31

Keras is a simple and high-level neural networks library, written in Python, that works as a wrapper for Tensorflow and Theano. It's easy to learn and use. Using Keras is like working with Lego blocks. It was built so that people can do quick experiments and proofs-of-concept before launching into a full-scale build process. With that in mind, it was made ...


14

Artificial Intelligence is a very broad field and it covers many and very deep areas of computer science, mathematics, hardware design and even biology and psychology. As for the math: I think calculus, statistics and optimization are the most important topics, but learning as much math as you can won't hurt. There are many good free introductory resources ...


10

It doesn't seem expensive at $399* (although the asterix needs to be taken into consideration.) If you're interested in this subject, this may be a decent course, however it is certainly not an accredited institution, thus any "degree" you get from this course will be meaningless in an academic sense. On the other hand, a certificate shows that you've ...


10

Start with Andrew Ng's introduction to Machine Learning course on Coursera. There are not many prerequisites for that course, but you will learn how to make some useful things. And, more importantly, it will clearly show you which subjects you need to learn next.


9

As a full-time AI researcher myself, I'd say that a PhD in machine learning would certainly be one useful option. However, in order make much-needed progress, AI needs avoid falling into the trap of thinking that currently fashionable methods are any kind of 'silver bullet'. There's some danger that a PhD that heads straight into (say) some sub-sub-sub area ...


9

A really good introduction is the Berkeley CS188 class videos and projects. You can find those materials at http://ai.berkeley.edu/home.html You probably also want to get ahold of a copy of Artificial Intelligence: A Modern Approach by Norvig and Russell. For more on the "machine learning" aspects of AI, including an introduction to Neural Networks, take ...


9

To excel in in AI you need a mathematical intuition or point of view. In order to become a full stack AI engineer, it is important that you have a firm understanding of the mathematical foundations of machine learning. My advice to anyone preparing to jump into the field is that learning mathematics is about doing. Remember the 20/80 rule. You need to ...


8

Good Mathematics Foundation Begin by ensuring full competency with intermediate algebra and some other foundations of calculus and discrete math, including the terminology and basic concepts within these topics. Infinite series Logical proofs Linear algebra and matrices Analytic geometry, especially the distinction between local and global extremes (minima ...


7

If you are coming from Java, it would make a lot of sense to play around with deeplearning4J at first. From there I would start learning python as this is the primary language used today in ML. Lectures are a great way to get your feet wet in understanding and applying ML concepts. My favorites are: Stanford ML(Andrew NG): https://www.youtube.com/watch?...


7

If you're doing deep learning (which I assume you are, if you say you want to learn "AI"), then Python is a MUST. Virtually all the big frameworks are Python wrappers over a C++ core. C# has no real deep learning frameworks. There are a couple such as the Microsoft Cognitive Toolkit, but they are on a completely different level from PyTorch or Tensorflow. ...


6

I work as a professor, and recently designed the mathematics requirements for a new AI major, in consultation with many of my colleagues at other institutions. The other answers, particularly @FauChrisian's do a good job of cataloging all the specific topics that might be useful somewhere in AI, but not all of them are equally useful for understanding core ...


6

EE Math Refresh AI is an interdisciplinary field. You can begin by ensuring you are fresh in the mathematics you've already taken. You may already have all the books from your BS and MS. Infinite series Logical proofs Linear algebra and matrices Analytic geometry, especially the distinction between local and global extremes (minima and maxima), saddle ...


5

Research on AI seems to be getting wider these days (2016). First, "obvious" few departments (no order): Computer Science (e.g. computation theory, algorithms): AI researchers there assume that intelligence is a kind of computation, under various forms (e.g. a neural network, a logic system). Software Engineering: Assuming we find a good model for AI, how ...


5

You'll find that both Calculus and Linear Algebra have some application in AI/ML techniques. In many senses, you can argue that most of ML reduces to Linear Algebra, and Calculus is used in, eg. the backpropagation algorithm for training neural networks. You'd be well served to take a class or two in probability and statistics as well. Programming ...


5

AI is quite large in scope and it sits at the intersection of several areas. However, there are a few essential fields or topics that you need to know Set theory Logic Linear algebra Calculus Probability and statistics I would recommend you to first explore the AI algorithms that you might be interested in. I advise you to start with machine learning and ...


