The Houston Data Science Meetup will be presenting a four-part introduction to neural networks and deep learning over the next couple months, led by Roy.Register to Attend
The meetup schedule is as follows:
Part 1: The basics of neural networks (16 Nov 2016)
Part 2: From shallow networks to deep networks (29 Nov 2016)
Part 3: Common deep learning networks (10 Apr 2017)
Part 4: Deep neural networks with Python and Keras (TBA)
The sessions will build on each other, though we hope each one will be worthwhile even if you cannot attend the whole sequence.
Part 3: Common deep learning networks
In part 3 of our series, we will look at commonly used network architectures and the problem domains they address. We will discuss convolutional neural networks and recurrent neural networks in detail.
This material will be drawn primarily from Chapters 6 of Michael Nielsen’s book “Neural Networks and Deep Learning” and Andrej Karpathy’s blog post, “The Unreasonable Effectiveness of Recurrent Neural Networks“.
Slides from Part 1: The basics of neural networks
Notebook from Part 1
Slides from Part 2: From shallow networks to deep networks
Lightning Talks: We are looking to have a 5-10 minute presentation to kick start the meetup. Message Ted or Roy if you have a topic you would like to present.
Finding Station Houston: Located at the corner of Polk and Fannin St. on the 24th floor. There is usually one door open past 6 p.m. If you are locked outside, leave a message below and someone will let you in.
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