What is a process that converts data (words) into a format (numbers) that machine learning can understand? An example is shown below: cat mat on sat the the => 00001 cat => 10000 sat => 00010

Draw a state diagram of a Turing machine (TM) recognizing the fol-lowing language over the alphabet Σ = {0, 1, >}.L = {x > y : x, y ∈L(1(0∪1)∗) ∧bin(x) is a power of 2 ∧bin(x) > bin(y)},where bin(x) is the value of x viewed as a binary number.Note that besides edge labels like 0 → 1, R, we allow abbreviationslike 0 →R, standing for 0 →0, R,and 0, 1 →R, standing for 0 →0, R, 1 →1, R.

The algorithms we will discuss are based on a fairly wide variety of fundamental learning techniques such as information theory, gradient descent, euclidean distance, Bayes’ theorem, and a number of other techniques.Which of those do you suppose is used by the following algorithms:

The language must be in python. The code for parts 1-4 is below. Just do parts 5-7 Neural Network Units Implement a single sigmoid neural network unit with weights of [-1.2, -1.1, 3.3, -2.1] Calculate the outputs for two training examples:Example 1: [0.9, 10.0, 3.1, 1]Example 2: [0.9, 2.1, 3.7, 1] Note that you don't have to explicitly include a threshold or bias since the examples include a last element of 1 which means that the last weight effectively operates as a threshold. Assuming that a sigmoid unit response >0.5 denotes a positive class and <0.5 is negative class, is example 1 positive or negative?  is example 2 positive or negative? Create a single ReLU unit and provide the outputs for those examples. Calculate the derivative of the sigmoid with respect to net input for both examples Calculate the derivative of the ReLU with respect to net input for both examples     import numpy as np import math  def forward(x,w):    t = np.dot(x.T,w)    t = 1/(1+math.e**(-t))…