The XGBoost library provides an implementation of gradient boosting designed for speed and performance. It is implemented to make best use of your computing resources, including all CPU cores and memory. In this post you will discover how you can setup a server on Amazon’s cloud service to quickly and cheaply create very large models. After […]
Data preparation is the transformation of raw data into a form that is more appropriate for modeling. It is a challenging topic to discuss as the data differs in form, type, and structure from project to project. Nevertheless, there are common data preparation tasks across projects. It is a huge field of study and goes […]
Developing machine learning models in Python often requires the use of NumPy arrays. NumPy arrays are efficient data structures for working with data in Python, and machine learning models like those in the scikit-learn library, and deep learning models like those in the Keras library, expect input data in the format of NumPy arrays and […]
Generative Adversarial Networks with Python Deep Learning Generative Models for Image Synthesis and Image Translation …so, What are Generative Adversarial Networks? Generative Adversarial Networks, or GANs, are a deep-learning-based generative model. More generally, GANs are a model architecture for training a generative model, and it is most common to use deep learning models in this […]
Deep Learning for Computer Vision Image Classification, Object Detection, and Face Recognition in Python …why deep learning? Traditionally, Computer Vision is REALLY HARD We are awash in images: photographs, videos, YouTube, Instagram, and increasingly from live video. Computer Vision, often shortened to CV, is defined as a field of study that seeks to develop techniques […]
Deep learning neural networks are relatively straightforward to define and train given the wide adoption of open source libraries. Nevertheless, neural networks remain challenging to configure and train. In his 2012 paper titled “Practical Recommendations for Gradient-Based Training of Deep Architectures” published as a preprint and a chapter of the popular 2012 book “Neural Networks: […]
XGBoost With Python Discover The Algorithm That Is Winning Machine Learning Competitions Why Is XGBoost So Powerful? … the secret is its “speed” and “model performance” The Gradient Boosting algorithm has been around since 1999. So why is it so popular right now? The reason is that we now have machines fast enough and enough […]
Microsoft recently launched support for machine learning in their Azure cloud computing platform. Buried in some of their technical documentation for the platform are some resources that you may find useful for thinking about what machine learning algorithm to use in different situations. In this post we take a look at the Microsoft recommendations for […]
Machine Learning has been commoditized into a service. This is a recent trend that looks like it will develop into the mainstream like commoditized storage and virtualization. It is the natural next step. In this review you will learn about BigML that provides commoditized machine learning as a service for business analysts and application integration. […]
Running large deep learning processes on Amazon Web Services EC2 is a cheap and effective way to learn and develop models. For just a few dollars you can get access to tens of gigabytes of RAM, tens of CPU cores, and multiple GPUs. I highly recommend it. If you are new to EC2 or the […]
