Due to contractual restrictions, only candidates that are US Persons (citizens, permanent residents, asylees or refugees) will be considered for this position.

In this blog post I will show how to use a low-code app in MATLAB, the Deep Network Designer, for two different tasks and design paradigms: creating a network from scratch vs. using transfer learning. The process of building deep learning (DL) solutions follows a standard workflow that starts from the problem definition and continues with the steps of collecting and preparing the data, selecting a suitable neural network architecture for the job, training and fine-tuning the network, and eventually deploying the model (Figure 1). The selection of a suitable neural network architecture usually follows the best practices for the application at hand, e.g., the use convolutional neural networks (CNNs or ConvNets) for image classification or recurrent neural networks (RNNs) with long short-term memory (LSTM) cells for text and sequence data types of applications. Transfer learning is an incredibly easy, quick, and popular method for building DL solutions in some domains, such as image classification – using neural network architectures pretrained on ImageNet (a large dataset of more than 1 million images in more than 1,000 categories). Essentially, it consists of using a deep neural network that has been pre-trained in a large dataset of similar nature to the problem you are trying to solve. This is usually accomplished by retraining some of its layers (while freezing the others).

Pre-trained generative language models (such as OpenAI's GPT2 and GPT3) or seq2seq models (such as T5 or the recently released T0) generate free-flowing natural language. This entails that their output sentences can have any shape. To get the most value out of these models, we sometimes want the outputs to follow a certain structure -- this is called a constrained language model. After reading this post, you will be able to gain more value out of your language model by controlling the shape of its outputs. This post relates to my previous post, where I explain how to unlock the true potential of GPT3 by manipulating the shape of its inputs (referred to prompt engineering).

Deep learning networks have gained immense popularity in the past few years. The'attention mechanism' is integrated with the deep learning networks to improve their performance. Adding attention component to the network has shown significant improvement in tasks such as machine translation, image recognition, text summarization and similar applications. This tutorial shows how to add a custom attention layer to a network built using a recurrent neural network. We'll illustrate an end to end application of time series forecasting using a very simple dataset.

Bio: Antonio Bicchi is a scientist interested in robotics and intelligent machines. After graduating in Pisa and receiving a Ph.D. from the University of Bologna, he spent a few years at the MIT AI Lab of Cambridge before becoming Professor in Robotics at the University of Pisa. In 2009 he founded the Soft Robotics Laboratory at the Italian Institute of Technology in Genoa. Since 2013 he is Adjunct Professor at Arizona State University, Tempe, AZ. He has coordinated many international projects, including four grants from the European Research Council (ERC).

Echoing the keyword "AIoT" of 2021 Spring Product Launch, Milesight is going to bring the smarter, securer and more connected world into live at the upcoming 2021 Autumn Product Launch. We'll dive deep into the application of technologies like AI Deep Learning, 5G, IoT and Radar in new products and solutions, and how they make the significance in increasing work efficiency as we move towards a digital world. Just join us and explore together!

TensorFlow is an undisputed leader among the various libraries used for deep learning-powered applications. The official website describes TensorFlow as an open-source platform that consists of a comprehensive, flexible ecosystem of tools, libraries, and community resources that allow developers to build and deploy machine learning and deep learning applications. According to Payscale, a machine learning engineer with deep learning skills earns an average annual salary of $112,331 in the US. With experience, such professionals can earn even more and even entry-level professionals can command high salaries. Learning TensorFlow will make you capable of designing and deploying deep learning models and validating the same in front of employers. The Brain team of Google created an open-source machine learning library in 2015 called TensorFlow. TensorFlow is the combination of two words, Tensor -- representation of data for multi-dimensional array and Flow -- the series of operations performed on the Tensor. It is a low-level toolkit used for performing complicated and complex mathematics.

The development of Deep Learning computer algorithms in conjunction with the proliferation of high-resolution cameras have helped revolutionize …

Riverside Research is seeking a Research Scientist with a general background in machine learning and artificial intelligence and a focus on signal processing to join a dynamic, growth-focused Artificial Intelligence and Machine Learning Lab. The Lab performs research and development focused on providing solutions to the Defense and Intelligence Communities. As a key member of our Open Innovation Center, the research scientist will execute as well as assist in growing opportunities with government research organizations (e.g. DARPA, IARPA, service labs, etc.), perform on our corporate-wide Independent Research & Development (IR&D) efforts in artificial intelligence and machine learning, manage existing R&D contracts, and transition technology into our other business units. The Research Scientist will work with team members located in the Dayton OH, Washington DC, New York City, and Boston office locations while reporting to the Director of the Artificial Intelligence and Machine Learning Lab of the Open Innovation Center Business Unit.

The 2021 edition of the State of AI Report came out last week. So did the Kaggle State of Machine Learning and Data Science Survey. There's much to be learned and discussed in these reports, and a couple of takeaways caught my attention. "AI is increasingly being applied to mission critical infrastructure like national electric grids and automated supermarket warehousing calculations during pandemics. However, there are questions about whether the maturity of the industry has caught up with the enormity of its growing deployment."