In countries experiencing unprecedented waves of urbanization, there is a need for rapid and high-quality urban street design. Our study presents a novel deep learning powered approach, DeepStreet (DS), for automatic street network generation that can be applied to the urban street design with local characteristics. DS is driven by a Convolutional Neural Network (CNN) that enables the interpolation of streets based on the areas of immediate vicinity. Specifically, the CNN is firstly trained to detect, recognize and capture the local features as well as the patterns of the existing street network sourced from the OpenStreetMap. With the trained CNN, DS is able to predict street networks' future expansion patterns within the predefined region conditioned on its surrounding street networks. To test the performance of DS, we apply it to an area in and around the Eixample area in the City of Barcelona, a well-known example in the fields of urban and transport planning with iconic grid-like street networks in the centre and irregular road alignments farther afield. The results show that DS can (1) detect and self-cluster different types of complex street patterns in Barcelona; (2) predict both gridiron and irregular street and road networks. DS proves to have a great potential as a novel tool for designers to efficiently design the urban street network that well maintains the consistency across the existing and newly generated urban street network. Furthermore, the generated networks can serve as a benchmark to guide the local plan-making especially in rapidly-developing cities. Keywords: Urban street network, machine learning, deep learning, Convolutional Neural Network (CNN), Generative Adversarial Network (GAN), image completion, image inpainting

In this paper, we consider the syntactic properties of languages emerged in referential games, using unsupervised grammar induction (UGI) techniques originally designed to analyse natural language. We show that the considered UGI techniques are appropriate to analyse emergent languages and we then study if the languages that emerge in a typical referential game setup exhibit syntactic structure, and to what extent this depends on the maximum message length and number of symbols that the agents are allowed to use. Our experiments demonstrate that a certain message length and vocabulary size are required for structure to emerge, but they also illustrate that more sophisticated game scenarios are required to obtain syntactic properties more akin to those observed in human language. We argue that UGI techniques should be part of the standard toolkit for analysing emergent languages and release a comprehensive library to facilitate such analysis for future researchers.

Open Source Python Deep Learning Library – 2015 published – code is hosted on GitHub – originally a uniform interface for various backend libraries (TensorFlow, Microsoft Cognitive Toolkit, Theano, R, PlaidM) – it focuses on being user-friendly, modular, and extensible, and Fast and easy prototyping of neural networks, – Part of the Tensorflow Core API, but was also continued independently – since version 2.4 Keras refers directly to the implementation of Tensorflow 2 – contains numerous implementations of commonly used neural-network building blocks (layers, activation functions, objectives, optimizers, tools to make working with image and text data)

IBM is a multinational technology company founded in 1911 and operates in over 170 countries worldwide. Today, IBM offers a wide spectrum of products and services that includes software solutions, hardware architecture (server and storage architecture), business and technology services, and global financing solutions. As a data driven-company, IBM understands the importance of data and data analytics at every layer of organization to drive better business decisions. Also, a leading provider of Analytics and Cloud-based solutions, IBM offers a full stack of cloud-based products and services spanning across data analytics, storage, AI, IoT, and blockchain. Check out this article about the Microsoft Data Scientist interview!

Modern state-of-the-art neural network architectures are HUGE. For instance, you have probably heard about GPT-3, OpenAI's newest revolutionary NLP model, capable of writing poetry and interactive storytelling. Well, GPT-3 has around 175 billion parameters. To give you a perspective about how large this number is, consider the following. A $100 bill is approximately 6.14 inches wide.

In an attempt to solve the classical question, "Can machine learning predict the market?", I landed on Forex GBPUSD as a challenging financial series with an abundant and free data set. At the end of the story, readers with some Python and ML experience will be able to use the concepts and modify the linked code to produce their own variation of the model. The model is built in Python 3.8 using TensorFlow/Keras 2.3.

Busted: A bot powered by OpenAI's powerful GPT-3 language model has been unmasked after a week of posting comments on Reddit. Under the username /u/thegentlemetre, the bot was interacting with people on /r/AskReddit, a popular forum for general chat with 30 million users. It was posting in bursts of roughly once a minute. Fooled ya--again: It's not the first time GPT-3 has fooled people into thinking what it writes comes from a human. In August a college student published a blog post that hit the top spot on Hacker News and led a handful of people to subscribe.

This article was published as a part of the Data Science Blogathon. Fastai is a popular open-source library used for learning and practicing machine learning and deep learning. Jeremy Howard and Rachel Thomas founded fast.ai with the objective of making deep learning more accessible. All the exhaustive resources such as courses, software, and research papers available in fast.ai In August 2020, fastai_v2 was released that promises to be much faster, and more flexible to implement deep learning frameworks.

It doesn't require a genius to know that Machine Learning (ML) and Data Science are increasingly hot topics. Deep Learning is even touted as one of the most critical skills of today. That being said, deep learning isn't something that can be acquired easily. Machine Learning consists of working with a large volume of data. Data- that needs to be organized, analyzed, and stored.

While synthetic biology has revolutionized our approaches to medicine, agriculture, and energy, the design of completely novel biological circuit components beyond naturally-derived templates remains challenging due to poorly understood design rules. Toehold switches, which are programmable nucleic acid sensors, face an analogous design bottleneck; our limited understanding of how sequence impacts functionality often necessitates expensive, time-consuming screens to identify effective switches. Here, we introduce Sequence-based Toehold Optimization and Redesign Model (STORM) and Nucleic-Acid Speech (NuSpeak), two orthogonal and synergistic deep learning architectures to characterize and optimize toeholds. Applying techniques from computer vision and natural language processing, we ‘un-box’ our models using convolutional filters, attention maps, and in silico mutagenesis. Through transfer-learning, we redesign sub-optimal toehold sensors, even with sparse training data, experimentally validating their improved performance. This work provides sequence-to-function deep learning frameworks for toehold selection and design, augmenting our ability to construct potent biological circuit components and precision diagnostics. The design of synthetic biology circuits remains challenging due to poorly understood design rules. Here the authors introduce STORM and NuSpeak, two deep-learning architectures to characterize and optimize toehold switches.