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
The Most Influential Deep Learning Research of 2019
Deep learning has continued its forward movement during 2019 with advances in many exciting research areas like generative adversarial networks (GANs), auto-encoders, and reinforcement learning. In terms of deployments, deep learning is the darling of many contemporary application areas such as computer vision, image recognition, speech... Read more
Generate Websites with Deep Learning
This posting discusses how you can generate websites with deep learning. When it comes to software development, there are two types; one is the back-end the other is the front-end development. As the name suggests, back end development is the development that goes on behind the... Read more
Working Towards Planetary Scale Location Insights
Approaches for making geospatial imagery accessible to (geo)data scientists. This post discusses planetary scale location insights. Recent innovations in agile aerospace have created unique offerings in high cadence satellite imagery. While this is of immense interest to imagery analysts, a significant portion of GIS professionals and... Read more
Generative Adversarial Networks for Finance
This posting discusses generative adversarial network for finance. Financial instruments like options and futures have been around for more than two centuries. Although they became quite notorious during the 2008 stock market turmoil, they serve a real economic purpose for companies around the world. [Related article:... Read more
What You Need to Know about DeepMind’s BSuite
Imagine this. You know that reinforcement learning has been responsible for some of AI’s most significant advancements. You’re in the exploratory phase of implementing your first project. You’d love a way to evaluate whether your RL agent is appropriate for the task you have, something not... Read more
A Crash Course on Deep Learning in the Cloud
This posting is crash course on deep learning in the Cloud. Deep learning is the newest area of machine learning and has become ubiquitous in predictive modeling. The complex brain-like structure of deep learning models is used to find intricate patterns in large volumes of data.... Read more
Behavior Suite for Reinforcement Learning
A team from DeepMind Technologies—made up of Ian Osband, Yotam Doron, Matteo Hessel, John Aslanides, Eren Sezner, Andre Saraiva, Katrina McKinney, Tor Lattimore, Csaba Szepezvari, Satinder Singh, Benjamin Van Roy, Richard Sutton, David Silver, and Hado Van Hesselt—has recently published a piece on their new program... Read more
Sequence Modelling with Deep Learning
This is a short preview post for my upcoming tutorial  “Sequence Modelling with Deep Learning” at ODSC London in November 2019. — Much of data is sequential — think speech, text, DNA, stock prices, financial transactions, and customer action histories. Our best-performing methods for modelling sequence data use... Read more