I am starting a series of blog explaining concept of Machine Learning and Deep Learning or can say will provide short notes from following books.

The solution to the above problem is to allow computers to learn from experience and understand the world in terms of a hierarchy of concepts, with each concept deﬁned in terms of its relation to simpler concepts.

In the case of probabilistic models, a good representation is often one that captures the posterior distribution of the underlying explanatory factors for the observed input(We will revisit this topic later in greater detail).

An autoencoder is the combination of an encoder function that converts the input data into a diﬀerent representation, and a decoder function that converts the new representation back into the original format.

It is not always clear which of these two views — the depth of the computational graph, or the depth of the probabilistic modeling graph — is most relevant, and because diﬀerent people choose diﬀerent sets of smallest elements from which to construct their graphs, there is no single correct value for the depth of an architecture, just as there is no single correct value for the length of a computer program.

Nor is there a consensus about how much depth a model requires to qualify as “deep.” Coming on to the division of various type of learning Fig 5 will give you a great idea about the difference and similarity between them.

These models were designed to take a set of n input values x1, . . . , xn and associate them with an output y.These models would learn a set of weights w1, . . . , wn and compute their output f(x, w) =x1*w1+···+xn*wn.

Most famously, they cannot learn theXOR function, where f([0,1], w) = 1 and f([1,0], w) = 1 but f([1,1], w) = 0 and f([0,0], w) = 0(Fig 7).

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