How to design your own deep neural network?

There are several ways of designing the deep neural network, but it depends on the programmer choosing their preferences. Some elements cannot be avoided in programming. In this article, you will have a perfect piece of knowledge on the steps included in designing the deep neural network. One of the foremost things that developers must keep in mind is the necessity for Deep Learning Network Optimization. Without optimization, it is not possible to have an efficient neural network.

 

What is Neural Network?

 

Primarily, the neural network means the mathematical function that works as an input to have the desired output. In the case of introductory text, the NN stands for brain analogies. Several things are included in the Neural Network. In the below section, the components are mentioned:

 

• An input layer, x

 

• A considerable amount of secret layers

 

• Output layer, y

 

• The specific set of weights and biases within each layer of W and b

 

• Preferred activation function for every secret layer.

 

The article discusses the creation of a Neural Network with the help of Python. Therefore, all the equations are related to Python programming.

 

Steps Required to Develop Neural Network

 

In this section, all the desired processes and steps are mentioned systematically to develop a Neural Network based on Python algorithms.

 

Neural Network Training 

 

There is a simple output y of a two-layer Neural Network. The equation stands as:

 

 

 

From the above equation, it is clear that weights W and the biases b affect y. This means that due to the slightest change in W and b, the entire output alternates. The value of weights and biases helps in determining the values of the predictions. Therefore, the process of tuning the weights and biases appropriately that helps to process the input data is called Neural Network Training.

 

Feedforward

 

The inclusion of the feedforward function in the sequential graph helps to develop the neural network. In developing the NN, you must assume that the biases are 0.

These are some primary steps that are included in developing the Neural Network. Now, let’s discuss the challenges that developers face when they optimize the neural network.

 

What are the Challenges Faced in Optimizing the Neural Network?

 

When discussing the optimization in the neural networks, the non-convex optimization.

 

Convex optimization

 

There is no need for local optima for the convex optimization issues, making them easy to solve. You will find these introductory chapters in the graduate and undergraduate optimization courses.

 

Non-convex optimization

 

It requires a function with several optima; within these lots, there will be one optimum that acts as global optima. This is one of the most prominent Online Deep Learning Network Optimization. 

 

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