University of California, Irvine AI/Machine Learning Part-Time Instructor Recruitment Period Open date: February 22nd, 2019 Last review date: Friday, Mar 1, 2019 at 11:59pm (Pacific Time) Applications received after this date will be reviewed by the search committee if the position has not yet been filled. Final date: Saturday, Feb 22, 2020 at 11:59pm (Pacific Time) Applications will continue to be accepted until this date, but those received after the review date will only be considered if the position has not yet been filled. Description At the University of California Irvine's Department of Continuing Education - Technology Programs, our mission is to provide the best technical professional development courses online. We are laser focused on inspiring our students to learn new technical coding skills and shaping the future for their success. We are passionate about our education programs that support our students to fulfil their career goals and we are empowered to help thousands of people learn online every day.
This book starts with the essentials of turning on the basic hardware. It provides the capability to interpret your commands and have your robot initiate actions. In this second edition, you will learn more specifics on how to use the Raspberry Pi's GPIO pins to communicate with and control a wide range of additional hardware. Teaching you to use the Raspberry Pi from scratch, this book will discuss a wide range of capabilities that can be achieved with it. These capabilities include voice recognition, human-like speech simulation, computer vision, motor control, GPS location, and wireless control.
Generative Adversarial Networks, or GANs, are an architecture for training generative models, such as deep convolutional neural networks for generating images. The GAN architecture is comprised of both a generator and a discriminator model. The generator is responsible for creating new outputs, such as images, that plausibly could have come from the original dataset. The generator model is typically implemented using a deep convolutional neural network and results-specialized layers that learn to fill in features in an image rather than extract features from an input image. Two common types of layers that can be used in the generator model are a upsample layer (UpSampling2D) that simply doubles the dimensions of the input and the transpose convolutional layer (Conv2DTranspose) that performs an inverse convolution operation. In this tutorial, you will discover how to use UpSampling2D and Conv2DTranspose Layers in Generative Adversarial Networks when generating images. Discover how to develop DCGANs, conditional GANs, Pix2Pix, CycleGANs, and more with Keras in my new GANs book, with 29 step-by-step tutorials and full source code.
Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself.
Your study material will be available to you on Imarticus's Learning Management System, which is a fully integrated state-of-the-art learning management system for an extended duration of 7 months. You will need to log in to the learning portal using the credentials provided and navigate through the portal as required.
It feels impossible to keep up with every new concept and technology in data science and machine learning. You have multiple languages, libraries and design principles. We have written pieces on different resources that can help data professionals keep up to date with all the various technologies. However, many of these courses cost money. But coursera offers an opportunity to take online courses for free from actual colleges and educational institutions.
Artificial Intelligence (AI) study and use is on the rise. Tools to enable AI are becoming more readily available, simpler to use and easier to implement. What's more is that the definition of AI itself has been broken down into ingredients that, when later applied into a recipe (or process), can provide multiple desired outcomes. One of the more important ingredients used in most recipes is Machine Learning. Machine Learning in essence is a way of teaching computers to provide more accurate predictions on provided data.
In an industry that is experiencing a steady rate of job creation, data science itself has moved from just a buzzword to a strategic component in organisations. In addition to this, data scientists are increasingly taking on more strategic roles as organisations employ a product-centric view of data. It is a field that promises tremendous job growth and higher earning potential. Our latest research posits 97,000 jobs are available in this buzzing field. On the hiring end, there is a significant overall growth in jobs in the field.