Reti neurali (NN)
Le reti neurali è una delle scoperte più significative della storia.
Le reti neurali possono risolvere problemi che non possono essere risolti da algoritmi:
- Diagnosi medica
- Riconoscimento facciale
- Riconoscimento vocale
Le reti neurali sono l'essenza del Deep Learning .
La rivoluzione del deep learning
La rivoluzione del deep learning è qui!
La rivoluzione del deep learning è iniziata intorno al 2010. Da allora, il Deep Learning ha risolto molti problemi "irrisolvibili".
La rivoluzione del deep learning non è stata avviata da una singola scoperta. Più o meno è successo quando diversi fattori necessari erano pronti:
- I computer erano abbastanza veloci
- La memoria del computer era abbastanza grande
- Sono stati inventati metodi di allenamento migliori
- Sono stati inventati metodi di messa a punto migliori
Neuroni
Gli scienziati concordano sul fatto che il nostro cervello ha circa 100 miliardi di neuroni.
Questi neuroni hanno centinaia di miliardi di connessioni tra di loro.
Credito immagine: Università di Basilea, Biozentrum.
Neurons (aka Nerve Cells) are the fundamental units of our brain and nervous system.
The neurons are responsible for receiving input from the external world, for sending output (commands to our muscles), and for transforming the electrical signals in between.
Neural Networks
Artificial Neural Networks are normally called Neural Networks (NN).
Neural networks are in fact multi-layer Perceptrons.
The perceptron defines the first step into multi-layered neural networks.
The Neural Network Model
Input data (Yellow) are processed against a hidden layer (Blue) and modified against another hidden layer (Green) to produce the final output (Red).
Neural Networks with JavaScript
Artificial Intelligence can be math-heavy. The nature of neural networks is highly technical, and the jargon that goes along with it tends to scare people away.
This is were JavaScript can come to help. We need easy to understand software APIs to simplifying the process of creating and training neural networks.
JavaScript Libraries
Brain.js
Brain.js is a JavaScript library that makes it easy to understand Neural Networks because it hides the complexity of the mathematics.
Building a neural network with Brain.js.
Introduction to ml5.js
ml5.js is trying to make machine learning more accessible to a wider audience.
The ml5 team is working to wrap machine learning functionality in friendlier ways.
The example below uses only three lines of code to classify an image:
<img id="image" src="pic1.jpg" width="100%">
<script>
const classifier = ml5.imageClassifier('MobileNet');
classifier.classify(document.getElementById("image"), gotResult);
function gotResult(error, results) { ... }
</script>
Try substitute "pic1.jpg" with "pic2.jpg" and "pic3.jpg".
TensorFlow Playground
TensorFlow Playground is a web application written in d3.js.
With TensorFlow Playground you can learn about Neural Networks (NN) without math.
In your own Web Browser you can create a Neural Network and see the result.
TensorFlow.js was previously called Tf.js and Deeplearn.js.
Tom Mitchell
Tom Michael Mitchell (born 1951) is an American computer scientist and University Professor at the Carnegie Mellon University (CMU).
He is a former Chair of the Machine Learning Department at CMU.
E: Experience (the number of times).
T: The Task (driving a car).
P: The Performance (good or bad).
Stories
Giraffe
In 2015, Matthew Lai, a student at Imperial College in London created a neural network called Giraffe.
Giraffe could be trained in 72 hours to play chess at the same level as an international master.
Computers playing chess are not new, but the way this program was created was new.
Smart chess playing programs take years to build, while Giraffe was built in 72 hours with a neural network.