (room for error), so that the output is the most accurate it can be. But before you start training, you need to have all your...

https://www.kdnuggets.com/2019/01/backpropagation-algorithm-demystified.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

...through the whole data set, we can create a function that shows us how wrong the AI’s outputs were from the real outputs. This function is called the Cost Function. Ideally, we want our cost function to be zero. That’s when our AI’s outputs are the same as the data set outputs. How can we reduce...

https://www.kdnuggets.com/2017/11/deep-learning-works-quick-guide-everyone.html

• ### The Gentlest Introduction to Tensorflow – Part 4

...ides a vector of scores; one score per class, which is becomes the prediction vector. The sum of all prediction vectors becomes the final prediction. Cost Function Transformation We cannot use as cost function, any function that involves numerical distance between predicted and actual outcomes....

https://www.kdnuggets.com/2017/02/gentlest-introduction-tensorflow-part-4.html

• ### XGBoost: A Concise Technical Overview"> XGBoost: A Concise Technical Overview

...corresponding actual values. Ideally, we want as little difference as possible between the predicted values and the actual values. Thus, we want the cost function to be minimized. The weights associated with a trained model, cause it to predict values that are close to the actual values. Thus, the...

https://www.kdnuggets.com/2017/10/xgboost-concise-technical-overview.html

• ### Regularization in Logistic Regression: Better Fit and Better Generalization?

...el is to minimize the cost function, i.e., we want to find the feature weights that correspond to the global cost minimum (remember that the logistic cost function is convex). Now, if we regularize the cost function (e.g., via L2 regularization), we add an additional to our cost function (J) that...

https://www.kdnuggets.com/2016/06/regularization-logistic-regression.html

• ### Word Morphing – an original idea

...d Google’s pre-trained embeddings from here, and use gensim package to access them. Choosing the Weight Function Given the cosine similarity distance function, we can define our f function to be Eq. 1. Definition of weight function using cosine similarity However, using this approach we’ll face a...

https://www.kdnuggets.com/2018/11/word-morphing-original-idea.html

• ### How To Write Better SQL Queries: The Definitive Guide – Part 2

...ases where you abuse the HAVING clause, like in the above examples, in which you query the database by performing a function and then calling another function, or you use logic that contains loops, conditions, User Defined Functions (UDFs), cursors, … to get the final result. In this approach,...

https://www.kdnuggets.com/2017/08/write-better-sql-queries-definitive-guide-part-2.html

• ### Generative Adversarial Networks, an overview

...s for the output layer. Then we define a cost, based on the values in the output layer and the desired output (target value). For example, a possible cost function is the mean-squared error cost function. where, x is the input, h(x) is the output and y is the target. The sum is over the various...

• ### Neural Networks: Innumerable Architectures, One Fundamental Idea

...own function. Formed by interconnected neurons. These neurons have weights, and bias which are altered during the network training depending upon the cost function. Nodes — Also called neurons and similar to the neurons in the human brain. Basic structural units of any neural network. A group of...

https://www.kdnuggets.com/2017/10/neural-networks-innumerable-architectures-one-fundamental-idea.html

• ### Deep Learning Key Terms, Explained"> Deep Learning Key Terms, Explained

...s of the neural network. This gives you a direction in the parameter weight space in which the error would become smaller. I'll leave it at that. 10. Cost Function When training a neural network, the correctness of the network's output must be assessed. As we know the expected correct output of...

https://www.kdnuggets.com/2016/10/deep-learning-key-terms-explained.html

• ### Data Science Projects Employers Want To See: How To Show A Business Impact"> Data Science Projects Employers Want To See: How To Show A Business Impact

...they actually wouldn’t): \$0 If we multiply the number of each prediction type by the associated cost and sum them, we get the following equation for cost: Cost = FN(\$300) + TP(\$60) + FP(\$60) + TN(\$0) Let’s calculate the cost per customer using various thresholds (0.1, 0.2, 0.3,…,0.9, 1.0). After...

• ### Linear Programming and Discrete Optimization with Python using PuLP

...file, and experiment with various constraints to change your diet plan. The code is here in my Github repository. Finally, we can print the objective function i.e. cost of the diet in this case, obj = value(prob.objective) print("The total cost of this balanced diet is: \${}".format(round(obj,2)))...

https://www.kdnuggets.com/2019/05/linear-programming-discrete-optimization-python-pulp.html

• ### How do Neural Networks Learn?

