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Machine learning tools and frameworks

Machine learning & Deep learning

Tensorflow

Define graph up front and then run it

  • Tensor: Multidimensional array that can hold data
  • Graph: Flow of data
import tensorflow as tf

Pytorch

Creating the graph as you go along. Can be more dynamic than TF.

import pytorch

MXNet

Framework of AWS Sagemaker, similar to pytorch you create graphs on the fly.

import mxnet as mx
from mxnet import nd #ndarray
from mxnet import autograd #autogradient

From Linux academy:

TensorFlow

TensorFlow (backed by Google) is an end-to-end, open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources. This lets researchers push the state-of-the-art in ML and developers easily build and deploy ML-powered applications.

(Source: https://www.tensorflow.org/)

Keras

Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.

(Source: https://keras.io/)

Or, to put it another way:

TensorFlow is a complex tool. Keras has been built on top of TensorFlow as a more user-friendly interface. It helps us rapidly prototype models, and we use it in this lab.