5

Actually, you don't need a rigorous study of these subjects to implement Machine Learning Algorithms. Only Probability Theory needs to be treated rigorously in Machine Learning. You can find a very good series of Probability Theory lectures here: Introduction to Probability - The Science of Uncertainty Also, a basic course in Calculus would suffice, for ...


5

AI is a wonderful field to get into. Not only is it in high demand in the job market, it also helps you perceive the world in a whole new way. It's great that you have a deep interest in AI. In my opinion, you'll progress faster if you are having fun. Learning is always accelerated when you are curious and deeply interested in a particular domain or ...


5

This is fairly boilerplate advice, but, since you're brand new to AI, I'd personally suggest writing a classical Tic-Tac-Toe AI, ideally using minimax. I suggest this because minimax is fundamental to AI, and there are many webpages devoted to this subject, such as How to make your Tic Tac Toe game unbeatable by using the minimax algorithm and Tic Tac Toe: ...


5

First of all, you need to realise that you will not be able to do it. Google is a multi-billion dollar company, with a large number of very bright and well-funded researchers. That tells me that it is not something a single person can do by themselves. Then, you already have some pre-conceptions about it. You want to use a machine learning approach, using ...


5

To the good answers here, I would add A brief overview of RL: Most essential concepts in one place. Another brief overview, in presentation format. Ben Recht's An outsider's tour of RL is pretty comprehensive and accessible. The Bellman equations: central to the whole RL theory. Policy gradients explained by Andrej Karpathy (mentioned in other answers as "...


5

Corporations, government research, and academia are favoring C, Python, Java, LISP, and R currently. The trends are not favorable to C# for AI. C#'s peak of use was in the 2009 to 2012 range. By buying GitHub, Microsoft intends to regain some control over development tools and language but has never been particularly successful in either. Even eclipse is ...


5

Using a machine learning or AI-powered model once it has been built and tested, is not directly an AI issue, it is just a development issue. As such, you won't find many machine learning tutorials that focus on this part of the work. But they do exist. In essence it is the same as integrating any other function, which might be in a third-party library: ...


4

If you want a very simple basic book on Neural Networks and not exactly Machine Learning you can try: Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition by Sandhya Samarasinghe Fundamentals of Neural Networks: Architectures, Algorithms And Applications by Laurene V. Fausett These 2 are basic and very ...


4

I would suggest you to start with Andrew Ng's Machine Learning course on Coursera. He provides the brief introduction to mathematics necessary for machine learning. Though not complete, it will be enough to cruise through the course. Next carefully learn logistic regression in the course. The sigmoid function will be widely used in neural networks. In the ...


4

When I got interested in AI, I started with the most basic things. My very first book was Russell&Norvig's Artificial Intelligence- A modern Approach. I think that's a good place to start, even if you're mostly interested in Deep Nets. It treats not just the basic AI concepts and algorithms (expert systems, depth-first and breadth-first search,knowledge ...


4

I recommend python over any other programming language for its availability of libraries. When it comes to machine learning, we have two types of libraries. Deep learning (RNN, CNN, fully connected nets, linear models) classic Machine Learning and the rest (SVM, GBMs, Naive Bayes, Random Forests, K-NN etc) Python has very good libraries in both types. ...


4

As a master's degree student in Artificial Intelligence, I strongly advise you to study some basics in Machine Learning. To do that, you can get a good book (Machine Learning, Tom Mitchell, McGraw Hill, 1997) for the theory and practice by yourself trying some Kaggle competitions. I suggested the book of Mitchell because he is an expert in the field, and ...


4

The most important skill you need is self-discipline. Regarding the mathematical prerequisites, you will need to study statistics, probability theory, calculus, and linear algebra, given that e.g. most machine learning algorithms are highly based on concepts from these areas. Regarding the programming prerequisites, Python and R are usually a good choice, ...


4

I think the key part of your question is "as a beginner". For all intents and purposes you can create a state of the art (SoTA) model in various fields with no knowledge of the mathematics what so ever. This means you do not need to understand back-propagation, gradient descent, or even mathematically how each layer works. Respectively you could just ...


3

I am also new in AI, but in my opinion as professor Andrew Ng said in machine learning course Creating AI application and test it or improve it till become as you looking for is something, and convert it to applicable real application is another thing. In other word, you should develop your app with easy, high level and quick tools like Matlab or Octave (...


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