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.
At the recent Dallas Predix meetup, the audience learnt how GE Predix is different from Cloud Foundry, IBM Bluemix, and other platforms. Embedded solutions for IoT with a focus on wireless / sensor technologies were also under discussion. Watch the video and slides from the meetup below.
According to Cisco, IoT will increase private sector profits by 21% and add $19 trillion to the global economy by 2020. At the recent Houston Predix meetup, the attendees were plunged into a brief overview of the Industrial Internet (IIoT) market.
A number of the common challenges faced by the companies from different industry domains were explored, as well as the ways to solve them. Some figures were also revealed, demonstrating how Industrial Internet technologies help to cut down on costs, decrease the crime rate, improve transportation experience, etc.
Gartner estimates that 20.8 billion connected devices will be in use worldwide by 2020. Approximately 5.5 million new “things” are get connected every day. So, IoT can be a real pain.
A recent Milwaukee Predix meetup focused on the challenges IoT brings along and how relying on a platform like Predix can help to solve them. From this blog post, you will also find out what the portability concerns are, when migrating apps to a platform.
Watch the videos below for more details.
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.
