Deep Learning Guides, Tutorials, Research, and Use-Cases

Keras Metrics: Everything You Need To Know

Deep Learning Modeling Keras Neural Networksposted by ODSC Community March 30, 2020

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

Blogs from ODSC Speakers Conferences Deep Learning Modeling East 2020 Neural Network VAE Variational Auto-Encodersposted by ODSC Community March 6, 2020

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

Deep Learning Modeling PersLay Topologyposted by Mathieu Carrière March 2, 2020

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

Deep Learning Modeling caffe frameworks Keras microsoft cognitive toolkit TensorFlowposted by ODSC Community February 24, 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

Deep Learning Modeling Neural Networks Reinforcement Learningposted by ODSC Community February 21, 2020

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

Blogs from ODSC Speakers Conferences Deep Learning Machine Learning Modeling East 2020posted by ODSC Community February 12, 2020

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

Blogs from ODSC Speakers Conferences Deep Learning Modeling East 2020 Neural Networkposted by Brandon Rohrer January 31, 2020

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

Data Science Academic Research Deep Learning Modeling deep reinforcement learning Reinforcement Learningposted by Daniel Gutierrez, ODSC January 15, 2020

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

Deep Learning Modeling cnn architecture Image Processingposted by ODSC Community January 9, 2020

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 Modeling 2020 Azure chainer Keras microsoft cognitive toolkit mxnet PyTorch Spark TensorFlowposted by Elizabeth Wallace, ODSC December 30, 2019

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