Neural Network Basics

Neural Network Basics

Neural Networks Introduction brilliant course,neural network learning

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Introduce to Neural Networks: Brilliant Courses, Neural Network Learning
Introduce to Neural Networks: Brilliant Courses: 
https://brilliant.org/courses/intro-neural-networks/
Quickly delve into one of the most cutting-edge computational learning methods.
This interactive course delves into the fundamentals of artificial neural networks, from basic frameworks to more modern techniques like adversarial models.
You'll answer questions like how computers differentiate between cat and dog images, and how to learn to play chess well.
The course includes 28 interactive quizzes, 210+ concepts and exercises, content that progresses from basic to advanced with inspiring examples, and an interactive online system, for instance, the first lesson:
Both dogs and cats are fluffy animals with four legs and many common features. So, why are they easy to distinguish?
When we were still children, we were told which animals are dogs and which are cats. Soon, we no longer needed new examples. Our learning ability is strong enough that even when a new animal doesn't look particularly similar to one we've seen before, we can classify it as a dog or a cat. It turns out that computers can also learn in a similar way.
Experience the quiz from the first lesson, you can definitely do it!
A supervised learning algorithm attempts to model a function to relate inputs to outputs. It uses known examples to learn this relationship.
When building a supervised learning model to distinguish whether an image is of a dog or a cat, what should the inputs for the examples be?
Quiz 1 
LEARNING PROBLEMS FOR NEURAL NETWORKS
         
The fur and eye colors of dogs and cats
The lengths and weights of dogs and cats
Numerical data representing images of dogs and cats
The quiz translation is as follows
A supervised learning algorithm attempts to model a function to relate inputs to outputs. It uses known examples to learn this relationship.
When building a supervised learning model to distinguish whether an image is of a dog or a cat, what should the inputs for the examples be?
Quiz 1: Neural Network Learning Problems, choose which option?
  •          
  • The fur and eye colors of dogs and cats
  • The lengths and weights of dogs and cats
  • Numerical data representing images of dogs and cats
The answer is 3
If you want to identify whether an animal is a dog or a cat, your input would naturally be images of dogs and cats. Typically, these data are represented by a numerical matrix, with the numbers corresponding to each pixel in the image.
Moreover, this data will be extremely useful for training the model because it allows computers to "see" images and find patterns between them, thus learning in a way similar to humans. Of course, computers will need more examples than humans to successfully distinguish the differences between most dogs and cats.
Consisting of eight modules and 27 units