Usually, when talking about regularization for neural networks there are 3 main types:
L1, L2 and dropout. All affect the gradient descent procedure.
L1 and L2 regularization is implemented in the loss function, and therefore are part of gradient descent directly by altering the derivatives of the loss function thereby altering the weight update rules of ...
It is really simple.
In gradient descent not using mini-batches, you feed your entire training set of data into the network and accumulate a cost function based on this full set of data. Then you use gradient descent to adjust the network weights to minimize the cost. Then you repeat this process until you get a satisfactory level of accuracy. For example, ...
(All notations based on Understanding ML: From Theory to Algorithms) The layman's term for NFL is super misleading. The comparison between PAC learnability and NFL is kind of baseless since both proof's are built on a different set of assumptions.
Let's review the definition of PAC learnability:
A hypothesis class $H$ is PAC learnable if there exist a ...
need a large human-labeled training set
brittle (doesn't work well with examples that are in a different genre from the training set)
only requires a small set of labeled data (seed relations)
complex iterative process
tl;dr The equivalent to a neuron in a Fully-Connected (FC) layer is the kernel (or filter) of a Convolution layer
The neurons of these two types of layers have two key differences. These are that the convolution layers implement:
Sparse connectivity, i.e. each neuron is connected only to an area of the input, not the whole.
Weight sharing, i.e....
I would say that the logic behind the introduction was more empirical than technical. The only difference between LSTM and Bi-LSTM is the possibility for Bi-LSTM to leverage future context chunks to learn better representations of single words. There is no special training step or units added, the idea is just to read a sentence forward and backward to ...
Can we say that $Q^\pi(s, a) = V^\pi(s)$
The correct relationship is this:
$$V^\pi(s) = \sum_a \pi(a|s) Q^\pi(s, a)$$
or, if you have a deterministic policy $a = \pi(s)$ you can instead write:
$$V^\pi(s) = Q^\pi(s, \pi(s))$$
Intuitively, this is because the $V^\pi(s)$ is the expected future return when following the policy $\pi$ from state $s$, ...
As you can find here:
Evolutionary algorithms form a subset of evolutionary computation in that they generally only involve techniques implementing mechanisms inspired by biological evolution such as reproduction, mutation, recombination, natural selection, and survival of the fittest.
It means, other types of evolutions, which are not necessarily a ...
It's a very simplified explantion. I am just talking about the core idea.
A neural network is a combination of many layers.
A neural network (Multiple Layer Perceptron: Regular neural network ): It does a linear combination (a mathematical operation) between the previous layer's output and the current layer's weights(vectors) and then it passes data to ...
Everything you say in your post is correct, apart from the wrong assumption that policy iteration is model-free. PI is a model-based algorithm because of the reasons you're mentioning.
See my answer to the question What's the difference between model-free and model-based reinforcement learning?.
First of all, I would like to say that it is possible that these terms are used inconsistently, given that at least transfer learning, AFAIK, is a relatively new expression, so, the general trick is to take terminology, notation and definitions with a grain of salt. However, in this case, although it may sound confusing to you, all of the current ...
The difference really comes down to the fact that in meta-learning, there is a population of tasks $\tau$ which have distribution $p(\tau)$. The goal is to perform well on a task drawn from $p(\tau)$. Generally 'perform well' means that with only a few training steps or data points, the model can give good classification accuracy, achieve high reward in an ...