Shallow vs Deep Neural Networks
Shallow neural networks sometimes have one or two hidden layers
Deep neural networks have three or extra hidden layers and plenty of neurons in every layer
Deep neural networks can course of uncooked knowledge like pictures and textual content, whereas shallow networks solely take vector inputs
Traits of Deep Neural Networks
Deep neural networks can routinely extract needed options from uncooked knowledge
They carry out higher with bigger quantities of information, not like typical machine studying algorithms
Deep neural networks are efficient at avoiding overfitting when skilled with massive datasets
Elements Behind the Deep Studying Growth
1. Developments within the discipline:
Introduction of ReLU activation operate helped overcome the vanishing gradient drawback
This development enabled the creation of very deep neural networks
2. Availability of information:
Deep neural networks require massive quantities of information for optimum efficiency
The elevated availability of information has facilitated experimentation with deep studying algorithms
3. Improved computational energy:
Highly effective GPUs have considerably diminished coaching time for deep neural networks
Sooner coaching permits for extra experimentation and prototyping
Affect on Machine Studying
Deep studying algorithms proceed to enhance with extra knowledge, not like typical machine studying strategies
The mix of elevated knowledge availability and computational energy has accelerated progress within the discipline
Historic Context
Neural networks have existed for a while however solely just lately turned “deep”
The latest developments have led to quite a few thrilling functions in varied fields
