Wide & Deep Learning: Better Together with TensorFlow
Posted by Heng-Tze Cheng, Senior Software Engineer, Google Research The human brain is a sophisticated learning machine, forming rules by memorizing everyday events (“sparrows can fly” and “pigeons can fly”) and generalizing those learnings to apply to things we haven't seen before (“animals with wings can fly”). Perhaps more powerfully, memorization also allows us to further refine our generalize
Google Launches Deep Learning with TensorFlow MOOC - KDnuggets
Google Launches Deep Learning with TensorFlow MOOC Google and Udacity have partnered for a new self-paced course on deep learning and TensorFlow, starting immediately. Deep learning is still everywhere, and its prevalence is steadily growing. Much of the current excitement surrounding machine learning is deep neural network-based, and practitioners and researchers alike are attracted to the subfie
Deep Learning | Udacity
Dive into deep learning with this practical course on TensorFlow and the Keras API. Gain an intuitive understanding of neural networks without the dense jargon. Learn to build, train, and optimize your own networks using TensorFlow. The course also introduces transfer learning, leveraging pre-trained models for enhanced performance. Designed for swift proficiency, this course prioritizes hands-on
The Unreasonable Reputation of Neural Networks | [ thinking machines ]
January 12, 2016It is hard not to be enamoured by deep learning nowadays, watching neural networks show off their endless accumulation of new tricks. There are, as I see it, at least two good reasons to be impressed: (1) Neural networks can learn to model many natural functions well, from weak priors. The idea of marrying hierarchical, distributed representations with fast, GPU-optimised gradient
![The Unreasonable Reputation of Neural Networks | [ thinking machines ]](https://cdn-ak-scissors.b.st-hatena.com/image/square/9c8660cede6e159c71f470b07839280f969d0c49/height=288;version=1;width=512/http%3A%2F%2Fweb.mit.edu%2Flbh%2Fwww%2Ftree51.png)
Reddit - Dive into anything
Hi everyone! Was wondering what you guys thought about implementing distributed deep learning using erlang. Basically the deep neural network would be modelled with each neuron acting as a separate process. Since erlang allows millions of processes and also scales across machines, this method could be used to potentially model large neural networks. Thoughts?
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