# Neural Networks 101

Input

x

y

output

Input

x

y

output

function

f(x)

Input

x

y

output

f(x)
f(x) = mx + b

b

m

m: slope

b: intercept

Input

x

y

output

f(x)
f(x) = mx + b

b

m

m: slope

b: intercept

parameters

Input

x

y

output

f(x)
f(x) = mx + b

b

m

m: slope

b: intercept

parameters

x

y

goal: find the right m and b that turn x into y

goal: find the right m and b that turn x into y

Input

x

y

output

f(x)
f(x) = mx + b

b

m

m: slope

b: intercept

parameters

x

y

learn

goal: find the right m and b that turn x into y

goal: find the right m and b that turn x into y

what is machine learning?

1

# what is machine learning?

ML: any model with parameters learnt from the data

Input

x

y

output

f(x) = mx + b

m = 0.4 and b=0

m: slope

b: intercept

parameters

x

L2 = (y_{1,p} - y_{1,t})^2 + (y_{2,p} - y_{2,t})^2 + (y_{3,p} - y_{3,t})^2
y_{3,p}
y_{3,t}

let's try

goal: learn the right m and b that turn x into y

f(x)

m

L2

-1.4

-.5

.6

1.5

2.4

L2 = (y_{1,p} - y_{1,t})^2 + (y_{2,p} - y_{2,t})^2 + (y_{3,p} - y_{3,t})^2
1 ~\mathrm{if} ~\sum_{i=1}^Nw_ix_i \geq\theta ~\mathrm{else}~ 0

w1

w1

w2

w2

The perceptron algorithm : 1958, Frank Rosenblatt

1958

Perceptron

The perceptron algorithm : 1958, Frank Rosenblatt

.

.

.

x_1
x_2
x_N
+b

output

weights

w_i

bias

b

linear regression:

w_2
w_1
w_N
1 ~\mathrm{if} ~\sum_{i=1}^Nw_ix_i \geq\theta ~\mathrm{else}~ 0

1958

Perceptron

y= \begin{cases} 1~ if~ \sum_i(x_i w_i) + b ~>=~Z\\ 0 ~if~ \sum_i(x_i w_i) + b ~<~Z \end{cases}

.

.

.

x_1
x_2
x_N
+b
f
w_2
w_1
w_N

output

f

activation function

weights

w_i

bias

b
y ~= f(~\sum_i w_ix_i ~+~ b)

The perceptron algorithm : 1958, Frank Rosenblatt

Perceptrons are linear classifiers: makes its predictions based on a linear predictor function

combining a set of weights (=parameters) with the feature vector.

Perceptron

The perceptron algorithm : 1958, Frank Rosenblatt

+b
f
w_2
w_1
w_N

output

f

activation function

weights

w_i

bias

b

sigmoid

f
\sigma = \frac{1}{1 + e^{-z}}

.

.

.

x_1
x_2
x_N
y ~= f(~\sum_i w_ix_i ~+~ b)

Perceptrons are linear classifiers: makes its predictions based on a linear predictor function

combining a set of weights (=parameters) with the feature vector.

Perceptron

ANN examples of activation function

The perceptron algorithm : 1958, Frank Rosenblatt

Perceptron

The Navy revealed the embryo of an electronic computer today that it expects will be able to walk, talk, see, write, reproduce itself and be conscious of its existence.

The embryo - the Weather Buerau's \$2,000,000 "704" computer - learned to differentiate between left and right after 50 attempts in the Navy demonstration

# NEW NAVY DEVICE LEARNS BY DOING; Psychologist Shows Embryo of Computer Designed to Read and Grow Wiser

July 8, 1958

Input

x

y

output

f(x)

x

y

A Neural Network is a kind of function that maps input to output

# multilayer perceptron

x_2
x_3

output

x_1

layer of perceptrons

b_1
b_2
b_3
b_4
b

# multilayer perceptron

x_2
x_3

output

input layer

hidden layer

output layer

1970: multilayer perceptron architecture

x_1

Fully connected: all nodes go to all nodes of the next layer.

b_1
b_2
b_3
b_4

# multilayer perceptron

x_2
x_3

output

x_1

layer of perceptrons

b_1
b_2
b_3
b_4
b
w_{11}
w_{12}
w_{13}
w_{14}

# multilayer perceptron

x_2
x_3

output

x_1

layer of perceptrons

b_1
b_2
b_3
b_4
b
w_{21}
w_{22}
w_{23}
w_{24}

# multilayer perceptron

layer of perceptrons

x_2
x_3

output

x_1

layer of perceptrons

b_1
b_2
b_3
b_4
b
w_{31}
w_{32}
w_{33}
w_{34}

# multilayer perceptron

x_2
x_3

output

Fully connected: all nodes go to all nodes of the next layer.

