The discipline of AI most often mentioned these days is deep learning (DL) along with its many incarnations implemented with deep neural networks. DL also is a rapidly accelerating area of research with papers being published at a fast clip by research teams from around the globe. I enjoy keeping a pulse on deep learning research and so far in 2021 research innovations have propagated at a quick pace. In this article, we'll take a brief tour of my top picks for deep learning research (in no particular order) of papers that I found to be particularly compelling. I'm pretty attached to this leading-edge research. I'm known to carry a thick folder of recent research papers around in my backpack and consume all the great developments when I have a spare moment.

In cognitive decoding, researchers aim to characterize a brain region's representations by identifying the cognitive states (e.g., accepting/rejecting a gamble) that can be identified from the region's activity. Deep learning (DL) methods are highly promising for cognitive decoding, with their unmatched ability to learn versatile representations of complex data. Yet, their widespread application in cognitive decoding is hindered by their general lack of interpretability as well as difficulties in applying them to small datasets and in ensuring their reproducibility and robustness. We propose to approach these challenges by leveraging recent advances in explainable artificial intelligence and transfer learning, while also providing specific recommendations on how to improve the reproducibility and robustness of DL modeling results.

This package provides an implementation of the inference pipeline of AlphaFold v2.0. This is a completely new model that was entered in CASP14 and published in Nature. For simplicity, we refer to this model as AlphaFold throughout the rest of this document. Any publication that discloses findings arising from using this source code or the model parameters should cite the AlphaFold paper. Please also refer to the Supplementary Information for a detailed description of the method.

TensorFlow is a state-of-the-art, open source machine learning framework created by Google to design, build, and train Machine Learning and Deep learning models. TensorFlow has a comprehensive and flexible ecosystem of tools and community resources that make it easy to develop and train ML and Deep Learning models. I know the options out there; prerequisites and the skills you need to acquire to overcome the learning blocks. So, Please refer to the Closing Notes section at the tail end of this piece, where you will find helpful resources for bootstrapping your intellectual abilities. My goal in this piece is to help you find some interactive courses from the Notable Educators that will edify you with a solid understanding of TensorFlow.

Today, connectionism dominates the world of AI. The emergence of deep learning, which is a quintessentially connectionist technique, has driven the worldwide explosion in AI activity and funding over the past decade. Deep learning's recent accomplishments have been nothing short of astonishing. Yet as deep learning spreads, its limitations are becoming increasingly evident. If AI is to reach its full potential going forward, a reconciliation between connectionism and symbolism is essential.

Most recent transformer-based models show impressive performance on vision tasks, even better than Convolution Neural Networks (CNN).

In my previous post, I wrote about my journey from a mechanical engineering graduate to becoming a masters student in machine learning at University of Toronto. In this post, I would like to share my process of getting started with my deep learning/computer vision research project in academic research setting. He later added that the image data would be Xray scans of baggages at airport security. The trained deep learning model would be identifying baggages that contain dangerous items, a task usually performed by security personnels at the airport. The professor shared with me a dataset folder containing the Xray baggage images.

The terms artificial intelligence, machine learning, and deep learning can be very unclear and muzzy sometimes even by its practitioners as it is used in the same context interchangeably. Lets begin with the popular "Artificial Intelligence". The term "Artificial Intelligence" has been around for over 60 years. Of course it triggers a lot of connotations to people when they hear the term especially at the first instance. Some of the ideas describes computers as being smart from probably getting some good article or data from the internet like Wikipedia.

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. For the better part of a year, OpenAI's GPT-3 has remained among the largest AI language models ever created, if not the largest of its kind. Via an API, people have used it to automatically write emails and articles, summarize text, compose poetry and recipes, create website layouts, and generate code for deep learning in Python. But an AI lab based in Tel Aviv, Israel -- AI21 Labs -- says it's planning to release a larger model and make it available via a service, with the idea being to challenge OpenAI's dominance in the "natural language processing-as-a-service" field. The startup says that the largest version of its model -- called Jurassic-1 Jumbo -- contains 178 billion parameters, or 3 billion more than GPT-3 (but not more than PanGu-Alpha, HyperCLOVA, or Wu Dao 2.0).

Artificial intelligence research company OpenAI has announced the development of an AI system that translates natural language to programming code--called Codex, the system is being released as a free API, at least for the time being. Codex is more of a next-step product for OpenAI, rather than something completely new. It builds on Copilot, a tool for use with Microsoft's GitHub code repository. With the earlier product, users would get suggestions similar to those seen in autocomplete in Google, except it would help finish lines of code. Codex has taken that concept a huge step forward by accepting sentences written in English and translating them into runnable code.