...g the accuracy of their predictions. To do this, the network compares initial outputs with a provided correct answer, or target. A technique called a cost function is used to modify initial outputs based on the degree to which they differed from the target values. Finally, cost function results are...

https://www.kdnuggets.com/2015/12/how-do-neural-networks-learn.html

• ### Tips for a cost-effective machine learning project

...vice and what we want to deliver. Then, I define possible ways to achieve our objective. Finally, I zoom in on how we drastically reduced our compute cost using serverless functions.   Service anatomy   First the user fetches the static assets, then locally executes the JS that calls the...

https://www.kdnuggets.com/2019/11/tips-cost-effective-machine-learning-project.html

• ### 17 More Must-Know Data Science Interview Questions and Answers"> 17 More Must-Know Data Science Interview Questions and Answers

...ive. e.g. wrongly predicting a cancer patient to be cancer-free is more dangerous than wrongly predicting a cancer-free patient to have cancer. Total Cost = Cost of FN * Count of FN + Cost of FP * Count of FP Use of different sampling methods: In this approach, you can use over-sampling,...

• ### Must-Know: How to evaluate a binary classifier

...ive. e.g. wrongly predicting a cancer patient to be cancer-free is more dangerous than wrongly predicting a cancer-free patient to have cancer. Total Cost = Cost of FN * Count of FN + Cost of FP * Count of FP Use of different sampling methods: In this approach, you can use over-sampling,...

https://www.kdnuggets.com/2017/04/must-know-evaluate-binary-classifier.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

…orks. Cyclic Cosine Annealing Instead of manually trying to figure out when to dive into a local minima or when to jump out of it, the authors used a function to automate this process. They used Learning Rate Annealing with the following function: Simplified The formula may look complicated, but…

https://www.kdnuggets.com/2017/08/train-deep-learning-faster-snapshot-ensembling.html

• ### A Visual Explanation of the Back Propagation Algorithm for Neural Networks

...unterpart of minimizing a cost function via gradient descent). However, this is not specific to backpropagation but just one way to minimize a convex cost function (if there is only a global minima) or non-convex cost function (which has local minima like the "plateaus" that let us think we reached...

https://www.kdnuggets.com/2016/06/visual-explanation-backpropagation-algorithm-neural-networks.html

• ### Explainability: Cracking open the black box, Part 1

...the heart of almost any machine learning algorithm is an optimization problem that minimizes a cost function. In the case of Linear Regression, that cost function is Residual Sum of Squares, which is nothing but the squared error between the prediction and the ground truth parametrized by the...

https://www.kdnuggets.com/2019/12/explainability-black-box-part1.html

• ### A Guide to Decision Trees for Machine Learning and Data Science"> A Guide to Decision Trees for Machine Learning and Data Science

...eep us from wasting computations on testing out split points that are trivially poor. For a regression tree, we can use a simple squared error as our cost function: Where Y is our ground truth and Y-hat is our predicted value; we sum over all the samples in our dataset to get the total error. For a...

https://www.kdnuggets.com/2018/12/guide-decision-trees-machine-learning-data-science.html

• ### Data Visualization of Census Data with R

..._Cost<- merge (home_median _price,home_average_insurance,Lat_Long, by="State") # adding median home price and 13 years average insurance >Total_Cost\$Sum<- Total_Cost \$Median_Price+Total_Cost\$Average_Insurance # plottingdata on the US map >install.packages("ggmap")...

https://www.kdnuggets.com/2014/06/data-visualization-census-data-with-r.html

• ### Only Numpy: Implementing GANs and Adam Optimizer using Numpy"> Only Numpy: Implementing GANs and Adam Optimizer using Numpy

...ta (Generated By Generator Network) Line 162 — Cost Function of our Discriminator Network. Also, please take note of the Blue Box Region, that is our cost function. Lets compare the cost function from the original paper, shown below. Image from original Paper The difference is the fact that we are...

• ### What is Softmax Regression and How is it Related to Logistic Regression?

