Can anyone suggest reference books to start with AI? Preferably, I am looking for books that provide source code in Java or Python.
For a foundation, there is nothing better than Cybernetics by Norbert Wiener. It is surprising how advanced this MIT professor was, prior to Turing's thought experiment on a general purpose computing machine or the embodiment of the von Neumann architecture upon which most contemporary computers are based. In key ways his analysis of time series and stability in biological and electronic control systems is far enough before his time that most of academia has yet to catch up.
Moving forward to the 1990s, a clear picture of the achivements arising out of government funded work at MIT, Princeton, and other universites, along with the relationship between most of the key AI sub-fields is presented cleanly in, Mathematical Methods in Artificial Intelligence, by Edward A. Bender, 1996.
Moving into the machine learning sector of AI, the quintessential book arising from academics associated with Berkeley, Google, and the NSF is Foundations of Machine Learning Mohri, Rostamizadeh,and Talwalkar, 2012, MIT Press. It presents the PAC learning framework, which is important for any industrial strength machine learning, beyond the experimentation of a home computing hobbyist.
Two O'Reilly books are quite comprehensive: Hands-On Machine Learning with Scikit-Learn and TensorFlow by Géron and Deep Learning by Patterson and Gibson.
This overview of Java libraries is well written and the main Python libraries are Tensorflow, PyTorch, Scipy, Scikit-Learn, and a few others.
Both Java and Python implementations rely on wrappers in those languages that call underlying C/C++ for mapping tensor operations to acceleration hardware in emerging chips by VLSI manufacturers like NVidia and Intel. These architectures facilitate both parallelism and the division of AI algorithms between software and hardware.
This will improve the speed of experiments in learning. It is up to the student whether to take the time to develop an understanding on how to minimize convergence time during learning early or later in the course of study.
A. M. Buckley: “Pixar: The Company and Its Founders” is an easy to read introduction into applied Artificial Intelligence which contains many insight story from computer history. The approach of combining technology with art is attractive for many people in the field. In contrast to other famous books, e.g. by Russell&Norvig, the idea behind Pixar was to visualize the result and see the computer as a medium for telling a plot.
Was also asked here How to get started in AI? and here's a similar too: How does one start learning artificial intelligence?
hmmmmm, the first question would be: In which direction do you want to go: Research Robotics BusinessIntelligence etc.
And do you have access to a fair amount of correlated data?
AI is very broad and there are lots of different applications.
How good is your mathematic background and how much do you know about AI?
If you think the knowledge is pretty fair, then go ahead and test yourself building an AI for a "game" in a worldwide competition:
Otherwise I would suggest you to read, or to copy and play with existing code like e.g. search algorithms like these: https://github.com/JayakrishnaThota/Algorithms-and-Data-Structures or any NeuralNetwork you can find, but remember that neural networks have to be trained so you need data.
Here is a little bit of a glimpse
Note ; This question is kinda of saying I'd like to learn more about science...anyone tell me where to start from? " ,So next time try to do some revision before posting.
That said, ArtificialIntelligence is a broad field besides the umbrella of computer science and other fields like Biology/Neuroscience...etc.; therefore,there are hundreds of books,if not thousands of Books out there,and any University for instance; Oxford & Tokyo Institute of Technology,have atleast several(probably 10 or more) courses at the graduate level concerning Artificial Intelligence.
And also there are numerous journals/articles dedicated to this as well,which have been published for decades.Not forgetting Conferences. Therefore,check out this link