# Tag: Regularization (19)

**4 ways to improve your TensorFlow model – key regularization techniques you need to know**- Aug 27, 2020.

Regularization techniques are crucial for preventing your models from overfitting and enables them perform better on your validation and test sets. This guide provides a thorough overview with code of four key approaches you can use for regularization in TensorFlow.**Batch Normalization in Deep Neural Networks**- Aug 7, 2020.

Batch normalization is a technique for training very deep neural networks that normalizes the contributions to a layer for every mini batch.**Getting Started with TensorFlow 2**- Jul 2, 2020.

Learn about the latest version of TensorFlow with this hands-on walk-through of implementing a classification problem with deep learning, how to plot it, and how to improve its results.**Applying Occam’s razor to Deep Learning**- Jan 10, 2020.

Finding a deep learning model to perform well is an exciting feat. But, might there be other -- less complex -- models that perform just as well for your application? A simple complexity measure based on the statistical physics concept of Cascading Periodic Spectral Ergodicity (cPSE) can help us be computationally efficient by considering the least complex during model selection.**Fighting Overfitting in Deep Learning**- Dec 27, 2019.

This post outlines an attack plan for fighting overfitting in neural networks.**Generalization in Neural Networks**- Nov 18, 2019.

When training a neural network in deep learning, its performance on processing new data is key. Improving the model's ability to generalize relies on preventing overfitting using these important methods.**Modeling Price with Regularized Linear Model & XGBoost**- May 2, 2019.

We are going to implement regularization techniques for linear regression of house pricing data. Our goal in price modeling is to model the pattern and ignore the noise.**Deep Compression: Optimization Techniques for Inference & Efficiency**- Mar 20, 2019.

We explain deep compression for improved inference efficiency, mobile applications, and regularization as technology cozies up to the physical limits of Moore's law.**KDnuggets™ News 18:n06, Feb 7: 5 Fantastic Practical Machine Learning Resources; 8 Must-Know Neural Network Architectures**- Feb 7, 2018.

5 Fantastic Practical Machine Learning Resources; The 8 Neural Network Architectures Machine Learning Researchers Need to Learn; Generalists Dominate Data Science; Avoid Overfitting with Regularization; Understanding Learning Rates and How It Improves Performance in Deep Learning**Avoid Overfitting with Regularization**- Feb 2, 2018.

This article explains overfitting which is one of the reasons for poor predictions for unseen samples. Also, regularization technique based on regression is presented by simple steps to make it clear how to avoid overfitting.**Regularization in Machine Learning**- Jan 10, 2018.

Regularization is a technique that helps to avoid overfitting and also make a predictive model more understandable.**Top 6 errors novice machine learning engineers make**- Oct 30, 2017.

What common mistakes beginners do when working on machine learning or data science projects? Here we present list of such most common errors.**The Top Predictive Analytics Pitfalls to Avoid**- Jan 23, 2017.

Predictive modelling and machine learning are significantly contributing to business, but they can be very sensitive to data and changes in it, which makes it very important to use proper techniques and avoid pitfalls in building data science models.**KDnuggets™ News 16:n23, Jun 29: Machine Learning Trends & Future of AI; Data Science Kaggle Walkthrough; Regularization in Logistic Regression**- Jun 29, 2016.

Machine Learning Trends and the Future of AI ; Doing Data Science: A Kaggle Walkthrough Part 6; Regularization in Logistic Regression; Top Machine Learning Libraries for Javascript.**Regularization in Logistic Regression: Better Fit and Better Generalization?**- Jun 24, 2016.

A discussion on regularization in logistic regression, and how its usage plays into better model fit and generalization.**21 Must-Know Data Science Interview Questions and Answers**- Feb 11, 2016.

KDnuggets Editors bring you the answers to 20 Questions to Detect Fake Data Scientists, including what is regularization, Data Scientists we admire, model validation, and more.**Deep Learning Adversarial Examples – Clarifying Misconceptions**- Jul 15, 2015.

Google scientist clarifies misconceptions and myths around Deep Learning Adversarial Examples, including: they do not occur in practice, Deep Learning is more vulnerable to them, they can be easily solved, and human brains make similar mistakes.**Top KDnuggets tweets, Jun 30 – Jul 06: Click Testing Proved that Beards Are Still A Thing; 16 Free #DataScience Books**- Jul 12, 2015.

How Screenshot Click Testing Proved that Beards Are Still A Thing; 16 Free #DataScience Books; How to avoid #Overfitting using #Regularization; #DataScience must read: quick puzzle tests your problem solving.**Data Science 101: Preventing Overfitting in Neural Networks**- Apr 17, 2015.

Overfitting is a major problem for Predictive Analytics and especially for Neural Networks. Here is an overview of key methods to avoid overfitting, including regularization (L2 and L1), Max norm constraints and Dropout.