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Using Mobile Devices for Deep Learning
A key avenue for deploying deep learning models is a mobile device. The advantage of running models in mobile apps instead of sending them to the cloud is the reduction in latency and the ability to ensure data privacy for users. Despite the variety of deep learning libraries and... Read more
How to Leverage Pre-Trained Layers in Image Classification
Deep learning models like convolutional neural networks (ConvNet) require large amounts of data to make accurate predictions. In general, sufficient sample size for a ConvNet application would involve tens of thousands of images. Often, only a few thousand labeled images are available for training, validation, and testing. Challenges associated... Read more
Using RAPIDS with PyTorch
In this post we take a look at how to use cuDF, the RAPIDS dataframe library, to do some of the preprocessing steps required to get the mortgage data in a format that PyTorch can process so that we can explore the performance of deep learning on tabular data and... Read more
Visualizing Your Convolutional Neural Network Predictions With Saliency Maps
In many cases, understanding why the model predicted a given outcome is a key detail for model users and a necessary diagnostic to insure your model makes decisions based on the correct features. For example, if you built a convolutional neural network that performed well at predicting damaged products... Read more
Deep Learning Research in 2019: Part 2
The deep learning revolution has continued to expand in 2019, affecting a wide range of fields from neuroscience to social media and more. In practical as well as theoretical applications, deep learning is growing more advanced and more influential. Below are some of the most interesting research papers published... Read more
Watch: Applications of Deep Learning in Aerospace
Recent advances in machine learning techniques such as deep learning (DL) have rejuvenated data-driven analysis in aerospace and integrated building systems. DL algorithms have been successful due to the presence of large volumes of data and its ability to learn the features during the learning process. The performance improvement... Read more
How to Choose Machine Learning or Deep Learning for Your Business
AI is the future, or so you’re hearing. Every day, news of another organization leveraging AI to produce business outcomes that outstrip competition hit your inbox, but your company either hasn’t started at all or is mired in the discussion. AI, machine learning, and deep learning are sometimes used... Read more
Come See Our Talk on MATLAB and TensorFlow: 3 Ways to Enhance TensorFlow with MATLAB
Shounak Mitra, MathWorks’ Product Manager for Deep Learning Toolbox, will be presenting “everything but the training” at ODSC on Thursday, May 2nd at 2 PM in Room 202. Here are some of the highlights of the talk and why you should attend. In AI and deep learning workflows, a... Read more
ODSC East DeepOps: Building an AI First Company
I’ve spoken to over a hundred AI companies as part of my job at MissingLink.ai and the result of analyzing their experiment, data and compute workflows. Certain challenges were a common theme across many teams – the solutions to these is a concept we call “DeepOps”, deep learning operations.... Read more
How To Make Your Deep Learning Process More Secure
Threats to security evolve with each new technology. History shows us this. Now that deep learning is on the rise, unique threats that both use and exploit deep learning paradigms are gaining traction. If your organization is involved in deep learning, the threats are going to change. Here’s how... Read more