Interactive Intro
Neural Networks
Problem
- How can we intuitively understand the neural net and apply cutting edge algorithms with intuition?
Scene 1 | Goal Supervised
Interactive Intro
Neural Networks
Definition
- You were just given a task to predict the house price given our input data- size of house in m2. Let's use industry standard Neural networks - which finds the relationship between the input and output data so that we can make predictions based on data it has never seen before.
Scene 2 | Goal Supervised
Interactive Intro
Neural Networks
Breakdown
- Show does it do that?
- Let's take a lesson from Feynman and break this complicated architecture down into simple components so that we can understand it.
Scene 3| Goal Supervised
Interactive Intro
Neural Networks
Define Neuron
Scene 2 | Goal Supervised
Our input data does not change, its the truth, we can't change the radius squared in this case. That's our x value.
Our slope which you will see as w, is what our neural network can change in order to make better predictions and learn.
Finally, the b value is our y-intercept which the neural net can change in order to make better initial starting points
Interactive Intro
Neural Networks
Illustrate equation
Scene 2 | Goal Supervised
So let's say we have a house of 5 Square meters, costing $2M and 9 Square meters costing $2.5M. What if we had 10 Square meters, what's the price? So our neural network will create a general function that will make the optimal predictions for us given input and output data.
Interactive Intro
Neural Networks
Illustrate equation
Scene 2 | Goal Supervised
So by changing our "w" value, we can see that the slope or the rise over run, changes to make better predictions.
But how good are those predictions? Our cost function will tell us the error of our predictions through the equation.
Prediction - Actual Price.
We then square it in order to make it absolute
Interactive Intro
Neural Networks
Illustrate equation
Scene 2 | Goal Supervised
So let's first manually change the weight in order to make the best outcome. So we
adjust the weights a little, adjust the bias. And, finally, we got the right outcome!
But wait, that was manual. How do you think a machine can do this automatically and learn from its mistakes using deep learning. How does your Tesla car learn, how do you automate reporting, and how do financial predictions actually work. Find out now
Chapter 1 | Goal Supervised
Interactive Intro
Neural Networks
Neuron
Chapter 1 | Goal Supervised
Interactive Intro
Neural Networks
Neuron
Chapter 1 | Goal Supervised
- But what if we had more than one input?
- Surface Area + Location?
Interactive Intro
Neural Networks
Neuron
Chapter 1 | Goal Supervised
Interactive Intro
Neural Networks
Chapter 1 | Goal Supervised
Interactive Intro
Neural Networks
Neuron and synaptic weight
Chapter 1 | Goal Supervised
- synaptic plasticity is the ability of synapses to strengthen or weaken over time, in response to increases or decreases in their activity
Interactive Intro
Neural Networks
Chapter 1 | Goal Supervised
Interactive Intro
Neural Networks
Chapter 1 | Goal Supervised
Interactive Intro
Neural Networks
Function
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A function is a block of code which only runs when it is called.
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You can pass data, known as parameters, into a function.
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A function can return data as a result.
Chapter 1 | Intro
Create a Function
To create a function, use the keyword def Name():
Example
Create a function named NeuralNet, with a property named and call it
Chapter 1 | Python, class intro
Optimization Parameters
Chapter 5 | Forward propagation Intro
Chapter | Forward propagation Intro
Chapter | Forward propagation Intro
Chapter | Sigmoid Function
OK but what is a cost function?
Chapter | Cost
Chapter | Error Calcuation
Chapter | Error Calcuation
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