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.

As roles and tasks shift in tandem with the expansion of new technologies, and the division of work between human and machine is redrawn, it is of critical importance to monitor how those changes will impact the evolution of economic gender gaps. Artificial Intelligence (AI) is a prominent driver of change within the transformations brought about by the Fourth Industrial Revolution (4IR), and can serve as key marker of the trajectory of innovation across industries.19 In partnership with the LinkedIn Economic Graph Team, the World Economic Forum aims to provide fresh evidence of the emerging contours of gender parity in the new world of work through near-term labour market information. The increasing expansion of AI is creating the demand for a range of new skills, among them neural networks, deep learning, machine learning, and "tools" such as Weka and Scikit-Learn. AI skills are among the fastest-growing specializations among professionals represented on the LinkedIn platform.

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.

Free Coupon Discount - The Data Science & Machine Learning Bootcamp in Python, Learn Python for Data Science,NumPy,Pandas,Matplotlib,Seaborn,Scikit-learn, Dask,LightGBM,XGBoost,CatBoost and much more Created by Derrick Mwiti, Namespace Labs, English [Auto] Students also bought Data Science 2020: Data Science & Machine Learning in Python COVID-19 Data Science Urban Epidemic Modelling in Python Data Visualization in Python Masterclass: Beginners to Pro Python Data Science with Pandas: Master 12 Advanced Projects Data Science: Supervised Machine Learning in Python Deep Learning Foundation: Linear Regression and Statistics Preview this Udemy Course GET COUPON CODE Description In this course, you'll learn how to get started in data science. You don't need any prior knowledge in programming. We'll teach you the Python basics you need to get started. Here are the items we'll cover in this course The Data Science Process Python for Data Science NumPy for Numerical Computation Pandas for Data Manipulation Matplotlib for Visualization Seaborn for Beautiful Visuals Plotly for Interactive Visuals Introduction to Machine Learning Dask for Big Data Deep Learning & Next Steps For the machine learning section here are some items we'll cover: How Algorithms Work Advantages & Disadvantages of Various Algorithms Feature Importances Metrics Cross-Validation Fighting Overfitting Hyperparameter Tuning Handling Imbalanced Data 100% Off Udemy Coupon .

In recent years, the fields of natural language processing (NLP) and information retrieval (IR) have made tremendous progress thanks to deep learning models like Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTMs) networks, and Transformer based models like Bidirectional Encoder Representations from Transformers (BERT). But these models are humongous in size. On the other hand, real world applications demand small model size, low response times and low computational power wattage. In this survey, we discuss six different types of methods (Pruning, Quantization, Knowledge Distillation, Parameter Sharing, Tensor Decomposition, and Linear Transformer based methods) for compression of such models to enable their deployment in real industry NLP projects. Given the critical need of building applications with efficient and small models, and the large amount of recently published work in this area, we believe that this survey organizes the plethora of work done by the 'deep learning for NLP' community in the past few years and presents it as a coherent story.

Performance evaluations are critical for quantifying algorithmic advances in reinforcement learning. Recent reproducibility analyses have shown that reported performance results are often inconsistent and difficult to replicate. In this work, we argue that the inconsistency of performance stems from the use of flawed evaluation metrics. Taking a step towards ensuring that reported results are consistent, we propose a new comprehensive evaluation methodology for reinforcement learning algorithms that produces reliable measurements of performance both on a single environment and when aggregated across environments. We demonstrate this method by evaluating a broad class of reinforcement learning algorithms on standard benchmark tasks.

While some lab-related activities, such as calculations and data analysis, can be done remotely, these can feel like extra work. Faced with the cancellation of their own in-person laboratory classes during the COVID-19 pandemic, Matthew J. Vergne and colleagues looked outside-the-box. They sought to develop an online game for their students that would mimic the cooperative learning that normally accompanies a lab experience. To do so, they designed a virtual escape game with an abandoned chocolate factory theme. Using a survey-creation app, they set up a series of "rooms," each containing a problem that required students to, for example, calculate the weight of theobromine, a component of chocolate.

PyTorch has rapidly become one of the most transformative frameworks in the field of Deep Learning. Since its release, PyTorch has completely changed the landscape in the field of deep learning due to its flexibility, and how easy it is to use when building Deep Learning models. Deep Learning jobs command some of the highest salaries in the development world. This course is meant to take you from the complete basics, to building state-of-the art Deep Learning and Computer Vision applications with PyTorch. With over 44000 students, Rayan is a highly rated and experienced instructor who has followed a "learn by doing" style to create this amazing course.