In machine learning, each type of artificial neural network is tailored to certain tasks. This article will introduce two types of neural networks: convolutional neural networks (CNN) and recurrent neural networks (RNN). Using popular Youtube videos and visual aids, we will explain the difference between CNN and RNN and how they are used in computer vision and natural language processing. The main difference between CNN and RNN is the ability to process temporal information or data that comes in sequences, such as a sentence for example. Moreover, convolutional neural networks and recurrent neural networks are used for completely different purposes, and there are differences in the structures of the neural networks themselves to fit those different use cases.

You don't need to work in the marketing department of Facebook or Google to understand the importance of large-scale data analytics when it comes to driving the modern economy. As the primary force behind everything from targeted advertising campaigns to self-driving cars, data analysis stands at the heart of today's most important and exciting technologies and innovations. The Deep Learning & Data Analysis Certification Bundle will help you take your analytical skills to the next level so you can land the best and most lucrative positions in your field, and it's available today for over 95% off at just $39.99. With eight courses and 30 hours of instruction led by the renowned data scientist Minerva Singh, this bundle will get you up to speed with the latest platforms and methodologies in the interconnected worlds of data analysis, visualization, statistics, deep learning, and more. Through easy-to-follow lessons that utilize real-world examples, the training courses will walk you through the fundamentals and more advanced elements of YouTube analytics and Google Ads, R programming in the context of machine learning, algorithms that can help you break down data frameworks, statistical models that will allow you to predict future trends, and more.

Deep learning bears promise for drug discovery, including advanced image analysis, prediction of molecular structure and function, and automated generation of innovative chemical entities with bespoke properties. Despite the growing number of successful prospective applications, the underlying mathematical models often remain elusive to interpretation by the human mind. There is a demand for ‘explainable’ deep learning methods to address the need for a new narrative of the machine language of the molecular sciences. This Review summarizes the most prominent algorithmic concepts of explainable artificial intelligence, and forecasts future opportunities, potential applications as well as several remaining challenges. We also hope it encourages additional efforts towards the development and acceptance of explainable artificial intelligence techniques. Drug discovery has recently profited greatly from the use of deep learning models. However, these models can be notoriously hard to interpret. In this Review, Jiménez-Luna and colleagues summarize recent approaches to use explainable artificial intelligence techniques in drug discovery.

Check our roadmap for more details. This project is for readers who are interested in high-performance implementation of their programs utilizing deep learning techniques, especially model inference, but may not have got their hands dirty yet. We assume readers have a minimal background of only having experience on NumPy before. With this in mind, we will explain things from scratch and introduce relative background when needed. Experienced readers, however, should also find the contents useful.

I recently started a new newsletter focus on AI education. TheSequence is a no-BS( meaning no hype, no news etc) AI-focused newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. Graphs are one of the fundamental data structures in machine learning applications. Specifically, graph-embedding methods are a form of unsupervised learning, in that they learn representations of nodes using the native graph structure.

Now, Penn State researchers have used artificial intelligence (AI) to clear up that noise, drastically facilitating and improving near real-time observation of volcanic movements and the detection of volcanic activity and unrest. "The shape of volcanoes is constantly changing and much of that change is due to underground magma movements in the magma plumbing system made of magma reservoirs and conduits," said Christelle Wauthier, associate professor of geosciences and Institute for Data and Computational Sciences (ICDS) faculty fellow. "Much of this movement is subtle and cannot be picked up by the naked eye." Geoscientists have used several methods to measure the ground changes around volcanoes and other areas of seismic activity, but all have limitations, said Jian Sun, lead author of the paper and a postdoctoral scholar in geosciences, funded by Dean's Postdoc-Facilitated Innovation through Collaboration Award from the College of Earth and Mineral Sciences. He added that, for example, scientists can use ground stations, such as GPS or tiltmeters, to monitor possible ground movement due to volcanic activity.

