Elements of Data Science, Machine Learning and Artificial Intelligence

  Deep Learning my takeaway from the from the book by Goodfellow, Bengio and Courville Introduction Today, artificial intelligence (AI) is a thriving field with many practical applications and active research topics. The true challenge to artificial intelligence proved to be solving the tasks that are easy for people to perform but hard for people to describe formally—problems that we solve intuitively, that feel automatic, like recognizing spoken words or faces in images . By gathering knowledge from experience, this approach avoids the need for human operators to formally specify all of the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones. If we draw a graph showing how these. concepts are built on top of each other, the graph is deep, with many layers. For this reason, we call this approach to AI deep learning . Several artificial intelligence projects have sought to hard-code ...

Let us continue with our journey further with the AI, a modern approach.   In this chapter we look into how an AI agent achieves a goal with a sequence of actions. These types of agents are also known as problems solving agents. Before this we will just look at the diagram below.   In an atomic representation each state of the world is indivisible, it has no internal structure. A factored representation splits up each state into a fixed set of variables or attributes, each of which can have a value. Structured representations underlie relational databases and first-order logic. The axis along which atomic, factored, and structured representations lie is the axis of increasing expressiveness. Roughly speaking, a more expressive representation can capture, at least as concisely, everything a less expressive one can capture, plus some more.   PROBLEM-SOLVING AGENTS   Whenever we have to solve a problem, what has to be achieved, has to be analyzed in the first plac...