If you're involved in technology, you have almost certainly been hearing a lot about artificial intelligence (AI) and machine learning (ML) recently. Digital Reality partners Google, Microsoft, Facebook, Apple and NVIDIA have all begun working on projects in this space. Google has announced the development of new purpose-built chips, Microsoft is offering online classes about how to use and develop AI solutions, Facebook and Apple are developing their own AI technology and companies like Intel and NVIDIA are releasing new hardware to support AI. These technologies seem to be pervasive across all industries, but two questions many organizations are asking are "What does it mean to me?" and "How do I use it?" The combinations of advanced algorithms and massive quantities of data promise to transform every industry by pushing businesses to new levels of efficiency and enabling distinct competitive advantages.

Your smartphone, smartwatch, and automobile (if it is a newer model) have AI (Artificial Intelligence) inside serving you every day. In the near future, more advanced "self-learning" capable DL (Deep Learning) and ML (Machine Learning) technology will be used in almost every aspect of your business and industry. So now is the right time to learn what DL and ML is and how to use it in advantage of your company. This course has three parts, where the first part focuses on DL and ML technology based future business strategy including details on new state-of-the-art products/services and open source DL software, which are the future enablers. The second part focuses on the core technologies of DL and ML systems, which include NN (Neural Network), CNN (Convolutional NN), and RNN (Recurrent NN) systems.

Patrick Winston's computer is learning about revenge, ambition, and murder. It knows that victory can make you happy. But it also knows you can't be happy if you're dead. The computer had to learn these things in order to read "Macbeth" -- or, rather, an extremely truncated version of Shakespeare's blood-soaked Scottish tragedy. At just 37 sentences, the rough summary reduces the Bard's immortal poetics to such clunkers as, "Witches had visions and danced" and "Lady Macbeth has bad dreams."

In this course you will build MULTIPLE practical systems using natural language processing, or NLP - the branch of machine learning and data science that deals with text and speech. This course is not part of my deep learning series, so it doesn't contain any hard math - just straight up coding in Python. All the materials for this course are FREE. After a brief discussion about what NLP is and what it can do, we will begin building very useful stuff. The first thing we'll build is a spam detector.

Make your first mobile app and game here. Learn how to code and make games in the popular Unreal Engine 4. Learn by building 6 actual games. Make next-level apps that use machine learning with Java, Android, TensorFlow Estimator, PyCharm, and MNIST. By taking this course you will make 3 complete mobile machine learning models and apps. We will build a simple weather prediction project, stock market prediction project, and text-response project.

Just last week, the CEO of this company announced that this system will be installed, for the first time ever, in an American high school and an American university. The names of the institutions have not been made public for obvious reasons, but in these pilot programs for this disruptive new technology, the exposure to real-world environments will serve to further hone and refine the algorithms. Why not just start installing them everywhere, you might ask? The reason is simple: Given the sensitive nature of this technology and the panic a false alarm may bring (just imagine a SWAT team rushing to the scene), it is of utmost importance that the algorithms are as refined as possible before a mass rollout can be effected. And there's nothing besides natural environment exposure that can do that. Think of machine learning as no different than human learning. You need constant stimulus from a variety of seemingly chaotic environmental factors for true artificial intelligence to be achieved.

In a matter of years, the technology industry has embedded itself in virtually every aspect of modern life, and MBA programs are scrambling to catch up. The demand for business leaders with technical knowhow is not lost on both MBA students and business schools. In short: there is now overwhelming demand for MBA programs that sell the hard stuff in addition to the traditional leadership, management, quantity analysis package. Tech MBAs have increasingly become lucrative propositions for students who want to understand how to employ artificial intelligence (AI), for instance, within the context of a consulting gig. And master's programs are getting involved too. Smith School of Business at Queen's University, Canada, has just launched a Master of Management in Artificial Intelligence (MMAI), the first program of its kind in North America.

At its heart, AI is computer programming that learns and adapts. It can't solve every problem, but its potential to improve our lives is profound. At Google, we use AI to make products more useful--from email that's spam-free and easier to compose, to a digital assistant you can speak to naturally, to photos that pop the fun stuff out for you to enjoy. Beyond our products, we're using AI to help people tackle urgent problems. A pair of high school students are building AI-powered sensors to predict the risk of wildfires.

We consider a basic problem at the interface of two fundamental fields: submodular optimization and online learning. In the online unconstrained submodular maximization (online USM) problem, there is a universe $[n]=\{1,2,...,n\}$ and a sequence of $T$ nonnegative (not necessarily monotone) submodular functions arrive over time. The goal is to design a computationally efficient online algorithm, which chooses a subset of $[n]$ at each time step as a function only of the past, such that the accumulated value of the chosen subsets is as close as possible to the maximum total value of a fixed subset in hindsight. Our main result is a polynomial-time no-$1/2$-regret algorithm for this problem, meaning that for every sequence of nonnegative submodular functions, the algorithm's expected total value is at least $1/2$ times that of the best subset in hindsight, up to an error term sublinear in $T$. The factor of $1/2$ cannot be improved upon by any polynomial-time online algorithm when the submodular functions are presented as value oracles. Previous work on the offline problem implies that picking a subset uniformly at random in each time step achieves zero $1/4$-regret. A byproduct of our techniques is an explicit subroutine for the two-experts problem that has an unusually strong regret guarantee: the total value of its choices is comparable to twice the total value of either expert on rounds it did not pick that expert. This subroutine may be of independent interest.

Dyna is an architecture for reinforcement learning agents that interleaves planning, acting, and learning in an online setting. This architecture aims to make fuller use of limited experience to achieve better performance with fewer environmental interactions. Dyna has been well studied in problems with a tabular representation of states, and has also been extended to some settings with larger state spaces that require function approximation. However, little work has studied Dyna in environments with high-dimensional state spaces like images. In Dyna, the environment model is typically used to generate one-step transitions from selected start states. We applied one-step Dyna to several games from the Arcade Learning Environment and found that the model-based updates offered surprisingly little benefit, even with a perfect model. However, when the model was used to generate longer trajectories of simulated experience, performance improved dramatically. This observation also holds when using a model that is learned from experience; even though the learned model is flawed, it can still be used to accelerate learning.