Cheat Sheets #2: Deep Nearning — Tensorflow, Keras, Neural Network, Data Science and DASK
Learning Machine learning and Deep learning is difficult for newbies. As well as deep learning libraries are difficult to understand. I am creating this series with cheat sheets which I collected from different sources.
Over the past few months, totally redesigned the cheat sheets. The goal was to make them easy to read and beautiful so you will want to look at them, print them and share them.
Do read this and contribute cheat sheets if you have any. If you like this post, give it a ❤️! Here we go:
1. Neural Networks
2. TensorFlow
In May 2017 Google announced the second-generation of the TPU, as well as the availability of the TPUs in Google Compute Engine.[12] The second-generation TPUs deliver up to 180 teraflops of performance, and when organized into clusters of 64 TPUs provide up to 11.5 petaflops.
3. Keras
In 2017, Google’s TensorFlow team decided to support Keras in TensorFlow’s core library. Chollet explained that Keras was conceived to be an interface rather than an end-to-end machine-learning framework. It presents a higher-level, more intuitive set of abstractions that make it easy to configure neural networks regardless of the backend scientific computing library.
4. Dask
Source — http://docs.dask.org/en/latest/_downloads/daskcheatsheet.pdf
Source — http://docs.dask.org/en/latest/_downloads/daskcheatsheet.pdf
Source — http://docs.dask.org/en/latest/_downloads/daskcheatsheet.pdf
Source — http://docs.dask.org/en/latest/_downloads/daskcheatsheet.pdf
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Till then- Keep Learning, keep Sharing, keep Growing.
