This talk will explain practical deep learning with tensorflow. No theory, just implementation.
All steps for implementing a model will be explained using good patterns.
The talk will introduce a split into model, loss, dataset and estimator that keeps your code clean and easy to understand. For each of those 4 topics details how to implement it in an efficient, reusable way are explained.
The talk will assume basic knowledge about deep learning, since it will focus on implementation and no theory.
You will learn:
* Writing good quality deep learning code
* Implementing a model from scratch
* Implementing a loss
* Loading a dataset
* Training a model
You will *not* learn:
* What to use Deep Learning for?
* What is a CNN, GAN, RNN, etc.?
* That latest super weird feature of tensorflow.
One more thing:
"The answer to the ultimate question of life, the universe and everything is 42." [The Hitchhiker's Guide to the Galaxy]