TensorFlow’s high-level APIs help you through each stage of your model-building process.

On this episode of TensorFlow Meets, Laurence Moroney talks with TensorFlow Engineering Manager Karmel Allison about how TF 2.0 will make building models much easier.

This article from Analytics India Magazine lists 10 comparisons between the two top deep learning frameworks: PyTorch and TensorFlow.

Libraries play an important role when developers decide to work in machine learning or deep learning researches. According to this article, a survey based on a sample of 1,616 ML developers and data scientists, for every one developer using PyTorch, there are 3.4 developers using TensorFlow.

Here’s an interesting tutorial for Keras and TensorFlow that predicts employee retention.

In this tutorial, you’ll build a deep learning model that will predict the probability of an employee leaving a company. Retaining the best employees is an important factor for most organizations. To build your model, you’ll use this dataset available at Kaggle, which has features that measure employee satisfaction in a company. To create this model, you’ll use the Keras sequential layer to build the different layers for the model.

Here’s an interesting look on the use of AI and machine learning in the geospatial world.  Given the huge datasets found in remote sensing, it’s not surprising to see that field leading the way in cutting edge data analytics.

From a geospatial perspective, machine learning has long been in wide use. Remote sensing datasets have always been large, so the large data processing power of Machine Learning has been a natural fit. For example, processing satellite images using K Means or ISODATA clustering algorithms was one of the first uses of remote sensing software.

As the popularity of AI and deep learning increases,  the popularity of the various deep learning frameworks and libraries also increase in popularity and in number. To provide an informed choice, researchers devised and ran benchmarks and have compiled the results.

Here’s an interesting summary of the findings.

TensorFlow had the lowest the GPU utilization, followed by Theano and CNTK. For CPU utilization, Theano had the lowest utilization followed by TensorFlow and CNTK. For Memory utilization while using CPU and GPU, the results were close to each other.

A sub-project of TensorFlow, MLIR, or Multi-Level Intermediate Representation, promises better performance for ML.

Here’s an article from IT World on the subject.

Engineers working on Google’s TensorFlow machine learning framework have revealed a subproject, MLIR, that is intended to be a common intermediate language for machine learning frameworks. MLIR, short for Multi-Level Intermediate Representation , will allow projects using TensorFlow and other machine learning libraries to be compiled to more efficient […]

Here’s a great tutorial that uses deep learning to compose one image in the style of another image. If you’ve ever wished that you could paint like Picasso or Van Gogh. then this AI technique is your big chance.

Known as neural style transfer and the technique is outlined in A Neural Algorithm of Artistic Style, you can do this today with TensorFlow.

Neural style transfer is an optimization technique used to take two images—a content image and a style reference image (such as an artwork by a famous painter)—and blend them together so the output image looks like the content image, but “painted” in the style of the style reference image.