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- The language must be in python. Neural Network Units weights of [-1.2, -1.1, 3.3, -2.1] 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. 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 examplesplease try to simulate the probability of rolling a Die with Sample Space* S={1,2,3,4,5,6} and the probability of each sample point has a 1/6 chance of occurring, i.e., you need to verify that your simulation converges to 1/6 when you select one point of sample space. When X is a random variable for sample point of rolling a Die, Pr(X<=4)=2/3. Please verify this result by simulation. Please let me know how to make an Excel file as stated above.Suppose we use the following KB (where x,y,z are variables and r1, r2, r3, goal are constants) to determine whether a particular robot can score a) Open(x) ∧ HasBall(x) -> CanScore(x) b) Open(x) ∧ CanAssist(y,x) ∧ HasBall(y) -> CanScore(x) c) PathClear(x,y) -> CanAssist(x,y) d) PathClear(x,z) ∧ CanAssist(z,y) -> CanAssist(x,y) e) PathClear(x,goal) -> Open(x) f) PathClear(y,x) -> PathClear(x,y) g) HasBall(r3) h) PathClear(r1, goal) i) PathClear(r2, r1) j) PathClear(r3, r2) k) PathClear(r3, goal) Intuitively, CanScore(x) means x can score on goal. CanAssist(x,y) means there exists some series of passes that can get the ball from x to y. Open(x) means x can shoot on goal directly. And PathClear(x,y) means the path between x and y is clear. Provide a SLD-derivation for the query CanScore(x) in which the answer provided is r1. Provide a SLD-derivation for the query CanScore(x) in which the answer provided is r3. How many "distinct" derivations (i.e., involving different…
- Computer Science A way to avoid overfitting in Deep Neural Networks is to add an additional term R to the loss function L (for example L can be the cross entropy loss) as follows: L(w) + λR(w). (1) You know that one choice for R is the L2 norm, i.e. R(w) = ||w||2 2 . One friend of yours from the School of Maths told you however that there’s no need to use squares (i.e. powers of two) and that you can achieve the same effect by using absolute values, i.e. the L1 norm: R(w) = ||w||1. Would you agree with him? i.e. is the use of the L2 norm equivalent to using the L1 norm for regularization purposes? Justify your answerThe correct statements are: Group of answer choices Google’s pagerank values can be considered as long-term visit rates of pages from a random walker on the Internet graph. Markov models can be used to compute pagerank values. Markov models are stochastic finite state machines. Markov models can be used to generate random music.How make simulation Aquarium Automatic Care (AOC) system (incl. coding) with Arduino. AOC is simulated using Tinkercad based on below conditions? Conditions: 1. Water must be replaced when -Water level (T) < 41 mm -Discard water and fill 30% of Aquarium water volume when high Acidic pH (pH> 9)2. Fish food (pellets): 1 x 4.5 mL/day at 6am in the morning, the machine only contains 100 mL (warning on D-day is over)3. To save electricity, the aquarium lights are on at night (8 hours)
- Suppose we use the following KB (where x, y, z are variables and r1, r2, r3, goal are constants) to determine whether a particular robot can score. (a) Open(x) ∧ HasBall(x) → CanScore(x)(b) Open(x) ∧ CanAssist(y, x) ∧ HasBall(y) → CanScore(x) (c) PathClear(x,y) → CanAsist(x,y)(d) PathClear(x,z) ∧ CanAssist(z,y) → CanAssist(x,y) (e) PathClear(x,goal) → Open(x)(f) PathClear(y,x) → PathClear(x,y) (g) HasBall(r3)(h) PathClear(r1,goal) (i) PathClear(r2,r1) (j) PathClear(r3,r2) (k) PathClear(r3,goal)Suppose we use the following KB (where x, y, z are variables and r1, r2, r3, goal are constants) to determine whether a particular robot can score. (a) Open(x) ∧ HasBall(x) → CanScore(x)(b) Open(x) ∧ CanAssist(y, x) ∧ HasBall(y) → CanScore(x) (c) PathClear(x,y) → CanAsist(x,y)(d) PathClear(x,z) ∧ CanAssist(z,y) → CanAssist(x,y) (e) PathClear(x,goal) → Open(x)(f) PathClear(y,x) → PathClear(x,y) (g) HasBall(r3)(h) PathClear(r1,goal) (i) PathClear(r2,r1) (j) PathClear(r3,r2) (k) PathClear(r3,goal) Intuitively, CanScore(x) means x can score on goal. CanAssist(x, y) means there exists some series of passes that can get the ball from x to y. Open(x) means x can shoot on goal directly. And P athClear(x, y) means the path between x and y is clear. Provide a SLD-derivation for the query CanScore(x) in which the answer provided is r1. Provide a SLD-derivation for the query CanScore(x) in which the answer provided is r3. How many “distinct” derivations (i.e., involving different…MATLAB Project : Fuzzy Control of Air-conditioner • Use M a x -Min O p e r a tio n & S u g e n o’s Sim plifie d FI E • RoomTemperature ~ x, Outside Temperature ~ y Representative Values forx & y : [20 22.5 25 27.5 30 32.5 35] (oC) • Motor Speed forCompressor ~ z : [0 500 1000 1500 2000] (rpm) • Rule 1: IF (x is LOW [1.0 0.8 0.6 0.4 0.2 0 0]) AND (y is HIGH [00 0.2 0.4 0.6 0.8 1.0]) THEN (z is SLOW [0.6 1.0 0.6 0.2 0] ~ 500rpm) • Rule 2: IF (x is HIGH [0 0 0.2 0.4 0.6 0.8 1.0]) AND (y is LOW[1.0 0.8 0.6 0.4 0.2 0 0]) THEN (z is MEDIUM [0 0.5 1.0 0.5 0] ~1000 rpm) • Rule 3: IF (x is HIGH [0 0 0.2 0.4 0.6 0.8 1.0]) AND (y is HIGH[0 0 0.2 0.4 0.6 0.8 1.0]) THEN (z is FAST [0 0.2 0.6 1.0 0.6] ~1500 rpm) • Rule 4: IF (x is LOW [1.0 0.8 0.6 0.4 0.2 0 0]) AND (y is LOW[1.0 0.8 0.6 0.4 0.2 0 0]) THEN (z s STOP [1.0 0.6 0.2 0 0] ~ 0rpm) • Draw 3D plots for control surface z = f(x,y), andInterpolate x, y, z axes with 31, 31, 21 points, resp. • Outputs àM-Script file “your_program.m” and…
- By using neural network algorithm solve the XOR problem. Where X1 and X2 are inputs Y is the output. Learning rate is 0.5. Threshold is 1 if weighted sum >= 0.5 otherwise 0 and weights w1 and w2 = 0.9 Add 17 with learning rate and w1, w2 before start solving the question. X1 X2 Y 0 0 0 0 1 1 1 0 1 1 1 0Correct answer will be upvoted else downvoted. There are n focuses on an endless plane. The I-th point has facilitates (xi,yi) to such an extent that xi>0 and yi>0. The directions are not really integer. In one maneuver you play out the accompanying activities: pick two focuses an and b (a≠b); move point a from (xa,ya) to either (xa+1,ya) or (xa,ya+1); move point b from (xb,yb) to either (xb+1,yb) or (xb,yb+1); eliminate focuses an and b. Notwithstanding, the move must be performed if there exists a line that goes through the new organizes of, another directions of b and (0,0). If not, the move can't be performed and the focuses stay at their unique directions (xa,ya) and (xb,yb), individually. Input The main line contains a solitary integer n (1≤n≤2⋅105) — the number of focuses. The I-th of the following n lines contains four integers ai,bi,ci,di (1≤ai,bi,ci,di≤109). The directions of the I-th point are xi=aibi and yi=cidi. Output :In the primary…Correct answer will be upvoted else Multiple Downvoted. Don't submit random answer. Computer science. How about we consider a line with n shops. The shops are numbered with integers from 1 to n from left to right. The expense of a dinner in the I-th shop is equivalent to man-made intelligence. You should handle q questions of two sorts: 1 x y: for each shop 1≤i≤x set ai=max(ai,y). 2 x y: we should think about an eager man with y cash. He visits the shops from x-th shop to n-th and if he can purchase a feast in the current shop he gets one thing of it. Observe the number of dinners he will buy. The man can purchase a feast in the shop I in the event that he has essentially simulated intelligence cash, and after it his cash diminishes by man-made intelligence. Input The main line contains two integers n, q (1≤n,q≤2⋅105). The subsequent line contains n integers a1,a2,… ,an (1≤ai≤109) — the expenses of the suppers. It is ensured, that a1≥a2≥… ≥an. Every one of the…