Google is Exploring the Neural Network Architecture to Boost Its Services
Since the Apple has debuted its neural engine a number of smartphone OEMs and tech firms has started using the neural network to advance the capabilities and features of their services or products. Google has also started making use of the machine learning to explore the benefits of the neural network architecture and it has helped in backing some of its popular services with neural network architecture. Google has successfully brought the deep learning backed performance boost on image recognition, speech recognition and machine translation with neural network architecture.
Neural Network Architecture: Manual development of machine learning models
The machine learning models with neural network architecture had been designed by the Google engineers and scientists after years of research and development. The search space utilized on all this models happens to extremely large that is typical 10-layer network. The process involved with designing the network is time consuming and the experiments performed in getting the same requires some high end machine learning expertise. In order to get rid of the manual method of designing machine learning models for the neural network architecture Google has come up with a new solution of simply automating the design. Working on the evolutionary and evolutionary learning algorithms has shown immense promise and it will be great for the future capability enhancement on the Google products and services.
New approach better capability with neural network architecture
Google has crafted a new approach named ‘AutoML’ wherein a controller neural net is used to impose a ‘child’ model architecture. Later on this neural controller can be used to train and work on a particular task and then it is used to evaluate. Then the feedback is utilized to make controller aware of the shortcoming and improvements to be made to get the optimum performance. In this way Google goes on implementing the neural network architecture backing while generating new architectures which are further improved upon through rigorous testing and feedback from the generated from the usage.
Finding the right neural network architecture
Google has used its approach to experiment ion the bench marked data sets right with the deep learning for the image recognition related processes. The neural network architecture chosen and generated by the machine learning helps in bringing a number of specific features. It also helps in bringing a number of new elements like ten presence of the multicative combination which are not common in the recurrent networks. Google has went a simpler form of this very approach which is suggested by the human designers rather the artificial intelligence. The best thing about using the multicative combination is that it helps in alleviating gradient vanishing along with exploding issues. On other the machine chose architecture apt at finding a useful and smart neural net architecture for this job.
Google research on neural network architecture has shown that certain types of neural nets works better than other but the time taken in synchronizing it with machine learning is quite astounding. Google is carefully analyzing and testing the machine-generated architecture which essentially helps in creating neural nets tailored to fulfill specific needs of the users.
