Multiplying Matrices Scalar Multiplication each element in a

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Multiplying Matrices

Multiplying Matrices

Scalar Multiplication - each element in a matrix is multiplied by a constant.

Scalar Multiplication - each element in a matrix is multiplied by a constant.

**Multiply rows times columns. **You can only multiply if the number of columns in

**Multiply rows times columns. **You can only multiply if the number of columns in the 1 st matrix is equal to the number of rows in the 2 nd matrix. They must match. Dimensions: 2 x 3 3 x 2 The dimensions of your answer.

Examples: 2(3) + -1(5) 3(3) + 4(5) 2(-9) + -1(7) 2(2) + -1(-6) 3(-9)

Examples: 2(3) + -1(5) 3(3) + 4(5) 2(-9) + -1(7) 2(2) + -1(-6) 3(-9) + 4(7) 3(2) + 4(-6)

Dimensions: 2 x 3 2 x 2 *They don’t match so can’t be multiplied

Dimensions: 2 x 3 2 x 2 *They don’t match so can’t be multiplied together. *

*Answer should be a 2 x 2 2 x 2 0(4) + (-1)(-2) 1(4)

*Answer should be a 2 x 2 2 x 2 0(4) + (-1)(-2) 1(4) + 0(-2) 2 x 2 0(-3) + (-1)(5) 1(-3) +0(5)

Sigmoid function

Sigmoid function

Sigmoid function 미분

Sigmoid function 미분

Neural Networks: Representation Non-linear hypotheses Machine Learning

Neural Networks: Representation Non-linear hypotheses Machine Learning

Neural Networks: Representation Model representation I Machine Learning

Neural Networks: Representation Model representation I Machine Learning

Neuron model: Logistic unit Sigmoid (logistic) activation function. Andrew Ng

Neuron model: Logistic unit Sigmoid (logistic) activation function. Andrew Ng

Neural Network Layer 1 Layer 2 Layer 3 Andrew Ng

Neural Network Layer 1 Layer 2 Layer 3 Andrew Ng

Neural Network “activation” of unit in layer matrix of weights controlling function mapping from

Neural Network “activation” of unit in layer matrix of weights controlling function mapping from layer to layer If network has units in layer , will be of dimension . units in layer , then Andrew Ng

Neural Networks: Representation Model representation II Machine Learning

Neural Networks: Representation Model representation II Machine Learning

Forward propagation: Vectorized implementation Add . Andrew Ng

Forward propagation: Vectorized implementation Add . Andrew Ng

Neural Network learning its own features Layer 1 Layer 2 Layer 3 Andrew Ng

Neural Network learning its own features Layer 1 Layer 2 Layer 3 Andrew Ng

Other network architectures Layer 1 Layer 2 Layer 3 Layer 4 Andrew Ng

Other network architectures Layer 1 Layer 2 Layer 3 Layer 4 Andrew Ng

Neural Networks: Representation Examples and intuitions I Machine Learning

Neural Networks: Representation Examples and intuitions I Machine Learning

Non-linear classification example: XOR/XNOR , are binary (0 or 1). x 2 x 1

Non-linear classification example: XOR/XNOR , are binary (0 or 1). x 2 x 1 Andrew Ng

Simple example: AND 1. 0 0 0 1 1 0 1 Andrew Ng

Simple example: AND 1. 0 0 0 1 1 0 1 Andrew Ng

Example: OR function -10 20 20 0 0 1 1 0 1 Andrew Ng

Example: OR function -10 20 20 0 0 1 1 0 1 Andrew Ng

Neural Networks: Representation Examples and intuitions II Machine Learning

Neural Networks: Representation Examples and intuitions II Machine Learning

Negation: 0 1 Andrew Ng

Negation: 0 1 Andrew Ng

Putting it together: -30 10 -10 20 -20 20 0 0 1 1 0

Putting it together: -30 10 -10 20 -20 20 0 0 1 1 0 1 Andrew Ng

Neural Network intuition Layer 1 Layer 2 Layer 3 Layer 4 Andrew Ng

Neural Network intuition Layer 1 Layer 2 Layer 3 Layer 4 Andrew Ng

Handwritten digit classification [Courtesy of Yann Le. Cun] Andrew Ng

Handwritten digit classification [Courtesy of Yann Le. Cun] Andrew Ng

Neural Networks: Representation Multi-classification Machine Learning Andrew Ng

Neural Networks: Representation Multi-classification Machine Learning Andrew Ng

Multiple output units: One-vs-all. Pedestrian Want Car , when pedestrian Motorcycle , when car

Multiple output units: One-vs-all. Pedestrian Want Car , when pedestrian Motorcycle , when car Truck , etc. when motorcycle Andrew Ng

Multiple output units: One-vs-all. Want , when pedestrian , when car , etc. when

Multiple output units: One-vs-all. Want , when pedestrian , when car , etc. when motorcycle Training set: one of , , , pedestrian car motorcycle truck Andrew Ng