layer of perceptrons

x_1
w_{11}x_1 + w_{12}x_2 + w_{13}x_3 + w_{14}x_4 + b1

# multilayer perceptron

x_2
x_3

output

Fully connected: all nodes go to all nodes of the next layer.

layer of perceptrons

w_{11}x_1 + w_{12}x_2 + w_{13}x_3 + b1
w_{21}x_1 + w_{22}x_2 + w_{23}x_3 + b2
w_{31}x_1 + w_{32}x_2 + w_{33}x_3 + b3
w_{41}x_1 + w_{42}x_2 + w_{43}x_3 + b4
x_1

w: weight

sets the sensitivity of a neuron

b: bias:

up-down weights a neuron

learned parameters

# multilayer perceptron

x_2
x_3

output

Fully connected: all nodes go to all nodes of the next layer.

layer of perceptrons

f(w_{11}x_1 + w_{12}x_2 + w_{13}x_3 + b1)
f(w_{21}x_1 + w_{22}x_2 + w_{23}x_3 + b1)
f(w_{31}x_1 + w_{32}x_2 + w_{33}x_3 + b1)
f(w_{41}x_1 + w_{42}x_2 + w_{43}x_3 + b1)
x_1

w: weight

sets the sensitivity of a neuron

b: bias:

up-down weights a neuron

f: activation function:

turns neurons on-off

# DNN:

hyperparameters of DNN

3

# EXERCISE

output

how many parameters?

input layer

hidden layer

output layer

hidden layer

# EXERCISE

output

how many parameters?

input layer

hidden layer

output layer

hidden layer

output

input layer

hidden layer

output layer

hidden layer

35

(3x4)+4

(4x3)+3

how many parameters?

# EXERCISE

(3)+1

output

input layer

hidden layer

output layer

hidden layer

1. number of layers-  1
2. number of neurons/layer-
3. activation function/layer-
4. layer connectivity-
5. optimization metric - 1
6. optimization method - 1
7. parameters in optimization- M
N_l
N_l ^ {~??}

how many hyperparameters?

# EXERCISE

GREEN: architecture hyperparameters

RED: training hyperparameters

N_l

# EXERCISE

Lots of parameters and lots of hyperparameters! What to choose?

cheatsheet

1. architecture - wide networks tend to overfit, deep networks are hard to train

2. number of epochs - the sweet spot is when learning slows down, but before you start overfitting... it may take DAYS! jumps may indicate bad initial choices (like in all gradient descent)

3. loss function - needs to be appropriate to the task, e.g. classification vs regression

4. activation functions - needs to be consistent with the loss function

5. optimization scheme - needs to be appropriate to the task and data

6. learning rate in optimization - balance speed and accuracy

7. batch size - smaller batch size is faster but leads to overtraining

An article that compars various DNNs

An article that compars various DNNs

accuracy comparison

An article that compars various DNNs

batch size

Lots of parameters and lots of hyperparameters! What to choose?

cheatsheet

1. architecture - wide networks tend to overfit, deep networks are hard to train

2. number of epochs - the sweet spot is when learning slows down, but before you start overfitting... it may take DAYS! jumps may indicate bad initial choices
3. loss function - needs to be appropriate to the task, e.g. classification vs regression

4. activation functions - needs to be consistent with the loss function

5. optimization scheme - needs to be appropriate to the task and data

6. learning rate in optimization - balance speed and accuracy

7. batch size - smaller batch size is faster but leads to overtraining
5
1

What should I choose for the loss function and how does that relate to the activation functiom and optimization?

Lots of parameters and lots of hyperparameters! What to choose?

Lots of parameters and lots of hyperparameters! What to choose?

cheatsheet

always check your loss function! it should go down smoothly and flatten out at the end of the training.

not flat? you are still learning!

too flat? you are overfitting...

loss  (gallery of horrors)

jumps are not unlikely (and not necessarily a problem) if your activations are discontinuous (e.g. relu)

when you use validation you are introducing regularizations (e.g. dropout) so the loss can be smaller than for the training set

loss and learning rate (not that the appropriate learning rate depends on the chosen optimization scheme!)

Building a DNN

with keras and tensorflow

autoencoder for image recontstruction

What should I choose for the loss function and how does that relate to the activation functiom and optimization?

loss good for activation last layer size last layer
mean_squared_error regression linear one node
mean_absolute_error regression linear one node
mean_squared_logarithmit_error ​regression linear one node
binary_crossentropy binary classification sigmoid one node
categorical_crossentropy multiclass classification sigmoid N nodes
Kullback_Divergence multiclass classification, probabilistic inerpretation sigmoid N nodes

On the interpretability of DNNs

# resources

NN are a vast topics and we only have 2 weeks!

Some FREE references!

michael nielsen

better pedagogical approach, more basic, more clear

ian goodfellow

michael nielsen

better pedagogical approach, more basic, more clear

An article that compars various DNNs

accuracy comparison

#### deck

By federica bianco

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