You may take anywhere between 6-8 months to complete this course. Expected time commitment is 8-10 hours per week. Prior programming experience & OOPS concepts in any language is a must, knowledge of Python is recommended but not mandatory. You may try out out our Python for Developers course for strengthening your Python Concepts. You should be familiar with Git-Github basics.

A new robot learning system that can learn about physical attributes of the environment through its own experiences in the real world, without the need for simulations or human supervision. University of California, Berkeley (UC Berkeley) researchers have developed a robot learning system that can learn about physical attributes of the environment through its own experiences in the real world, without the need for simulations or human supervision. BADGR: the Berkeley Autonomous Driving Ground Robot autonomously collects data and automatically labels it. The system uses that data to train an image-based neural network predictive model, and applies that model to plan and execute actions that will lead the robot to accomplish a desired navigational task. UC Berkeley's Gregory Kahn wrote, "The key insight behind BADGR is that by autonomously learning from experience directly in the real world, BADGR can learn about navigational affordances, improve as it gathers more data, and generalize to unseen environments."

Data science is one of the most well-payed jobs in the contemporary market. It is even considered as the hottest job of the 21st century. Data science has been a game-changer across every industry. With high-level digitization of processes, the generation of data is at peak and thus data science technology and tools are deployed to drive more productivity across organizations. This tech-field as a whole has a bunch of perks to provide including technologies for Big Data, Data Mining, Machine Learning, Data Analysis, and Data Analytics.

It's not easy to break into a new field, especially one as complex and multi-faceted as data science. What a data scientist used to do, used to need to understand, and the types of companies that need to hire data scientists are in a state of rapid evolution. Why in the world would there be only one path to follow? Honestly, if you're trying to break into data science, I can't think of a better time to get started! If you can take on a graduate degree, go for it!

Both few-shot and continual learning have seen substantial progress in the last years due to the introduction of proper benchmarks. That being said, the field has still to frame a suite of benchmarks for the highly desirable setting of continual few-shot learning, where the learner is presented a number of few-shot tasks, one after the other, and then asked to perform well on a validation set stemming from all previously seen tasks. Continual few-shot learning has a small computational footprint and is thus an excellent setting for efficient investigation and experimentation. In this paper we first define a theoretical framework for continual few-shot learning, taking into account recent literature, then we propose a range of flexible benchmarks that unify the evaluation criteria and allows exploring the problem from multiple perspectives. As part of the benchmark, we introduce a compact variant of ImageNet, called SlimageNet64, which retains all original 1000 classes but only contains 200 instances of each one (a total of 200K data-points) downscaled to 64 x 64 pixels. We provide baselines for the proposed benchmarks using a number of popular few-shot learning algorithms, as a result, exposing previously unknown strengths and weaknesses of those algorithms in continual and data-limited settings.

Despite success in many real-world tasks (e.g., robotics), reinforcement learning (RL) agents still learn from tabula rasa when facing new and dynamic scenarios. By contrast, humans can offload this burden through textual descriptions. Although recent works have shown the benefits of instructive texts in goal-conditioned RL, few have studied whether descriptive texts help agents to generalize across dynamic environments. To promote research in this direction, we introduce a new platform, BabyAI++, to generate various dynamic environments along with corresponding descriptive texts. Moreover, we benchmark several baselines inherited from the instruction following setting and develop a novel approach towards visually-grounded language learning on our platform. Extensive experiments show strong evidence that using descriptive texts improves the generalization of RL agents across environments with varied dynamics.

Carnegie Mellon University has shaped artificial intelligence (AI) from the field's very beginning. Today, researchers from all seven colleges across CMU continue to define AI as the next frontier in human progress and are working to help solve problems in areas from healthcare to education. Because of the interdisciplinary campus culture, CMU is a place where women are thriving in the field of AI, even though they are underrepresented in science, technology, engineering and math (STEM) across the globe. CMU's women in AI -- from undergraduate students to distinguished faculty members -- discuss their research, its impact and their future in the world of AI.

Academic reputation is a key driver for colleges and universities to attract the best students, but campus safety is becoming an increasingly important factor in the college-choice equation. Topping the list of safety concerns is the threat of gun violence at schools, which struck on a nearly weekly basis in the past year in the United States. Of the 45 school shootings that took place up to Nov. 19, 2019, 14 occurred on higher education campuses, according to a CNN report. The active-shooter-on-campus threat is real, and it's the primary threat that keeps campus safety leaders up at night thinking of new ways to detect, deter and react to such incidents. Other top campus crime concerns include burglaries, forcible sex offenses, vehicle theft, assault and robberies.

Working at Shell's Deepwater division in New Orleans gives Barbara Waelde a front-row seat to how the right data can unlock crucial information for the oil giant. So when her supervisor asked her last year if she was interested in a program that could sharpen her digital and data science capabilities, Waelde, 55, jumped at the chance. Since she began her online coursework, the seven-year Shell veteran has learned Python programming, supervised learning algorithms and data modeling, among other skills. Shell began making these online courses available to U.S. employees long before COVID-19 upended daily life. And according to the oil giant, there are no plans to halt or cancel any of them, despite the fact that on March 23 it announced plans to slash operating costs by $9 billion.

We propose a first-order stochastic optimization algorithm incorporating adaptive regularization applicable to machine learning problems in deep learning framework. The adaptive regularization is imposed by stochastic process in determining batch size for each model parameter at each optimization iteration. The stochastic batch size is determined by the update probability of each parameter following a distribution of gradient norms in consideration of their local and global properties in the neural network architecture where the range of gradient norms may vary within and across layers. We empirically demonstrate the effectiveness of our algorithm using an image classification task based on conventional network models applied to commonly used benchmark datasets. The quantitative evaluation indicates that our algorithm outperforms the state-of-the-art optimization algorithms in generalization while providing less sensitivity to the selection of batch size which often plays a critical role in optimization, thus achieving more robustness to the selection of regularity.