Running Keras using Cygwin - How to make your Operation Team Proud
Several weeks ago one of our business unit members told me that “life can be more convenient” if they will be able to run our DL engines on their servers. The notion “servers” means running on Cygwin engines where python is not always installed and if it is, the version is unknown and obviously no packages such as Keras or Pytorch can be installed (due to inner constrain). Ideally speaking from his side was having a shell tool that performs DL tasks. At that time I knew well how Torch or Keras work when I use them on my PC, but taking a models and “Cygwin” them? I knew nothing about. Probably some of the readers are claiming now, “well it is trivial” they are right. However, when I began to work on it I found that there are many websites that discuss many of the actions but nothing is an end to end description. This is the motivation of this post.
This video on TensorFlow and Keras tutorial will help you understand Deep Learning frameworks, what is TensorFlow, TensorFlow features and applications, how TensorFlow works, TensorFlow 1.0 vs TensorFlow 2.0, TensorFlow architecture with a demo. Then we will move into understanding what is Keras, models offered in Keras, what are neural networks and they work.
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We will go over what is the difference between pytorch, tensorflow and keras in this video. Pytorch and Tensorflow are two most popular deep learning frameworks. Pytorch is by facebook and Tensorflow is by Google. Keras is not a full fledge deep learning framework, it is just a wrapper around Tensorflow that provides some convenient APIs.
This video explains four reasons why deep learning has become so popular in past few years. In this deep learning tutorial python, I will cover following things in this video: Introduction; Data growth; Hardware advancements; Python and opensource ecosystem; Cloud and AI boom
Keras with TensorFlow Course - Python Deep Learning and Neural Networks for Beginners Tutorial: How to use Keras, a neural network API written in Python and integrated with TensorFlow. We will learn how to prepare and process data for artificial neural networks, build and train artificial neural networks from scratch, build and train convolutional neural networks (CNNs), implement fine-tuning and transfer learning, and more!