Don’t Learn TensorFlow! Start with Keras or PyTorch Instead

Don’t Learn TensorFlow! Start with Keras or PyTorch Instead

  • June 29, 2018
Table of Contents

Don’t Learn TensorFlow! Start with Keras or PyTorch Instead

So, you want to learn deep learning? Whether you want to start applying it to your business, base your next side project on it, or simply gain marketable skills – picking the right deep learning framework to learn is the essential first step towards reaching your goal. We strongly recommend that you pick either Keras or PyTorch.

These are powerful tools that are enjoyable to learn and experiment with. We know them both from the teacher’s and the student’s perspective. Piotr has delivered corporate workshops on both, while Rafał is currently learning them.

Keras and PyTorch are open-source frameworks for deep learning gaining much popularity among data scientists. Keras is a high-level API capable of running on top of TensorFlow, CNTK, Theano, or MXNet (or as tf.contrib within TensorFlow). Since its initial release in March 2015, it has gained favor for its ease of use and syntactic simplicity, facilitating fast development.

It’s supported by Google. PyTorch, released in October 2016, is a lower-level API focused on direct work with array expressions. It has gained immense interest in the last year, becoming a preferred solution for academic research, and applications of deep learning requiring optimizing custom expressions.

It’s supported by Facebook. Keras is a high-level API capable of running on top of TensorFlow, CNTK, Theano, or MXNet (or as tf.contrib within TensorFlow). Since its initial release in March 2015, it has gained favor for its ease of use and syntactic simplicity, facilitating fast development.

It’s supported by Google. PyTorch, released in October 2016, is a lower-level API focused on direct work with array expressions. It has gained immense interest in the last year, becoming a preferred solution for academic research, and applications of deep learning requiring optimizing custom expressions.

It’s supported by Facebook.

Source: deepsense.ai

Tags :
Share :
comments powered by Disqus

Related Posts

Attacks against machine learning – an overview

Attacks against machine learning – an overview

At a high level, attacks against classifiers can be broken down into three types: Adversarial inputs, which are specially crafted inputs that have been developed with the aim of being reliably misclassified in order to evade detection. Adversarial inputs include malicious documents designed to evade antivirus, and emails attempting to evade spam filters. Data poisoning attacks, which involve feeding training adversarial data to the classifier.

Read More
Neural scene representation and rendering

Neural scene representation and rendering

There is more than meets the eye when it comes to how we understand a visual scene: our brains draw on prior knowledge to reason and to make inferences that go far beyond the patterns of light that hit our retinas. For example, when entering a room for the first time, you instantly recognise the items it contains and where they are positioned. If you see three legs of a table, you will infer that there is probably a fourth leg with the same shape and colour hidden from view.

Read More
Learn Reinforcement Learning from scratch

Learn Reinforcement Learning from scratch

Deep RL is a field that has seen vast amounts of research interest, including learning to play Atari games, beating pro players at Dota 2, and defeating Go champions. Contrary to many classical Deep Learning problems that often focus on perception (does this image contain a stop sign?) , Deep RL adds the dimension of actions that influence the environment (what is the goal, and how do I get there?).

Read More