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Numbers can be multiplied, subtracted and squared in a vacuum, alone in a room. Books, too, on almost any subject, can be processed independently. Emotions, on the other hand, are typically experienced -- and learned -- in context, among people, in a social environment. That's why some California parents are concerned that virtual learning, mandated in areas that have seen a spike in coronavirus cases, might impede their school-age children's social and emotional learning. Experts agree it's not something to take lightly.
Maria Viego and Cooper Glynn were thriving at their elementary schools. Maria, 10, adored the special certificates she earned volunteering to read to second-graders. Cooper, 9, loved being with his friends and how his teacher incorporated the video game Minecraft into lessons. But when their campuses shut down amid the COVID-19 pandemic, their experiences diverged dramatically. Maria is a student in the Coachella Valley Unified School District, where 90% of the children are from low-income families. She didn't have a computer, so she and her mother tried using a cellphone to access her online class, but the connection kept dropping, and they gave up after a week. She did worksheets until June, when she at last received a computer, but struggled to understand the work. Now, as school starts again online, she has told her mother she's frustrated and worried.
After multiple online meetings and virtual conversations, I've learned there are many ways people are dealing with suddenly working from home. I would categorize a really low desire as, "I don't want to start anything new, let's just try to get through this." And a really high desire as, "I have more free time than I used to, I should learn something new!" If and when you are looking to learn new things, I've compiled a list of deep learning resources. Below is a range of deep learning resources that can take anywhere from 5 minutes to 3 hours depending on what you're looking for.
Online Courses Udemy Machine Learning - Get Your Hands Dirty by Solving Real Industry Challenges with Python Created by Kirill Eremenko, Hadelin de Ponteves, Dr. Ryan Ahmed, Ph.D., MBA, SuperDataScience Team, Rony Sulca English [Auto-generated] Students also bought Machine Learning Classification Bootcamp in Python Python for Computer Vision with OpenCV and Deep Learning Optimization problems and algorithms Machine Learning Regression Masterclass in Python Complete Guide to TensorFlow for Deep Learning with Python Preview this course GET COUPON CODE Description So you know the theory of Machine Learning and know how to create your first algorithms. There are tons of courses out there about the underlying theory of Machine Learning which don't go any deeper – into the applications. This course is not one of them. Are you ready to apply all of the theory and knowledge to real life Machine Learning challenges? We gathered best industry professionals with tons of completed projects behind.
Core engine performance enhancements accelerate verification throughput by reducing stimulation cycles with matching coverage on randomized test suites. Cadence Design Systems, Inc. today announced that the Cadence Xcelium Logic Simulator has been enhanced with machine learning technology (ML), called Xcelium ML, to increase verification throughput.Using new machine learning technology and core computational software, Xcelium ML enables up to 5X faster verification closure on randomized regressions. Using computational software and a proprietary machine learning technology that directly interfaces to the simulation kernel, Xcelium ML learns iteratively over an entire simulation regression.It analyzes patterns hidden in the verification environment and guides the Xcelium randomization kernel on subsequent regression runs to achieve matching coverage with reduced simulation cycles. Cadence's Xcelium Logic Simulator provides best-in-class core engine performance for SystemVerilog, VHDL, mixed-signal, low power, and x-propagation.It supports both single-core and multi-core simulation, incremental and parallel build, and save/restart with dynamic test reload. The Xcelium Logic Simulator has been deployed by a majority of top semiconductor companies, and a majority of top companies in the hyper-scale, automotive, and consumer electronics segments.Kioxia has effectively utilized Xcelium simulation for a variety of our designs, and it addresses our ever-growing verification needs.
The Indian Institute of Technology-Roorkee (IIT-R) in partnership with leading online learning platform Coursera on Thursday launched two new online certificate programmes for professionals looking to build skills in data science, Artificial Intelligence (AI) and Machine Learning (ML). The six-month certificate programme in AI and ML will consist of video lectures, hands-on learning opportunities, team projects, tutorials and workshops. The programme will also teach classical ML techniques and provide hands-on programming experience with'Tensorflow' software for model building, robust ML production and powerful experimentation. The certificate programme in data science will help professionals build skills in data science, machine learning, critical thinking, data collection, data visualization and data management. "We are delighted to partner with Coursera to help fulfil the goal of inclusive education of the New Education Policy," Professor Ajit K Chaturvedi, Director, IIT Roorkee, said in a statement.
Before we do any further analysis using our data we need to split the entire data set into three different subsets: training set, validation set and test set. The test set is also referred to as hold-out set and once we split it from the remaining data we do not touch it again until we have trained and tweaked our Machine Learning models to a point where we think they will perform well on data that they have never seen before. We split the remaining data into a training and a validation set. This allows us to train our models on the training data and then evaluate their performance on the validation data. In theory, we can then go and tweak our models and evaluate them on the validation data again and thereby find ways to improve model performance.
Big data has gotten really, really big: By 2025, all the world's data will add up to an estimated 175 trillion gigabytes. For a visual, if you stored that amount of data on DVDs, it would stack up tall enough to circle the Earth 222 times. One of the biggest challenges in computing is handling this onslaught of information while still being able to efficiently store and process it. A team from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) believe that the answer rests with something called "instance-optimized systems." Traditional storage and database systems are designed to work for a wide range of applications because of how long it can take to build them -- months or, often, several years.