New computational algorithms make it possible to build neural networks with many input nodes and many layers, and distinguish "deep learning" of these networks from previous work on artificial neural nets.
Follow my Twitter and join the Geometric Deep Learning subreddit for latest updates in the space. The vast majority of deep learning is performed on Euclidean data. This includes datatypes in the 1-dimensional and 2-dimensional domain. But we don't exist in a 1D or 2D world. All that we can observe exists in 3D, and our data should reflect that.
A whopping 90% of data created since the dawn of human civilization was produced in the past two years! The rate of data creation continues to increase with the proliferation of digital technologies such as social media and the internet of things (IoT) together with ever-faster wireless communication technologies such as 5G. However, most new data created is "Unstructured," such as text, images, audio, and video [Source]. Unstructured data gets its name because it does not have an inherent structure, unlike a table of rows and columns. Instead, unstructured data contains information in one of several possible formats. For example, e-commerce images, customer reviews, social media posts, surveillance videos, speech commands, etc., are rich sources of information that do not follow the traditional tabular data format. Recent advances in Artificial Intelligence (AI) and Machine Learning (ML) technologies have created a way to extract useful information from unstructured data sources in a scalable way by the use of "embeddings."
XManager is a platform for packaging, running and keeping track of machine learning experiments. It currently enables one to launch experiments locally or on Google Cloud Platform (GCP). Interaction with experiments is done via XManager's APIs through Python launch scripts. To get started, install XManager, its prerequisites if needed and follow the tutorial or codelab.ipynb to create and run a launch script. Or, alternatively, a PyPI project is also available.
A hypernetwork aims to find the best deep neural network architecture to solve a given task. Boris Knyazev of the University of Guelph in Ontario and his colleagues have designed and trained a "hypernetwork" that could speed up the training of neural networks. Given a new, untrained deep neural network designed for some task, the hypernetwork predicts the parameters for the new network in fractions of a second, and in theory could make training unnecessary. The work may also have deeper theoretical implications. The name outlines the approach.
In December 2019, DeepMind's AI system, AlphaFold, solved a 50-year-old grand challenge in biology, known as the protein-folding problem. A headline in the journal Nature read, "It will change everything" and the President of the UK's Royal Society called it a "stunning advance [that arrived] decades before many in the field would have predicted". In this episode, Hannah uncovers the inside story of AlphaFold from the people who made it happen and finds out how it could help transform the future of healthcare and medicine. Thank you to everyone who made this season possible! Find Seasons 1 & 2 on YouTube: http://dpmd.ai/3geDPmL
We tend to think of machine and deep learning AI as consistent, logical, and unwavering, but surprisingly, that isn't always the case. Bias is the source of many AI failures. So why, and how, does bias happen in AI models? The simple answer is that bias exists in these models because they're created by humans. Let's take a look at three types of AI bias that can plague AI models – sample bias, measurement bias, and prejudice bias – and how developers can eliminate these biases with more thorough AI model training.
Tiny deep learning on microcontroller units (MCUs) is challenging due to the limited memory size. Memory bottleneck exists with MCUs because of the imbalanced memory distribution in convolutional neural network (CNN) designs. For instance, in MobileNetV2 only the first 5 blocks have a high peak memory ( 450kB), becoming the memory bottleneck of the entire network. The remaining 13 blocks have a low memory usage, which can easily fit a 256kB MCU. The peak memory of the initial memory-intensive stage is 8 times higher than the rest of the network.
Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. The second most populous country in the world, India has enjoyed steady economic growth and has achieved self-sufficiency in grain production in recent years.
It is not a research niche anymore. Random Forests and Deep Learning networks are now available in libraries and tools, waiting to be applied to business problems and real-world data. This is so true that the focus of AI has now shifted from proposing new paradigms and new algorithms to engineering existing solutions via a standardized sequence of MLOps. When something moves away from research and becomes mainstream, other segments of the data analytics society claim access to it. Can people who haven't learned how to code, like marketing analysts, physicians, nurses, CFOs, accountants, mechanical engineers, auditing professionals, and many other professional figures, successfully implement AI solutions?
If you want to be the very best that no one ever was, you should read this tutorial on how to use an AWS Deep Learning AMI to train a Neural Network classifier in Python. The goal of this classifier is to give an image of a Gen 1 Pokemon, to identify it. That was a lot of acronyms and funny words, before we get started on the tutorial, let's cover some background information. AMI stands for Amazon Machine Image and is a template that is used to launch a virtual server (which in AWS is also known as an EC2 instance that you can read more about below). Since it is a template, you can use one AMI to launch multiple EC2 instances with the same configurations.