AI News, advanced topics in artificial intelligence

Decade-long Curriculum for Solving Intelligence

If you are an individual passionate about modern word combination “Artificial Intelligence” more broadly than “plug Keras here” ,“connect SQL there “ and all these questions learnable in a few weeks or months from scratch to pass interviews, then this sequence of blog posts might be valuable for your purposes.

Despite the fact this curriculum was built with certain global goal kept in mind, which states solving intelligence as one of the most valuable achievements for the future of mankind at current stage of scientific-technological progress, it still handles socio-industrial environments’ common demands for research-oriented engineers or scientsits in domains of Computer Science and Artificial Intelligence.

Hawking — Large Scale Structure of Space-Time Basis: Foundations Stewart Calculus Strang Geometry Fenn Linear Algebra Strang Probablities Ross Probability and Statistics DeGroot Convex Optimization Boyd Advanced basis: Math analysis Rudin Logic Herrick Introduction to Set Theory Jech Calculus Apostol Vol.1, Vol.2 Linear Algebra Mehrmann Linear Algebra Handbook Hogben Probability Theory Yanes Probability Theory Loeve Vol.1, Vol.2 Graduate general: Set Theory Jech Abstract Algebra Foote Algebra Jacobson Vol.1, Vol.2 Metric and Topological Spaces Sutherland Measture Theory Terence Tao General Topology Willard Topological Manifolds Lee Real and complex analysis Rudin Functional Analysis Rudin Differential Geometry Spivak, Vol.1–5 ODE Arnol’d PDE Taylor Vol.1–3 Погружение в мир нейронных сетей Николенко (Certainly worth reading if you know russian language at decent level) Mathematics for machine learning Deisenroth Machine Learning Stephen Machine Learning Murphy Elements of Statistical Learning Friendman Deep Learning Bengio Reinforcement Learning Sutton Information Theory MacKay Informational Geometry Amari Theoretical: Algorithms Kormen Concrete Mathematics Knuth Game Theory Tadelis Game Theory Neumann Graph theory Bollobas Language Theory Pierce Intro to automata theory Ulman Art of programming Knuth Computer Systems: Engineering a Compiler Cooper Computer Architecture Tanenbaum Operating Systems Tanenbaum Behavioral Biology Sapolsky Neurobilogy Kandel Cognitive Neuroscience Gazzaniga Computational Neurosciene Abbott Neuroanatomy through Clinical Cases Basic clinical Neuroscience Additional biology to cover: Campbel’s Biology Molecular biology of the cell Alberts Molecular biology of the gene Watson All the CS courses are from: General: Deep Learning Coursera CS229 + T CS230 CS236 CV: CS231 N + A CS331 A + B Self-driving cars Yolo CV NLP: CS224 + N RL: CS234 CS332 Github course Berkeley course Deepmind lectures Programming Paradigms(CS106 + CS107) Algorithms Stanford Coursera Cryptography Stanford Coursera Game Theory Stanford Coursera Social and Economic Networks Stanford Coursera Circuits and Computer Architecture MIT EdX

Advanced Topics in Deep Convolutional Neural Networks

However, we know that companies such as Google and Microsoft have dedicated teams of data scientists that have spent years developing exceptional networks for the purpose of image classification — why not just use these networks as a starting point for your own image classification projects?

Obviously, just purely transferring the model will not be helpful, you must still train the network on your new data, but it is common to freeze the weights of the former layers as these are more generalized features that will likely be unchanged during training.

Transfer learning works best for problems that are fairly general and there are networks freely available online (such as image analysis) and when the user has a relatively small dataset available such that it is insufficient to train a neural network — this is a fairly common problem.

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Smart phones, smart watches, and wearable sensors are commonplace with most adults in the OECD countries having multiple devices.Machines and components of machines are now equipped with 'intelligence', allowing machine to machine, machine to person and machine to computer connection.We live and work in the Internet of Things that can automate or support context aware processes, creating the Internet of Everything.

Advanced Topics in Artificial Intelligence 2nd Advanced Course, ACAI '87, Oslo, Norway, July 28 A

Reinforcement Learning 8: Advanced Topics in Deep RL

Machine Learning vs Deep Learning vs Artificial Intelligence | ML vs DL vs AI | Simplilearn

This Machine Learning vs Deep Learning vs Artificial Intelligence video will help you understand the differences between ML, DL and AI, and how they are ...

Stanford Seminar - Artificial Intelligence: Current and Future Paradigms and Implications

EE380: Computer Systems Colloquium Seminar Artificial Intelligence: Current and Future Paradigms and Implications Speaker: Scott Phoenix, Vicarious ...

Advanced Topics in ML Lecture 10 Reinforcement Learning 5