The two biggest barriers to the use of machine learning (both classical machine learning and deep learning) are skills and computing resources. You can solve the second problem by throwing money at it, either for the purchase of accelerated hardware (such as computers with high-end GPUs) or for the rental of compute resources in the cloud (such as instances with attached GPUs, TPUs, and FPGAs).
On the other hand, solving the skills problem is harder. Data scientists often command hefty salaries and may still be hard to recruit. Google was able to train many of its employees on its own TensorFlow framework, but most companies barely have people skilled enough to build machine learning and deep learning models themselves, much less teach others how. Read More
Daily Archives: February 12, 2020
ARM’s new edge AI chips promise IoT devices that won’t need the cloud
Edge AI is one of the biggest trends in chip technology. These are chips that run AI processing on the edge — or, in other words, on a device without a cloud connection. Apple recently bought a company that specializes in it, Google’s Coral initiative is meant to make it easier, and chipmaker ARM has already been working on it for years. Now, ARM is expanding its efforts in the field with two new chip designs: the Arm Cortex-M55 and the Ethos-U55, a neural processing unit meant to pair with the Cortex-M55 for more demanding use cases. Read More
#011 Deep L-layer Neural Network
In this post we will make a Neural Network overview. We will see what is the simplest representation of a Neural Network and how deep representation of a Neural Network looks like.
Rick's Cafe AI 