There's no way you never heard the word "Python" when reading something about Machine Learning. Python isn't famous to be a fast language. In fact it's among the slowests, even compared with other interpreted languages, and we know that speeding up the training phase is key: you don't want to wait forever before being able to use your program. The question, then, becomes more and more relevant: why is it the god of machine learning, though? You don't want to write everything from scrach if it's already out there and free to use. An important example is Google's TensorFlow, the machine learning library we're going to install today.

It's been nearly 4 years since Tensorflow was released, and the library has evolved to its official second version. Tensorflow is Google's library for deep learning and artificial intelligence. Deep Learning has been responsible for some amazing achievements recently, such as: Generating beautiful, photo-realistic images of people and things that never existed (GANs) Beating world champions in the strategy game Go, and complex video games like CS:GO and Dota 2 (Deep Reinforcement Learning) Self-driving cars (Computer Vision) Speech recognition (e.g. Siri) and machine translation (Natural Language Processing) Even creating videos of people doing and saying things they never did (DeepFakes - a potentially nefarious application of deep learning) Tensorflow is the world's most popular library for deep learning, and it's built by Google, whose parent Alphabet recently became the most cash-rich company in the world (just a few days before I wrote this). It is the library of choice for many companies doing AI and machine learning.

In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and matrices. Then we look through what vectors and matrices are and how to work with them, including the knotty problem of eigenvalues and eigenvectors, and how to use these to solve problems. Finally we look at how to use these to do fun things with datasets - like how to rotate images of faces and how to extract eigenvectors to look at how the Pagerank algorithm works. Since we're aiming at data-driven applications, we'll be implementing some of these ideas in code, not just on pencil and paper. Towards the end of the course, you'll write code blocks and encounter Jupyter notebooks in Python, but don't worry, these will be quite short, focussed on the concepts, and will guide you through if you've not coded before.

Valuable marketing insights are hard to find in the mountains of data many marketers are now faced with – resurfacing them requires more sophistication than manual methods can deliver. Machine learning and AI has come to the fore in this regard. So much so that 92% of companies have increased their investment in AI and big data this year. But things are rarely simple when it comes to data analytics. Even early adopters admit that they are yet to become fully'data-driven': In most cases, the lack of a proper AI adoption strategy is to blame.

Meta-learning has been proposed as a framework to address the challenging few-shot learning setting. The key idea is to leverage a large number of similar few-shot tasks in order to learn how to adapt a base-learner to a new task for which only a few labeled samples are available. As deep neural networks (DNNs) tend to overfit using a few samples only, meta-learning typically uses shallow neural networks (SNNs), thus limiting its effectiveness. In this paper we propose a novel few-shot learning method called meta-transfer learning (MTL) which learns to adapt a deep NN for few shot learning tasks. Specifically, meta refers to training multiple tasks, and transfer is achieved by learning scaling and shifting functions of DNN weights for each task.

Artificial intelligence is everywhere and it's already making a huge impact on our lives. It's autocompleting texts on our cellphones, telling us which videos to watch on YouTube, beating us at video games, recognizing us in photos, ordering products in stores, driving cars, scheduling appointments, you get the idea. Today we're going to explain what AI can (and can't) do right now and explain how we got to where we are today. Crash Course is produced in association with PBS Digital studios. Thanks to the following patrons for their generous monthly contributions that help keep Crash Course free for everyone forever: Eric Prestemon, Sam Buck, Mark Brouwer, Timothy J Kwist, Brian Thomas Gossett, Haxiang N/A Liu, Jonathan Zbikowski, Siobhan Sabino, Zach Van Stanley, Bob Doye, Jennifer Killen, Nathan Catchings, Brandon Westmoreland, dorsey, Indika Siriwardena, Kenneth F Penttinen, Trevin Beattie, Erika & Alexa Saur, Justin Zingsheim, Jessica Wode, Tom Trval, Jason Saslow, Nathan Taylor, Khaled El Shalakany, SR Foxley, Sam Ferguson, Yasenia Cruz, Eric Koslow, Caleb Weeks, Tim Curwick, David Noe, Shawn Arnold, William McGraw, Andrei Krishkevich, Rachel Bright, Jirat, Ian Dundore -- Want to find Crash Course elsewhere on the internet?

Human Resource Development Minister Ramesh Pokhriyal'Nishank' instructed officials of his ministry to develop the syllabus on artificial intelligence introduced by the Central Board of Secondary Education (CBSE) for Class 9 students according to the needs of the industrial sector. The CBSE introduced artificial intelligence as an optional subject for Class 9 from this academic session. IIT Kharagpur too began a six-month course on it recently, the minister said. "The courses of Artificial Intelligence, from school education to higher education level should be designed according to the needs of the industrial sector. There is no dearth of talent among our students. Surely the best results will come out," Nishank said at the launch of two initiatives under the Department of Higher Education at Pravasi Bharatiya Kendra here.

Deliver data-driven results and predict the future by building machine learning models that change every aspect of your business. This 3-day course provides the building blocks of machine learning so students can improve revenue, reduce costs, create new opportunities and learn essential skills for this high-demand field.

Artificial intelligence is growing exponentially. There is no doubt about that. Self-driving cars are clocking up millions of miles, IBM Watson is diagnosing patients better than armies of doctors and Google Deepmind's AlphaGo beat the World champion at Go – a game where intuition plays a key role. But the further AI advances, the more complex become the problems it needs to solve. And only Deep Learning can solve such complex problems and that's why it's at the heart of Artificial intelligence.

In this paper, we propose an AdaBoost - assisted extreme learning machine for efficient online sequential classification (AOS - ELM) . In order to achieve better accuracy in online sequential learning scenarios, we utilize the cost - sensitive algorithm - AdaBoost, which diversifying the weak classifiers, and addin g the forgetting mechanism, which stabilizing the performance during the training procedure . Hence, AOS - ELM adapt s bet ter to sequentially arrived data compared with other voting based methods. The experim ent results show AOS - ELM can achieve 9 4.41 % accuracy on MNIST dataset, which is the theoretical accuracy bound performed by original batch learning algorithm, AdaBoost - EL M. Moreover, with the forgetting mechanism, the standard deviation of accuracy during the online sequential learning process is reduced to 8.26x.