What exactly is the difference is between Keras and TensorFlow?

! Keras is actually integrated into TensorFlow. It’s a wrapper around the TensorFlow backend

Technically speaking, you could use Keras with a variety of potential backends.

But what exactly does that mean?

Basically, you are able to make any Keras call you need from within TensorFlow.

You get to enjoy the TensorFlow backend, while leveraging the simplicity of of Keras.

Here’s a great article on Medium that walks you through the process of creating a neural network with Keras.

Machine Learning represents a new paradigm in programming, where instead of programming explicit rules in a language such as Java or C++, you build a system which is trained on data to infer the rules itself.

But what exactly does ML actually look like?

In part one of Machine Learning Zero to Hero, AI Advocate Laurence Moroney walks through a basic Hello World example of building an ML model, introducing ideas which we’ll apply in later episodes to a more interesting problem: computer vision.

Try this code out for yourself in the Hello World of Machine Learning: https://goo.gle/2Zp2ZF3

TensorFlow is already one of the most popular tools for creating deep learning models.

Google this week introduced Neural Structured Learning (NSL) to make this tool even better.

Here’s why, NSL is a big deal.

Neural Structured Learning in TensorFlow is an easy-to-use framework for training deep neural networks by leveraging structured signals along with feature inputs. This learning paradigm implements Neural Graph Learning in order to train neural networks using graphs and structured data. As the researchers mention, the graphs can come from multiple sources such as knowledge graphs, medical records, genomic data or multimodal relations. Moreover, this framework also generalises to adversarial learning.

This looks interesting.

Google today introduced Neural Structured Learning (NSL) , an open source framework that uses the Neural Graph Learning method for training neural networks with graphs and structured data. NSL works with with the TensorFlow machine learning platform and is made to work for both experienced and inexperienced machine learning […]

Here’s an in-depth look at doing Natural Language Processing in the three top frameworks: TensorFlow, PyTorch, and Keras.

Before beginning a feature comparison between TensorFlow vs PyTorch vs Keras, let’s cover some soft, non-competitive differences between them. Non-competitive facts Below we present some differences between the three that should serve as an introduction to TensorFlow vs PyTorch vs Keras. These differences aren’t written in the spirit of […]

While this is technically a press release, there could be something to DarwinAI if it really can increase neural networks performance more than 1600%. We’ll have to keep an eye on this technology. 😉

“The complexity of deep neural networks makes them a challenge to build, run and use, especially in edge-based scenarios such as autonomous vehicles and mobile devices where power and computational resources are limited,” said Sheldon Fernandez, CEO of DarwinAI. “Our Generative Synthesis platform is a key technology in enabling AI at the edge – a fact bolstered and validated by Intel’s solution brief.”

InfoWorld write a glowing review of TensorFlow 2.

Of all the excellent machine learning and deep learning frameworks available, TensorFlow is the most mature, has the most citations in research papers (even excluding citations from Google employees), and has the best story about use in production. It may not be the easiest framework to learn, but it’s much less intimidating than it was in 2016. TensorFlow underlies many Google services.

Here’s an interesting computer vision / IoT project you can make at home.

The JeVois machine vision sensor can recognize a wide variety of objects and symbols. My own project, Hedley the Robotic Skull , uses one to track me as I walk around in his field of view. The sensor communicates with an Arduino microcontroller, which moves the pan servo to […]

SparkFun is a company well known for their IoT goodies and, now, they are venturing into the AI space with this new TensorFlow based kit: the SparkFun Artemis.

SparkFun released the company’s first open-source, embedded-systems module, SparkFun Artemis, Engineering Version. The SparkFun Artemis is intended to empower engineers, prototype makers, and R&D teams to integrate the TensorFlow machine-learning platform into any design. Additionally, the SparkFun team has launched three boards with the unshielded module: BlackBoard Artemis; BlackBoard […]

Here’s an interesting idea: an open deep learning compiler stack to compile various deep learning models from different frameworks to the CPU, GPU or specialised accelerators. It’s called the Tensor Virtual Machine or TVM for short.

TVM supports model compilation from a wide range of frontends like TensorFlow, Onnx, Keras, Mxnet, Darknet, CoreML and Caffe2. TVM-compiled modules can be deployed on backends like LLVM (JavaScript or WASM, AMD GPU, ARM or X86), NVidia GPU (CUDA), OpenCL and Metal. TVM also supports runtime bindings for programming languages like JavaScript, Java, Python, C++ and Golang. With a wide range of frontend, backend and runtime bindings, this deep learning compiler enables developers to integrate and deploy deep learning models from any framework to any hardware, via any programming language.