Redes Neuronales (NN)
Neural Networks es uno de los descubrimientos más significativos de la historia.
Las Redes Neuronales pueden resolver problemas que no pueden ser resueltos por algoritmos:
- Diagnostico medico
- Detección de rostro
- Reconocimiento de voz
Las Redes Neuronales son la esencia del Aprendizaje Profundo .
La revolución del aprendizaje profundo
¡La revolución del aprendizaje profundo está aquí!
La revolución del aprendizaje profundo comenzó alrededor de 2010. Desde entonces, el aprendizaje profundo ha resuelto muchos problemas "irresolubles".
La revolución del aprendizaje profundo no comenzó con un solo descubrimiento. Más o menos sucedió cuando varios factores necesarios estaban listos:
- Las computadoras eran lo suficientemente rápidas
- El almacenamiento de la computadora era lo suficientemente grande
- Se inventaron mejores métodos de entrenamiento.
- Se inventaron mejores métodos de sintonización.
neuronas
Los científicos coinciden en que nuestro cerebro tiene alrededor de 100 mil millones de neuronas.
Estas neuronas tienen cientos de miles de millones de conexiones entre ellas.
Crédito de la imagen: Universidad de 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.