A collaborative course incorporating labs in TensorFlow and peer brainstorming along with lectures.

Here at Robohub, we're celebrating by sharing our list of 20 robot related books (in no particular order) aimed at engaging kids and/or teens with everything robotics. Ranging from funny fictional narratives to DIY drone building and coding, these books are sure to stoke the imaginations of budding innovators and creators. If you can think of any other books that deserve inclusion, we'd love to hear about them! Robot Building for Teens will teach you how to get started in this fascinating hobby. The projects in this book include the "Digital Brain Robot," the "Bug Bot," and even a robot on wheels!

We have put together a list of highly rated and most enrolled online courses on Artificial intelligence, machine learning, deep learning. The list will keep on increasing as and when we find more resources. Consider bookmarking this page and come back often to see newly added courses. The course is created by Lazy Programmer Inc. and has currently 2513 students enrolled with a feedback score of 4.6 out of 5. It is listed as the best selling Udemy course on Artificial Intelligence.

A few weeks ago, I wrote about how and why I was learning Machine Learning, mainly through Andrew Ng's Coursera course. Machine Learning is built on prerequisites, so much so that learning by first principles seems overwhelming. Do you really need to spend a month learning linear algebra? You'll be okay if you have some math and programming experience. You really just have to be familiar with Sigma notation and be able to express it in a for loop. Sure, your assignments will take longer to complete and the first few times you see those giant equations your head will spin, but you can do this!

This is a guest post by Lauren Delapenha. She is an editor at DiscoverDataScience.com, a one-stop resource for learning about the rapidly-evolving field of data science through comprehensive education and career guides. According to IBM, the world generates 2.5 quintillion bytes of data every day. A decent chunk of those quintillion bytes is made up of people asking the experts how to break into and excel in the dynamic, lucrative field of data science. An even larger chunk of those bytes consists of convoluted, contradicting answers to that question.

Here at Udacity, we are tremendously excited to announce the kick-off of the second term of our Artificial Intelligence Nanodegree program. Because we are able to provide a depth of education that is commensurate with university education; because we are bridging the gap between universities and industry by providing you with hands-on projects and partnering with the top industries in the field; and last but certainly not least, because we are able to bring this education to many more people across the globe, at a cost that makes a top-notch AI education realistic for all aspiring learners. During the first term, you've enjoyed learning about Game Playing Agents, Simulated Annealing, Constraint Satisfaction, Logic and Planning, and Probabilistic AI from some of the biggest names in the field: Sebastian Thrun, Peter Norvig, and Thad Starner. Term 2 will be focused on one of the cutting-edge advancements of AI -- Deep Learning. In this Term, you will learn about the foundations of neural networks, understand how to train these neural networks with techniques such as gradient descent and backpropagation, and learn different types of architectures that make neural networks work for a variety of different applications.

Chances are, if you are searching for a tutorial on artificial neural networks (ANN) you already have some idea of what they are, and what they are capable of doing. But did you know that neural networks are the foundation of the new and exciting field of deep learning? Deep learning is the field of machine learning that is making many state-of-the-art advancements, from beating players at Go and Poker, to speeding up drug discovery and assisting self-driving cars. If these types of cutting edge applications excite you like they excite me, then you will be interesting in learning as much as you can about deep learning. However, that requires you to know quite a bit about how neural networks work. This tutorial article is designed to help you get up to speed in neural networks as quickly as possible. In this tutorial I'll be presenting some concepts, code and maths that will enable you to build and understand a simple neural network. Some tutorials focus only on the code and skip the maths – but this impedes understanding. I'll take things as slowly as possible, but it might help to brush up on your matrices and differentiation if you need to. The code will be in Python, so it will be beneficial if you have a basic understanding of how Python works. You'll pretty much get away with knowing about Python functions, loops and the basics of the numpy library. By the end of this neural networks tutorial you'll be able to build an ANN in Python that will correctly classify handwritten digits in images with a fair degree of accuracy. Once you're done with this tutorial, you can dive a little deeper with the following posts: All of the relevant code in this tutorial can be found here. Here's an outline of the tutorial, with links, so you can easily navigate to the parts you want: Artificial neural networks (ANNs) are software implementations of the neuronal structure of our brains. We don't need to talk about the complex biology of our brain structures, but suffice to say, the brain contains neurons which are kind of like organic switches. These can change their output state depending on the strength of their electrical or chemical input. The neural network in a person's brain is a hugely interconnected network of neurons, where the output of any given neuron may be the input to thousands of other neurons.

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In this paper we introduce a model of lifelong learning, based on a Network of Experts. New tasks / experts are learned and added to the model sequentially, building on what was learned before. To ensure scalability of this process, data from previous tasks cannot be stored and hence is not available when learning a new task. A critical issue in such context, not addressed in the literature so far, relates to the decision which expert to deploy at test time. We introduce a set of gating autoencoders that learn a representation for the task at hand, and, at test time, automatically forward the test sample to the relevant expert. This also brings memory efficiency as only one expert network has to be loaded into memory at any given time. Further, the autoencoders inherently capture the relatedness of one task to another, based on which the most relevant prior model to be used for training a new expert, with fine-tuning or learningwithout-forgetting, can be selected. We evaluate our method on image classification and video prediction problems.