Building your first neural network can seem like a daunting task, especially if you’re new to the world of artificial intelligence (AI). However, with a basic understanding of AI concepts and some practical steps, you can successfully construct your initial neural network.

Artificial Intelligence is a rapidly growing field that seeks to create machines capable of intelligent behavior. Among the various techniques employed in AI, one particularly powerful method is the use of neural networks. Neural networks are computing systems inspired by the human brain’s biological neural networks. They learn from data inputs and improve their performance over time.

To build your first create image with neural network, you need to understand its fundamental components: neurons or nodes, layers, weights and biases, activation functions, and backpropagation. A neuron takes in inputs from other neurons or an external source (like data), processes it using an activation function that decides whether it should be activated based on its weighted sum plus bias.

The neurons are grouped into layers – input layer where data enters the system; output layer where final predictions are made; and hidden layers which perform computations between input and output layers. The weights denote how important a particular input is while biases help in shifting the activation function curve up or down for better fitting.

Next comes choosing an appropriate activation function which determines whether a neuron should be activated or not based on its inputs’ weighted sum plus bias value. Commonly used activation functions include ReLU (Rectified Linear Unit), sigmoid function etc., each having their own advantages depending upon different scenarios.

Once these components are set up correctly within a framework such as TensorFlow or Keras- both open-source software libraries for machine learning- we feed our model with training data so it can learn patterns from this information through forward propagation.

However, just feeding forward isn’t enough because initially our model would make many errors in prediction due to random initialization of weights and biases. Here comes another crucial concept called backpropagation which essentially involves tweaking the weights and biases based on the error or loss function to minimize the prediction errors.

Building a neural network is not about merely setting up layers and feeding data into it. It’s an iterative process of training, testing, tuning parameters, adjusting weights and biases until you achieve a model that can make accurate predictions.

As a beginner in AI, building your first neural network might seem complex but remember that every expert was once a beginner. Start with simple networks and gradually move onto more complex structures as you gain confidence. There are numerous resources online including tutorials, courses, forums etc., which can guide you through this exciting journey into the world of artificial intelligence.