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 […]
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-art performance on […]
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 […]
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 […]
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 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 […]
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 […]
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 […]
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 […]
There are a ton of packages for R. Which ones are best to use for your machine learning project? In this post you will discover the exact R functions and packages recommended for each sub task in a machine learning journey. This is useful. Bookmark this page. I’m sure you will be checking back time […]
