Neural Network: How to Create One?

1. Neural Network: How to Create One? Neural networks are at the foundation of deep learning. With relevant data and computational power, they can be used to solve the most complicated problems. Using Python or R library can be used to create a neural network and train it on any dataset and get great accuracy. They can be treated as black-box and use them without any difficulty. But even if it seems easy to go that way, it is exciting to find out what lies behind these algorithms and how they work. Some examples of the neural network are: Banking- Most banks are betting their future on this technology. It’s being used to predict how much funds are required to be put inside the ATM to make the best of each trip. They are replacing the old technology to detect frauds and disputed credit card transactions. Advertisement. Big advertising companies like Google AdSense use the neural networks to optimize their ad-choice in relevancy. It results in better targeting of ads and an increase in the click-through- rate. Healthcare. Many companies are using tech to solve the problems that could not be solved earlier. For instance, it is used in clinical imaging to help the doctors in reading the MRIs and genomics to read the DNA sequence.

2. Automotive. Self-driving cars are are a good example of use of neural network. Details of Neural Network The neural network is a technology which is based on the structure of the neurons that are there in our brain. Each neuron works as a processing tool inside our brains. Every neuron tries to stimulate the others using its terminals and tell which terminal should be active and which one should not be when communicating with each other. With the help of over across multiple neurons, the brain can process complicated things and solve the complex problems. How to Make a Neural Network with Python? Python is the library with a complete set of Neural Network libraries. Keras is a popular neural network library TensorFlow. Due to high- level abstraction, there is no need to build a low-level algorithm. Python, along with library TensorFlow, is needed that includes the Keras. After the data is available, the next step is train-validation-test split. Extensive training data makes the network get trained better. The more trained you are, the better you would work. Next is the validation of data followed by the final validation of data. Finally, The answer to the question ‘how to make a neural network’ can’t be answered in simple ways and requires a lot more study.