Keras is a Python library for deep learning that wraps the powerful numerical libraries Theano and TensorFlow. A difficult problem where traditional neural networks fall down is called object recognition. It is where a model is able to identify the objects in images. In this post, you will discover how to develop and evaluate deep […]

# Search results for "normalize"

## Image Augmentation for Deep Learning With Keras

Data preparation is required when working with neural network and deep learning models. Increasingly data augmentation is also required on more complex object recognition tasks. In this post you will discover how to use data preparation and data augmentation with your image datasets when developing and evaluating deep learning models in Python with Keras. After […]

## Handwritten Digit Recognition using Convolutional Neural Networks in Python with Keras

A popular demonstration of the capability of deep learning techniques is object recognition in image data. The “hello world” of object recognition for machine learning and deep learning is the MNIST dataset for handwritten digit recognition. In this post you will discover how to develop a deep learning model to achieve near state of the […]

## How To Prepare Your Data For Machine Learning in Python with Scikit-Learn

Many machine learning algorithms make assumptions about your data. It is often a very good idea to prepare your data in such way to best expose the structure of the problem to the machine learning algorithms that you intend to use. In this post you will discover how to prepare your data for machine learning […]

## Learning Vector Quantization for Machine Learning

A downside of K-Nearest Neighbors is that you need to hang on to your entire training dataset. The Learning Vector Quantization algorithm (or LVQ for short) is an artificial neural network algorithm that lets you choose how many training instances to hang onto and learns exactly what those instances should look like. In this post […]

## K-Nearest Neighbors for Machine Learning

In this post you will discover the k-Nearest Neighbors (KNN) algorithm for classification and regression. After reading this post you will know. The model representation used by KNN. How a model is learned using KNN (hint, it’s not). How to make predictions using KNN The many names for KNN including how different fields refer to […]

## Naive Bayes for Machine Learning

Naive Bayes is a simple but surprisingly powerful algorithm for predictive modeling. In this post you will discover the Naive Bayes algorithm for classification. After reading this post, you will know: The representation used by naive Bayes that is actually stored when a model is written to a file. How a learned model can be […]

## R Machine Learning Mini-Course

From Developer to Machine Learning Practitioner in 14 Days In this mini-course you will discover how you can get started, build accurate models and confidently complete predictive modeling machine learning projects using R in 14 days. This is a big and important post. You might want to bookmark it. Let’s get started. Who Is This […]

## Machine Learning Evaluation Metrics in R

What metrics can you use to evaluate your machine learning algorithms? In this post you will discover how you can evaluate your machine learning algorithms in R using a number of standard evaluation metrics. Let’s get started. Model Evaluation Metrics in R There are many different metrics that you can use to evaluate your machine […]

## Get Your Data Ready For Machine Learning in R with Pre-Processing

Preparing data is required to get the best results from machine learning algorithms. In this post you will discover how to transform your data in order to best expose its structure to machine learning algorithms in R using the caret package. You will work through 8 popular and powerful data transforms with recipes that you can […]