NINE STEPS OF NEURAL NETWORK PROJECT
A world without a future may be full of uncertainties that may also result in uncontrolled perils. The world cannot be habitable in this manner of uncertainty and opaqueness about the future. With the advanced technology man had to find solutions to the uncertain future and outcomes shortly. The development of the neural network became a transformative idea and a modern answer to the invisible future. This paper describes more about the neural network and its processes in building a neural network project.
The neural network is defined as a network comprised of artificial neurons that aid in giving answers to artificial problems. The neural network aids in processing models such as predictive models, adaptive control. The neural network also deals with data processing, clustering and analysis as some of the utilities it may offer. There are various ways that one can run and use the neural network in running various jobs. The first way is by building a neural network project which is through various steps. The initialization step is the first thing to do (Olah, and Carter, 2016). This involves importing the data to be used and processed. The other step involves data generation, this involves modifying simple and clear data while assigning the inputs and outputs specific identities. Training test splitting which involves the splitting of the data developed into 30% for testing and 70% for training.
The training is needed for tuning the neural networks while testing is for evaluating the performance when the training is over. The fourth step involved data standardization which involves the adjustment of information in the training set. This is to ensure that the standardized structure is well distributed and the means remain zero and unit variance. Neural net construction, where layers are formed through python (Huang, Cheng, Wang, Li, Shi, and Pan, 2016). Every layer is attached to a neural netlist that represents a complete neural network. Forward propagation is the sixth step, where functions are defined based on weights and biases ion a matrix dimension. Backpropagation which reflects on how the weights and biases are affected by the loss of metrics. The eighth step is iterative optimization, which allows one to balance the metrics. Lastly testing to see how the model works.
References
Huang, Z., Cheng, G., Wang, H., Li, H., Shi, L., & Pan, C. (2016). Building extraction from multi-source remote sensing images via deep deconvolution neural networks. In 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 1835-1838). Ieee.
Olah, C., & Carter, S. (2016). Attention and augmented recurrent neural networks. Distill, 1(9), e1.