Digital assistants built with machine learning solutions are gaining their momentum. At TensorBeat 2017, one of the sessions covered how to deliver an answer bot with Keras and TensorFlow, what tools may help to address the issues, as well as tips on training a model and improving prediction results.
Though practical usage of TensorFlow within finance is still in its germ phase, the scenarios are already quite a few. The fireside chat at TensorBeat 2017 discussed how the world of finance can drive value and improve customer experience from employing TensorFlow-based solutions.
This blog post highlights things-to-know while enabling reinforcement learning with TensorFlow, as discussed at one of the sessions at TensorBeat 2017. You will find out what toolkit simplifies the work done within an environment, how to handle pitfalls of distributed learning, boost performance across multiple environments, etc.
At TensorFlow Dev Summit 2017, Google’s Ashish Agarwal introduced a TensorFlow-based toolkit of machine learning algorithms. The toolkit provides out-of-the-box packed solutions to enable researchers and developers to create high-level custom model architectures.
Optical character recognition (OCR) drives the conversion of typed, handwritten, or printed symbols into machine-encoded text. However, the OCR process brings the need to eliminate possible errors, while extracting only valuable data from ever-growing amount of it.
At the recent TensorFlow meetup, the attendees learnt how employing the one-shot attention mechanism for token extraction in Keras using TensorFlow as a back end can help out. In addition, the meetup discussed how to enable multilingual neural machine translation with TensorFlow.
At the recent Tensorflow meetup in Washington DC, the attendees learnt how TensorFlow can help in automating wildfire detection/prediction, as well as what’s underlying the TensorFlow four core concepts.
Convolutional neural networks (CNN) solve a variety of tasks related to image/speach recognition, text analysis, etc. These topics were discussed at a recent Dallas TensorFlow meetup—organized and sponsored by Altoros. The sessions demonstrated how CNN can foster deep learning with TensorFlow in the context of image recognition. The examples featured MNIST, a large data set of handwritten digits, and Word2vec, a group of models used to generate word embeddings.
Watch the videos below for more detail.
Below are the videos from the Boston TensorFlow meetup—sponsored and organized by Altoros on March 7, 2016.
Day 1 | Day 2 | Top 100 Quotes
This year, the Cloud Foundry Summit introduced a stronger focus on community diversity and closer collaboration between its ecosystem members. New partnerships, integrations, support for a broader technology stack, etc. Just like a year ago, we recap what was happening this Monday at the Santa Clara Convention Center, which was themed in the cyberpunk style.
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