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Martin Görner

28 Jun, 4 tweets, 2 min read

@lak_gcp

I made a ton of ML architecture illustrations for an upcoming book. Starting with good old Alex Net

The book: oreilly.com/library/view/p… by @lak_gcp, Ryan Gillard and myself.

and just as good and old VGG19:

Here is a SqueezeNet module.The pape rcalls them "fire
🔥 modules"

And an example of a full SqueezeNet architecture:

I like SqueezeNet for quick prototyping when I want to write all the code. It's quick to write in Keras.

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Martin Görner

@martin_gorner

28 Nov 19

@fchollet

Now reading the ARC paper by @fchollet.
arxiv.org/abs/1911.01547 “On the measure of intelligence” where he proposes a new benchmark for “intelligence” called the “Abstraction and Reasoning corpus”.
Highlights below ->

@fchollet

@fchollet Chess was considered the pinnacle of human intelligence, … until it was solved by a computer and surpassed Garry Kasparov in 1997. Today, it is hard to argue that a min-max algorithm with optimizations represents “intelligence”.

@fchollet

@fchollet AlphaGo took this to the next step. It became world champion at Go by using deep learning. Still, the program is narrowly focused on playing Go and solving this task did not lead to breakthroughs in other fields.

Read 32 tweets

Martin Görner

@martin_gorner

13 Sep 18

Google Cloud Platform now has preconfigured deep learning images with Tensorflow, PyTorch, Jupyter, Cuda and CuDNN already installed. It took me some time to figure out how to start Jupyter on such an instance. Turns out it's a one liner:

Detailed instructions:
1) Go to cloud.google.com/console and create an instance (pick the Tensorflow deep learning image and a powerful GPU)

2) Ssh into your instance using the "gcloud compute ssh" command in the pic (there will be additional install prompts to accept and a reboot on the first connection. Relaunch the command after that to reconnect). Replace PROJECT_NAME and INSTANCE_NAME with your own values.

Read 7 tweets

Martin Görner

@martin_gorner

19 Jan 17

I believe a dev can get up to speed on neural networks in 3h and then learn by himself. Ready for a crash course? /1

Got 3 more hours ? The "Tensorflow without a PhD" series continues. First a deep dive into modern convolutional architectures: .

This session walks you through the construction of a neural network that can spot airplanes in aerial imagery. A good place to start for software devs who know some basics (relu, softmax, ...) and want to see a real model built from scratch.

Read 8 tweets

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Martin Görner

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