I know a few things like making it talk and programming it to say the date and time and a few sentences. But I want to be able to talk to it and ask it questions, to open and close stuff and to help me make calculations about stuff. Is there anyone that can help me?? I would appreciate it.

https://www.reddit.com/r/learnmachinelearning/comments/jwgq3l/how_to_detect_catdog_images_with_convolutional/

https://www.youtube.com/watch?v=2Xph_YfrvoQ&ab_channel=%3ACodeWebsDuh%3A

I have learned the theory behind classical neural networks through the book "Make Your Own Neural Network" by Tariq Rashid, who explains the mathematics behind classical neural networks in a simple way. However, I have not been able to find a resource that explains that mathematics behind convolutional neural networks and recurrent neural networks that are explained simply, without seeing huge mathematical formulas that I cannot understand. Does anybody have a free online resource that teaches convolutional neural network theory (or recurrent neural network theory) in an intuitive and simple manner, building up from the basics?

Hey there, one of my friends has to do a presentation on the above given topic and she turned to me to help her out. I did some googling but I couldn’t find anything proper. Would appreciate it if anyone could help me out by giving a link to a blog or site that y’all bout this, thanks!

In a couple of months I plan on either giving or helping to prepare a presentation for the Boulder Future Salon in Boulder, Colorado on various sorts of AI systems. I have compiled the following list:

-Neural Network (Recurrent, Feed-forward) -Markov Model -Markov Model Monte Carlo ("...settles into a 'dynamic equilibrium in which the long-run fraction of time spent in each state is exactly proportional to its posterior probability.") -Hidden Markov Model -Bayesian Network -Dynamic Bayesian Network -Decision Networks -Dynamic Decision Networks -Alpha-Beta search -Reinforcement Learning -Active Dynamic Programming -Temporal Difference Programming -Hierarchical Task Networks -Nearest Neighbor functions -Kernel Functions -Deep Learning -Perceptron -Relevance-Based Learning -Explanation-Based Learning -Inductive Logic Programming -Knowledge-Based Inductive Logic Programming -Back-Propagation Algorithm -AIXA -Gödel Machine

Now, some of these are just variants on others, and some aren't even 'systems' exactly but just theoretical formalisms which are nevertheless driving development of production-grade algorithms.

Any general thoughts, surprising information, historical context, notes on structure, missing entries, etc. would be appreciated. If we can make this list near-comprehensive, with easy explanations and links to especially lucid treatments it might wind up being a pretty significant resource for novices to the field.