Smart Image Analysis for Omnichannel Retail Applications
Editor’s note: Abon is a speaker for ODSC West this Fall! Consider attending his talk, “Computer Vision for E-Commerce: Intelligent Analysis and Selection of Product Images at Scale” then. In retail, the role of product images is critical in delivering satisfactory customer experience. Images help online... Read more
Exploring the Deep Learning Framework PyTorch
There are a variety of open-source deep learning frameworks to choose from including Keras, TensorFlow, Caffe2, and MXNet among others. At ODSC West in 2018, Stephanie Kim, a developer at Algorithmia, gave a great talk introducing the deep learning framework PyTorch. Primarily developed by Facebook, PyTorch... Read more
Watch: Deep Learning in Real Time
Deep learning provides (relatively) new methods of automating a wide array of tasks historically thought only to be accomplished by the human brain. Applications range from difficult classifications and regressions to natural language understanding and image recognition. With Tyler Freckmann, we will take a tour of... Read more
Deep Learning for Speech Recognition
Deep learning is well known for its applicability in image recognition, but another key use of the technology is in speech recognition employed to say Amazon’s Alexa or texting with voice recognition. The advantage of deep learning for speech recognition stems from the flexibility and predicting... Read more
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... 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... 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... 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... 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... 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.... Read more