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Neural Network Optimization
This article is the third in a series of articles aimed at demystifying neural networks and outlining how to design and implement them. In this article, I will discuss the following concepts related to the optimization of neural networks: Challenges with optimization Momentum Adaptive Learning Rates Parameter Initialization Batch... Read more
Deep Learning Talks Coming to the ODSC Virtual Conference April 14-17
Deep learning is an increasingly important of any data scientist’s skillset and any company’s strategies to get ahead. This practice requires a broad knowledge of frameworks, libraries, and algorithms, ranging from neural networks to knowing about hardware capabilities. To help any data scientist stay up-to-date with DL, here are... Read more
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 situations you need... Read more
Variational Auto-Encoders for Customer Insight
Github repository: VAEs-in-Economics 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? Briefly, a persistence... 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 majority of business... 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 fundamentals of RL,... 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 stability of CO2... 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 shows how to... 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 papers at that... Read more