If you are a deep learning engineer, you need to have a thorough understanding not only of coding but also of each of the components that go into creating a successful deep learning algorithm.
Example: "The primary function of a neural network is to receive a set of inputs, perform complex calculations and then use the output to solve the problem. A neural network is used for a range of applications. One example is classification; there are many classifiers available today, such as random forest, decision trees, support vector, logistic regression and so on, and of course neural networks."
The most common Neural Networks consist of three network layers :
* An input layer
* A hidden layer (this is the most important layer where feature extraction takes place, and adjustments are made to train faster and function better)
* An output layer
Each sheet contains neurons called “nodes,” performing various operations. Neural Networks are used in deep learning algorithms like CNN, RNN, GAN, etc.
