Keras Metrics: Everything You Need To Know
Keras metrics are functions that are used to evaluate the performance of your deep learning model. Choosing a good metric for your problem is usually a difficult task. you need to understand which metrics are already available in Keras and tf.keras and how to use them, in many... Read more
Variational Auto-Encoders for Customer Insight
Neural networks are sometimes perceived as super complicated. They’re not. The most attractive application, in my opinion, of neural networks for small and medium-sized businesses, is in customer segmentation, and in my upcoming workshop at ODSC East 2020, “Variational Auto-Encoders for Customer Insight,” I will show... Read more
Mixing Topology and Deep Learning with PersLay
In a former post, I presented Topological Data Analysis and its main descriptor, the so-called persistence diagram. In this post, I would like to show how these descriptors can be combined with neural networks, opening the way to applications based upon both deep learning and topology! What are persistence diagrams?... Read more
5 Deep Learning Frameworks to Consider for 2020
Enough of flirting with deep learning and deep learning frameworks; it’s time to glide across the room and say, “Hello.” Call it an advanced subfield of machine learning or future to enhanced vision in the field of technology, deep learning won’t stop now!  Imbibed in the... Read more
Deep Q-Learning Algorithm in Reinforcement Learning
In this article, we will discuss Q-learning in conjunction with neural networks (NNs). This combination has the name deep Q-network (DQN). This article is an excerpt from the book Deep Reinforcement Learning Hands-on, Second Edition by Max Lapan. This book provides you with an introduction to the... Read more
Inversion of 2D Remote Sensing Data to 3D Volumetric Models Using Deep Dimensionality Exchange
By Graham Ganssle, PhD, Head of Data Science, Expero Inc. Be sure to check out his upcoming talk at ODSC East 2020 this April 13-17, “Inversion of 2D Remote Sensing Data to 3D Volumetric Models Using Deep Dimensionality Exchange,” there! Many companies are continuously exploring for and monitoring the... Read more
Build a First Neural Network
Neural networks are weirdly good at translating languages and identifying dogs by breed, but they can be intimidating to get started with. In an effort to smooth this on-ramp, I created a neural network framework specifically for teaching and experimentation. It’s called Cottonwood and this notebook... Read more
Best Deep Reinforcement Learning Research of 2019
Since my mid-2019 report on the state of deep reinforcement learning (DRL) research, much has happened to accelerate the field further. Read my previous article for a bit of background, brief overview of the technology, comprehensive survey paper reference, along with some of the best research... Read more
Using the CNN Architecture in Image Processing
This post discusses using CNN architecture in image processing. Convolutional Neural Networks (CNNs) leverage spatial information, and they are therefore well suited for classifying images. These networks use an ad hoc architecture inspired by biological data taken from physiological experiments performed on the visual cortex. Our... Read more
Deep Learning Frameworks You Need to Know in 2020
Deep learning networks have a mind-boggling ability to learn, so training these models requires massive computing power and intense amounts of data. You’ll need a framework to make that development easier. Deep learning requires massive processing power and lots of data. Because it uses unstructured, often non-text... Read more