My 21-year-old goddaughter, a second-year undergraduate, mentioned in passing that she watches video lectures offline at twice the normal speed. Struck by this, I asked some other students I know. Many now routinely accelerate their lectures when learning offline – often by 1.5 times, sometimes by more. Speed learning is not for everyone, but there are whole Reddit threads where students discuss how odd it will be to return to the lecture theatre. One contributor wrote: "Normal speed now sounds like drunk speed."

Description DeepLearning AI TensorFlow Developer Professional Certificate is a training course specializing in working with the TensorFlow framework. Today...

The world has been revolving much around the terms "Machine Learning" and "Deep Learning" recently. With or without our knowledge every day we are using these technologies. There are tons of other applications too. No wonder why "Deep Learning" and "Machine Learning along with Data Science" are the most sought after talent in the technology world now a days. But the problem is that, when you think about learning these technologies, a misconception that lots of maths, statistics, complex algorithms and formulas needs to be studied prior to that.

Thompson sampling (TS) has attracted a lot of interest in the bandit area. It was introduced in the 1930s but has not been theoretically proven until recent years. All of its analysis in the combinatorial multi-armed bandit (CMAB) setting requires an exact oracle to provide optimal solutions with any input. However, such an oracle is usually not feasible since many combinatorial optimization problems are NP-hard and only approximation oracles are available. An example (Wang and Chen, 2018) has shown the failure of TS to learn with an approximation oracle. However, this oracle is uncommon and is designed only for a specific problem instance. It is still an open question whether the convergence analysis of TS can be extended beyond the exact oracle in CMAB. In this paper, we study this question under the greedy oracle, which is a common (approximation) oracle with theoretical guarantees to solve many (offline) combinatorial optimization problems. We provide a problem-dependent regret lower bound of order $\Omega(\log T/\Delta^2)$ to quantify the hardness of TS to solve CMAB problems with greedy oracle, where $T$ is the time horizon and $\Delta$ is some reward gap. We also provide an almost matching regret upper bound. These are the first theoretical results for TS to solve CMAB with a common approximation oracle and break the misconception that TS cannot work with approximation oracles.

Artificial Intelligence has been transforming industries and academic research across the globe, and research software development is no exception. Machine learning and deep learning are being applied in every aspect of the research software development lifecycles, from new algorithm design paradigms to software development processes. In this paper, we discuss our views on today's challenges and opportunities that AI has presented on research software development and engineers, and the approaches we, at the University of Florida, are taking to prepare our workforce for the new era of AI.

The first thing we'll do is to introduce you to TensorFlow, the most widely used machine learning library. By learning the basics and then moving on to some fundamental techniques, you will gain a reasonable understanding of how to use this powerful library in your applications. The next step is to build an actual model. As you go through the process, you will learn how to prepare your data, train the network, and make predictions.

The Convolutional Neural Network (CNN) has been used to obtain state-of-the-art results in computer vision tasks such as object detection, image segmentation, and generating photo-realistic images of people and things that don't exist in the real world! This course will teach you the fundamentals of convolution and why it's useful for deep learning and even NLP (natural language processing). You will learn about modern techniques such as data augmentation and batch normalization, and build modern architectures such as VGG yourself. The basics of machine learning and neurons (just a review to get you warmed up!) Neural networks for classification and regression (just a review to get you warmed up!) All of the materials required for this course can be downloaded and installed for FREE.

All machine learning models involve optimization. Most likely, we use computational algorithms to optimize. There are many ways to optimize numerically. SciPy has a number of functions handy for this. We can also try to implement the optimization algorithms on our own. In this crash course, you will discover how you can get started and confidently run algorithms to optimize a function with Python in seven days. This is a big and important post. You might want to bookmark it. Optimization for Machine Learning (7-Day Mini-Course) Photo by Brewster Malevich, some rights reserved.

University campuses are essentially a microcosm of a city. They comprise diverse facilities such as residences, sport centres, lecture theatres, parking spaces, and public transport stops. Universities are under constant pressure to improve efficiencies while offering a better experience to various stakeholders including students, staff, and visitors. Nonetheless, anecdotal evidence indicates that campus assets are not being utilised efficiently, often due to the lack of data collection and analysis, thereby limiting the ability to make informed decisions on the allocation and management of resources. Advances in the Internet of Things (IoT) technologies that can sense and communicate data from the physical world, coupled with data analytics and Artificial intelligence (AI) that can predict usage patterns, have opened up new opportunities for organisations to lower cost and improve user experience. This thesis explores this opportunity via theory and experimentation using UNSW Sydney as a living laboratory.

Become a Python Pro with these valuable skills. Find the right instructor for you. Choose from many topics, skill levels, and languages. This course is designed to provide a complete introduction to Deep Learning. It is aimed at beginners and intermediate programmers and data scientists who are familiar with Python and want to understand and apply Deep Learning techniques to a variety of problems.