There are several differences between these two frameworks. Keras is a neural network library while TensorFlow is the open-source library for a number of various tasks in machine learning. TensorFlow provides both high-level and low-level APIs while Keras provides only high-level APIs.

TensorFlow is an open-sourced end-to-end platform, a library for multiple machine learning tasks, while Keras is a high-level neural network library that runs on top of TensorFlow. Both provide high-level APIs used for easily building and training models, but Keras is more user-friendly because it’s built-in Python.

Keras and PyTorch are two of the most powerful open-source machine learning libraries….Related Articles.

Keras is a high-level API capable of running on top of TensorFlow, CNTK and Theano. TensorFlow is a framework that provides both high and low level APIs. Pytorch, on the other hand, is a lower-level API focused on direct work with array expressions.

Easy to learn PyTorch is comparatively easier to learn than other deep learning frameworks. This is because its syntax and application are similar to many conventional programming languages like Python. PyTorch’s documentation is also very organized and helpful for beginners.

Both TensorFlow and PyTorch have their advantages as starting platforms to get into neural network programming. Traditionally, researchers and Python enthusiasts have preferred PyTorch, while TensorFlow has long been the favored option for building large scale deep learning models for use in production.

TensorFlow has adopted PyTorch innovations and PyTorch has adopted TensorFlow innovations. Notably, now both languages can run in a dynamic eager execution mode or a static graph mode. Both frameworks are open source, but PyTorch is Facebook’s baby and TensorFlow is Google’s baby.

Here’s a link to PyTorch’s open source repository on GitHub. Uber Technologies, 9GAG, and StyleShare Inc. are some of the popular companies that use TensorFlow, whereas PyTorch is used by Suggestic, cotobox, and Depop.

PyTorch allows quicker prototyping than TensorFlow, but TensorFlow may be a better option if custom features are needed in the neural network.

PyTorch is specifically designed to accelerate the path from research prototyping to product development. Even Tesla is using PyTorch to develop full self-driving capabilities for its vehicles, including AutoPilot and Smart Summon.

What makes TensorFlow really, really slow is traning large models on only a CPU, because unfortunately, your CPU just isn’t optimized for that, whereas your GPU is. TensorFlow is a framework.

Companies Currently Using PyTorch

Starting today, you can easily train and deploy your PyTorch deep learning models in Amazon SageMaker.

It is easy to learn, use, extend, and debug. Great API: PyTorch shines in term of usability due to better designed Object Oriented classes which encapsulate all of the important data choices along with the choice of model architecture. The documentation of PyTorch is also very brilliant and helpful for beginners.

Using Amazon SageMaker and TensorFlow, Aerobotics was able to improve their training speed by 24 times per sample. Fannie Mae uses Amazon SageMaker with TensorFlow for its home appraisal model to generate accurate property valuations, which helps reduce mortgage risk.

AWS Sagemaker has been a great deal for most data scientists who would want to accomplish a truly end-to-end ML solution. It takes care of abstracting a ton of software development skills necessary to accomplish the task while still being highly effective and flexible and cost-effective.

An Amazon Machine Image (AMI) provides the information required to launch an instance. You must specify an AMI when you launch an instance. You can launch multiple instances from a single AMI when you need multiple instances with the same configuration.

Amazon Personalize is a fully managed machine learning service that goes beyond rigid static rule based recommendation systems and trains, tunes, and deploys custom ML models to deliver highly customized recommendations to customers across industries such as retail and media and entertainment.

Instead, Amazon devised an algorithm that began looking at items themselves. It scopes recommendations through the user’s purchased or rated items and pairs them to similar items, using metrics and composing a list of recommendations. That algorithm is called “item-based collaborative filtering.”

1 : personify. 2 : to make personal or individual specifically : to mark as the property of a particular person personalized stationery.

A9 Algorithm

When Amazon search results are sorted by price low to high, the results are limited to 7 pages 99 percent of the time. Why? Because Amazon wants people that sort L2H, to stop doing as such because the best deals are the lowest priced, and most of the lowest priced items are not Amazon Prime items.

Top 100 Amazon searches in the US

Amazon’s recommendation technology is based on collaborative filtering, which means it decides what it thinks you want by building up a picture of who you are, then offering you products that people with similar profiles have purchased. Amazon gathers data on every one of its customers while they use the site.

You need a copy of an government issued ID that shows the date of birth. You can’t ask for it at Amazon. Amazon will suspend you for this immediately without asking for any plan of action. Their business is simple: they sell and you ship.

Search queries — When you search for items, Amazon keeps track of what you searched, when you searched it, and which products you ended up viewing based on those searches. In fact, if you revisit any item you’ve purchased in the past, Amazon will tell you when you bought it.

Amazon gathers individual data on each and every one of its customers while they use the website. Big Data has helped propel Amazon to the top of the e-commerce pile. The company links with manufacturers and tracks their inventory to ensure orders are fulfilled quickly.

10 companies that are using big data

• Amazon. The online retail giant has access to a massive amount of data on its customers; names, addresses, payments and search histories are all filed away in its data bank.
• American Express.
• BDO.
• Capital One.
• General Electric (GE)
• Miniclip.
• Netflix.
• Next Big Sound.

PostgreSQL

Amazon is a leader in collecting, storing, processing and analyzing personal information from every customer as a means of determining how customers are spending their money. The company uses predictive analytics for targeted marketing which helps them in increasing customer satisfaction and get loyalty in return.