Latest / Neural Network / The Basics of Neural Networks - Understanding the Fundamentals

Transcript

The Basics of Neural Networks - Understanding the Fundamentals · Neural Network

• 0:02
  Neural networks are a type of machine learning that can be used for a variety
• 0:05
  of tasks such as image recognition, natural language processing, and prediction. Neural
• 0:11
  networks are based on the structure of the human brain and can be used to
• 0:14
  identify patterns in large data sets and then make predictions or decisions based on those
• 0:19
  patterns. At its core, a neural network is composed of layers of artificial
• 0:23
  neurons that are interconnected to form a network. These neurons work together to process
• 0:27
  information and make decisions based on the input they receive. The input to a
• 0:31
  neural network is called the feature vector, which is a list of values that
• 0:34
  represent the features of the input data. For example, if the input data
• 0:38
  is an image, the feature vector would be a list of pixel values that
• 0:42
  represent the image. The first layer of the neural network is called the input
• 0:46
  layer, and it receives the feature vector as input. The subsequent layers are
• 0:50
  called hidden layers because they are not directly connected to the input or output layers.
• 0:54
  The output layer produces the final output of the neural network. Neurons in
• 0:58
  each layer are connected to neurons and the next layer by weights, which are
• 1:00
  used to adjust the strength of the connection between the neurons. During training,
• 1:04
  the neural network learns the appropriate weights by adjusting them to minimize the difference between
• 1:08
  the predicted output and the actual output. Neural networks use an activation function to
• 1:14
  determine the output of each neuron in the network. The activation function takes the
• 1:18
  weighted sum of the inputs to the neuron and applies a nonlinear function to produce
• 1:22
  the output. The most commonly used activation functions are the sigmoid function and the
• 1:26
  rl you function. The process of training a neural network involves feeding it a
• 1:30
  set of input data along with the desired output. The neural network then adjusts
• 1:34
  its weights to produce output that is as close as possible to the desired output.
• 1:38
  This process is repeated many times until the network produces accurate output for a
• 1:42
  wide variety of input data. Neural networks are capable of learning complex relationships between
• 1:49
  inputs and outputs, making them useful for a wide variety of tasks. However,
• 1:53
  training a neural network can be a time consuming and computationally intensive process.
• 1:57
  In summary, neural networks are a type of machine learning that is based on
• 2:01
  the structure of the human brain. They are composed of layers of artificial neurons
• 2:06
  that work together to process information and make decisions based on the input they receive.
• 2:09
  During training, the neural network learns the appropriate weights and activation functions to
• 2:15
  produce accurate output for a wide variety of input data. Understanding the basics of
• 2:19
  neural networks is an essential step in developing expertise in this exciting and rapidly growing feel