Cornell researchers have invented an earphone that can continuously track full facial expressions by observing the contour of the cheeks – and can then translate expressions into emojis or silent speech commands. With the ear-mounted device, called C-Face, users could express emotions to online collaborators without holding cameras in front of their faces – an especially useful communication tool as much of the world engages in remote work or learning. "This device is simpler, less obtrusive and more capable than any existing ear-mounted wearable technologies for tracking facial expressions," said Cheng Zhang, assistant professor of information science and senior author of "C-Face: Continuously Reconstructing Facial Expressions by Deep Learning Contours of the Face With Ear-Mounted Miniature Cameras." The paper will be presented at the Association for Computing Machinery Symposium on User Interface Software and Technology, to be held virtually Oct. 20-23. "In previous wearable technology aiming to recognize facial expressions, most solutions needed to attach sensors on the face," said Zhang, director of Cornell's SciFi Lab, "and even with so much instrumentation, they could only recognize a limited set of discrete facial expressions."

Back-propagation (BP) is costly to implement in hardware and implausible as a learning rule implemented in the brain. However, BP is surprisingly successful in explaining neuronal activity patterns found along the cortical processing stream. We propose a locally implementable, unsupervised learning algorithm, CLAPP, which minimizes a simple, layer-specific loss function, and thus does not need to back-propagate error signals. The weight updates only depend on state variables of the pre- and post-synaptic neurons and a layer-wide third factor. Networks trained with CLAPP build deep hierarchical representations of images and speech.

Corresponding authors Abstract In optimization of a shared autonomous electric vehicle (SAEV) system, idle vehicle relocation strategies are important to reduce operation costs and customers' wait time. However, for an on-demand service, continuous optimization for idle vehicle relocation is computationally expensive, and thus, not effective. This study proposes a deep learning-based algorithm that can instantly predict the optimal solution to idle vehicle relocation problems under various traffic conditions. The proposed relocation process comprises three steps. First, a deep learningbased passenger demand prediction model using taxi big data is built. Second, idle vehicle relocation problems are solved based on predicted demands, and optimal solution data are collected. Finally, a deep learning model using the optimal solution data is built to estimate the optimal strategy without solving relocation. In addition, the proposed idle vehicle relocation model is validated by applying it to optimize the SAEV system. We present an optimal service system including the design of SAEV vehicles and charging stations. Further, we demonstrate that the proposed strategy can drastically reduce operation costs and wait times for on-demand services. Keywords: Idle vehicle relocation, deep learning, shared autonomous electric vehicle (SAEV), demand prediction, system optimization 1. Introduction Shared autonomous electric vehicles (SAEVs) that combine car sharing services, autonomous driving technology, and electric vehicles (EVs) are expected to revolutionize transportation systems in the near future [1,2]. An SAEV autonomously goes to the location requested by a customer and rides that customer to a prescribed destination, thus providing a low-stress and safe transportation service [3,4], promoting transportation accessibility [5], and reducing mobility costs [6]. In addition, EVs help reduce fuel consumption and produce less environmental pollutants and greenhouse gas emissions [7-10].

How do humans learn to acquire a powerful, flexible and robust representation of objects? While much of this process remains unknown, it is clear that humans do not require millions of object labels. Excitingly, recent algorithmic advancements in self-supervised learning now enable convolutional neural networks (CNNs) to learn useful visual object representations without supervised labels, too. In the light of this recent breakthrough, we here compare self-supervised networks to supervised models and human behaviour. We tested models on 15 generalisation datasets for which large-scale human behavioural data is available (130K highly controlled psychophysical trials). Surprisingly, current self-supervised CNNs share four key characteristics of their supervised counterparts: (1.) relatively poor noise robustness (with the notable exception of SimCLR), (2.) non-human category-level error patterns, (3.) non-human image-level error patterns (yet high similarity to supervised model errors) and (4.) a bias towards texture. Taken together, these results suggest that the strategies learned through today's supervised and self-supervised training objectives end up being surprisingly similar, but distant from human-like behaviour. That being said, we are clearly just at the beginning of what could be called a self-supervised revolution of machine vision, and we are hopeful that future self-supervised models behave differently from supervised ones, and---perhaps---more similar to robust human object recognition.