Deep Learning for SEO
Digital Marketing and Search Engine Optimisation, in specific are two fields currently feeling the resonating effects of Neural Networks.
Deep learning is a type of machine learning and Neural Network is a form of Deep Learning.Deep Learning is a subdivision of artificial intelligence. Supervised learning is used by few neural sets– which means that all machine learning takes place with the data that is fed into, Unsupervised learning is used by others, which uses the data divided into groups or categories and others use reinforcement learning. At the moment, neural networks are the most promising for true Artificial Intelligence.
Neural network is a multi-layer network of neurons like the human brain. Inside a human brain, neurons are the basic unit to carry out electrical impulses. Several of these neurons dictate how the body functions, for example lifting a limb, retrieving an old memory. Similarly in computer science, neural networks consist of a series of algorithms which are given enormous amounts of data to analyse the relationship of the data that is fed.
Neural networks are used broadly for several purposes and some of them include marketing, fraud detection , business analytics, risk assessment. Neural networks consist of millions of artificial neurons. Artificial neurons are similar to biological human neurons, except for the fact that instead of electrical impulses, they deal with numbers.
A very basic neural network consists of only three layers, which are input, processing and output. Once the data is fed into the input layer, it is processed internally in multiple ways to determine the relationship of the data and also to have marginally minimal errors. Massive amount of data is fed to neural networks to fully understand the data to mimic desired actions.
There are several types of neural networks. With the forever changing research, there are always new types that keep popping up. There are some basic types of neural networks, which are - feedforward, convolutional, multiplayer, recurrent neural networks.
When users search for anything on the search engine, typically they only look at the first page from the search engine’s results. This makes it very competitive for a website to always show up on top of search results by maintaining its ranking on the search engine. Data science algorithms can help with optimizing the site to improve its performance.
Data science algorithms generally work by processing huge amounts of data repeatedly to deduce the relationship of the data given to it. Understanding this relationship will help in designing strategies which will help improve the traffic, conversion rate, rating and several other factors of the website.
Data science algorithms can help in improving the website’s performance by providing analysis of traffic patterns, keywords that attract the users, bounce rate, conversion rate, topics that users are looking into on the site in particular, better marketing strategies, pages that received most views within the site, relationship between viewership and conversion rate etc.
The results from data science algorithms need to be thoroughly analysed before processing to plan out the next step of dealing with page’s performance. Once the analysis is complete, extensive testing needs to be done and results should be verified with the previous performance of the site. For example, one of the findings from the data science algorithms might suggest the keywords that attract more visitors. By adding these keywords to the site, the site’s performance can be tested using means like viewership, traffic rate, conversion rates to verify if the analysis of the data science algorithms really worked.
There may be a chance of errors in the analysis of data science algorithms as anything related to artificial intelligence will have some degree of error margin. So the more the data that can be input into the data science algorithm, the better it’s analysis to improve the site’s performance.
Google uses neural matching algorithm, Our Platform uses proprietary neural matching. This AI technology helps google to understand better about the language, indepth sentences and also published the Deep Relevance Ranking using intensify Document-Query Interactions.
Most awaited question from SEO's is that What Machine learning and Deep learning mean for future of Search Engine Optimisation. Google advances their Machine learnign and Deep learning capabilities. These are tough topics to understand, Even Data As Services does some level of Machine Learnign for Neural SEO Network. This is tactically and strategically important to understand for SEOs. Google search is going to be your next brain. Major funtions of these machine learning methods are to identify the features in web more precisely, classify types from extraction, extracted features to map and predict, build algorithms that constantly learns and improves with search, finally grade and improve the resutls to the best relevancy. Organising several layers of extracted information connected by semantic relations, so the entire sytem of neural networks will train or will be training continuously to divine coherence to output more relevant results.