Machine Learning
Cód:
491_9781802347906
? ? ? 55% OFF for Bookstores! ? ? ?Are you thinking that as much as we want to look for logical frameworks for intelligence, there is no certainty or scientific proof that intelligence is as structured as we believe it to be?Your Customers Will Never Stop to Use this Awesome Book!As in the evolutionary process, where chaos and order wisely coexist, I see a research gap related to our brain and mind, typically related to focusing on models based solely on order.But if we are researching Artificial Intelligence, why are we so attached to the order and models that are supposed to be those of our brain?Or, what binds us so much to what we see only, without opening spaces to what we dont see, if only to consider them small pieces of chaos?In this openness and vision, when it comes to intelligence, I propose a new concept: that of unstructured intelligence, which I will try to explain in this book.In this book, you will learn:Automatic LearningMachine Learning ParadigmsInductive LearningInduction Of Decision TreesThe relevance of attributesAlgorithmsClusterAnd Much more...I think one of the main reasons for AIs long winter was that we went deep into it, creating architectures focused on existing paradigms, with little investment in new technologies and standards, such as machine learning itself.But are we arent repeating the same mistake in this new wave of AI?If so, I consider the main mistake too much focus on artificial neural network architectures, as if this was the solution to solving complex learning problems in the human pattern or even the main door to generic artificial intelligence with semantic analysis capabilities.And a possible solution to avoid the same history of past failure, perhaps, is to tackle high complexity real-world learning problems collectively and collaboratively, such as creating AI systems that can teach them to learn for themselves, like us humans.So the architecture that seems to be the most logical for such problems is precisely
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