- Data Science 101: Normalization, Standardization, and Regularization - Apr 20, 2021.
Normalization, standardization, and regularization all sound similar. However, each plays a unique role in your data preparation and model building process, so you must know when and how to use these important procedures.
Data Preprocessing, Feature Engineering, Normalization, Regression, Regularization, Statistics
- Popular Machine Learning Interview Questions, part 2 - Jan 27, 2021.
Get ready for your next job interview requiring domain knowledge in machine learning with answers to these thirteen common questions.
Convolutional Neural Networks, Interview Questions, Linear Regression, Logistic Regression, Machine Learning, Regularization, Transfer Learning, Unbalanced
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.
Machine Learning, Overfitting, Regularization, 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.
Deep Learning, Neural Networks, Normalization, Regularization
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.
Advice, Beginners, Deep Learning, Python, Regularization, TensorFlow
- 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.
Complexity, Deep Learning, Model Performance, Regularization
- Fighting Overfitting in Deep Learning - Dec 27, 2019.
This post outlines an attack plan for fighting overfitting in neural networks.
Deep Learning, Keras, Neural Networks, Overfitting, Python, Regularization, Transfer Learning
- 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.
Complexity, Deep Learning, Dropout, Neural Networks, Overfitting, Regularization, Training Data
- 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.
Modeling, Python, Regularization, XGBoost
- 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.
Compression, Convolutional Neural Networks, Deep Learning, ICLR, Inference, Optimization, Regularization
- Regularization in Machine Learning - Jan 10, 2018.
Regularization is a technique that helps to avoid overfitting and also make a predictive model more understandable.
Machine Learning, Overfitting, Regularization
- 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.
Beginners, Machine Learning, Mistakes, Outliers, Regression, Regularization, Time Series
- 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.
Bias, Machine Learning, Model Performance, Predictive Analytics, Regularization, Statistics
- 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.
Cost Function, Logistic Regression, Machine Learning, Regression, Regularization
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.
Pages: 1 2 3
Bootstrap sampling, Data Science, Interview Questions, Kirk D. Borne, Precision, Recall, Regularization, Yann LeCun
- 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.
Adversarial, Deep Learning, Ian Goodfellow, Myths, Regularization
- 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.
Pages: 1 2
Neural Networks, Nikhil Buduma, Overfitting, Regularization
