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.
In his session at Google Cloud Next 2017, Yufeng Guo, a developer advocate for Google, focused on the steps to take if you want to incorporate a machine learning model with an Android-based mobile device.
Find out the tricks behind training and optimizing a model for mobile to recognize images.
The recent TensorFlow webinar focused on behind-the-scenes mechanisms of text prediction. In addition to using TensorFlow and long short-term memory networks for the purpose, the attendees learnt about two word2vec models for generating word embeddings, their concept differences and employment.
TensorFlow and neural networks are actively used to perform image recognition and classification. At the recent TensorFlow meetup, attendees learnt how these technologies can be employed to enable a machine to recognize what is depicted in the image and to deliver a caption for it. In addition, an insightful overview of using TensorBoard was provided.
What is the magic that makes Google products so powerful? At TensorFlow Dev Summit 2017, the attendees learnt about the sequence-to-sequence models that back up language-processing apps like Google Translate. This recap explains what it takes to read and batch sequence data, as well as which of the TensorFlow-based tools enable fully dynamic calculations.
Written in Python, Keras is a high-level neural networks API that can be run on top of TensorFlow. This API was designed to provide machine learning enthusiasts with a tool that enables easy and fast prototyping, supports both convolutional and recurrent neural networks (and a combination of the two), while running on a CPU or GPU.
At the recent webinar, the attendees learned how to build an image classifier from scratch using Keras on top of TensorFlow, how containerization can help, how to fight data overfitting and reach 90% of accuracy, 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.
At TensorFlow Dev Summit 2017, Jonathan Hseu of the Google Brain team elaborated on how to integrate TensorFlow with your infrastructure. The three major steps to take can be broadly divided into three high-level perspectives: data preparation, training, and serving in production.
With all of the three steps outlined, this blog post highlights nuances to consider: the perks of distributed training, how containers help out, why input / output file formats matter, etc.
TensorFlow Dev Summit 2017 brought together deep / machine learning enthusiasts to share their experience and breakthroughs. At the session delivered by Douglas Eck of the Google Brain team, the attendees learned how TensorFlow-based Magenta project can facilitate music generation and why output evaluation is critical.