...abels are [0, 1, 2, 2]. Now, in order to train our logistic model (e.g., via an optimization algorithm such as gradient descent), we need to define a cost function J that we want to minimize: which is the average of all cross-entropies over our n training samples. The cross-entropy function is...

https://www.kdnuggets.com/2016/07/softmax-regression-related-logistic-regression.html

• ### 7 Ways to Get High-Quality Labeled Training Data at Low Cost

...ning data, as noted in this recent Medium post. As author Rasmus Rothe notes, there are other approaches that will produce labeled training data at a cost that won’t necessarily bust your data-science budget. What follows is my summary of these approaches: Repurposing existing training data and...

https://www.kdnuggets.com/2017/06/acquiring-quality-labeled-training-data.html

• ### Viewpoint: Why your company should NOT use “Big Data”

...with a future sale (or any other interim activities that could predict a future sale). You should be able to determine the value of a phone call as a function of the predicted value of the lead. Now you can use that (along with the cost of your sales people) to determine if you are over- or...

https://www.kdnuggets.com/2014/01/viewpoint-why-your-company-should-not-use-big-data.html

• ### From Insight-as-a-Service to Insightful Applications

...fy better insights (e.g., health care, security, marketing). Need to provide higher quality service that is consistent across all channels at a lower cost (e.g., financial services, manufacturing, logistics). In general, my thesis is that through insightful applications and a fixed amount of...

https://www.kdnuggets.com/2016/05/simoudis-insight-service-insightful-applications.html

• ### Teaching the Data Science Process

...notch products: A manufacturing process control product working out the asymmetric cost of false positives and false negatives using well-calibrated cost of maintenance, cost of production, satisfaction cost, and margin. The team showed improvement above several baselines (checking none, checking...

https://www.kdnuggets.com/2017/05/teaching-data-science-process.html

• ### Deep Learning on the Edge

...f the our edge devices (here, a node) fail, its neighbor can take over temporarily. This greatly ensures reliability and heavily reduces downtime. 6. Cost effective in the long run In the long run, cloud services will turn out to be more expensive than having a dedicated set of inference devices....

https://www.kdnuggets.com/2018/09/deep-learning-edge.html

• ### No order left behind; no shopper left idle.

...ur a lost deliveries cost. By minimizing these costs across all simulation runs, we solve for all x_h: N: number of simulation runs h: hour of day ld_cost: lost deliveries cost x_h: non-negative integer variables l_h,i: shoppers required at hour h in simulation i Final set of staffing levels The...

https://www.kdnuggets.com/2017/10/no-order-left-behind-shopper-left-idle.html

• ### Industry AI, Analytics, Machine Learning, Data Science Predictions for 2020

..., there's still one thing that keeps them from being accessible: pricing. In 2020, we'll begin to see affordable SaaS BI tools with similar power and functionality as tools that cost tens of thousands of dollars. That combination of Machine Learning capabilities and self-service functionality all...

https://www.kdnuggets.com/2019/12/predictions-ai-machine-learning-data-science-industry.html

• ### 2014 Jan: Analytics, Big Data, Data Mining and Data Science News

...ded education, mentorship from experts along with practical skills and techniques. Course starts Jan 19. Salford: Interpreting Model Performance with Cost Functions - Jan 13, 2014. Cost functions are critical for the correct assessment of performance of data mining and predictive models. This...

https://www.kdnuggets.com/2014/01/index-old.html

• ### KDnuggets™ News 14:n02, Jan 22

...ded education, mentorship from experts along with practical skills and techniques. Course starts Jan 19. Salford: Interpreting Model Performance with Cost Functions - Jan 13, 2014. Cost functions are critical for the correct assessment of performance of data mining and predictive models. This...

https://www.kdnuggets.com/2014/n02.html

• ### Machine Learning Trends and the Future of Artificial Intelligence

...arning at Microsoft. In the near-future, “every business is an algorithmic business.” This creates an algorithm economy, where algorithm marketplaces function as the global meeting place for researchers, engineers, and organizations to create, share, and remix algorithmic intelligence at scale. As...

https://www.kdnuggets.com/2016/06/machine-learning-trends-future-ai.html

• ### Data Preparation Tips, Tricks, and Tools: An Interview with the Insiders

...ting the collection, organization and preparation of enterprise-wide data (supplier, customer, product, financial, etc.) for the full range of spend, cost service and revenue analytics. Tamr’s machine-driven, expert-guided approach radically reduces the cost, time and effort of preparing data for...

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