The past few years have witnessed tremendous growth of machine learning across various industries. From being a technology of the future, machine learning is now providing resources for billion-dollar businesses. One of the latest trend observed in this field is the application of statistical mechanics to process complex information. The areas where statistical mechanics is applied ranges from natural models of learning to cryptosystems and error correcting codes. This article discusses how has statistical mechanics influenced machine learning.
What is Statistical Mechanics?
Statistical mechanics is a prominent subject of the modern day’s physics. The fundamental study of any physical system with large numbers of degrees of freedom requires statistical mechanics. This approach makes use of probability theory, statistical methods and microscopic laws.
The statistical mechanics enables a better study of how macroscopic concepts such as temperature and pressure are related to the descriptions of the microscopic state which shifts around an average state. This helps us to connect the thermodynamic quantities such as heat capacity to the microscopic behavior. In classical thermodynamics, the only feasible option to do this is measure and tabulate all such quantities for each material.
Also, it can be used to study the systems that are in a non-equilibrium state. Statistical mechanics is often used for microscopically modeling the speed of irreversible processes. Chemical reactions or flows of particles and heat are examples of such processes.
So, How is it Influencing Machine Learning?
Anyone who has been following machine learning training would have heard about the backpropagation method used to train the neural networks. The main advantage of this method is the reduced loss functions and thereby improved accuracy. There is a relationship between the loss functions and many-dimensional space of the model’s coefficients. So, it is very beneficent to make the analogy to another many-dimensional minimization problem, potential energy minimization of the many-body physical system.
A statistical mechanical technique, called simulated annealing is used to find the energy minimum of a theoretical model for a condensed matter system. It involves simulating the motion of particles according to the physical laws with the temperature reducing from a higher to lower temperature gradually. With proper scheduling of the temperature reduction, we can settle the system into the lowest energy basin. In complex systems, it is often found that achieving global minimum every time is not possible. However, a more accurate value than that of the standard gradient descent method can be found.
Because of the similarities between the neural network loss functions and many-particle potential energy functions, simulated annealing has also been found to be applicable for training the artificial neural networks. Other many techniques used for minimizing artificial neural networks also use such analogies to physics. So basically, statistical mechanics and its techniques are being applied to improve machine learning, especially the deep learning algorithms.
If you find machine learning interesting and worth making a career out of it, join a machine learning course to know more about this. Also, in this time of data revolution, a machine learning certification can be very useful for your career prospects.
Day: June 6, 2019
Is Lisk the best Blockchain?
Lisk is a platform, out of many platform coins seeking to serve the broader applications of blockchain technology. Lisk is opposed to Bitcoin which is a digital currency. It is in its earlier stages of development and Lisk is being tested by multiple companies testing multiple methods in a race towards mass adoption.
The History of Lisk
Lisk was initially called Crypti and was created in September 2014. According to their Crunchbase profile, it was created as a fully stacked solution to deploy truly decentralized applications onto the blockchain. Founders Max Kordek and Oliver Beddows created the open source dapp platform to inspire more blockchain developers to participate in the cryptocurrency space. The team released their ICO in Q1 2016 and sold 100 million of their native tokens, LSK, in return for 14,000 BTC which was worth $5.8 million at that time. Since its inception and ICO, the team has made steady progress on the project – from the implementation of their road-map to the Q1 2018 rebranding.
Why is Lisk different?
Lisk is different than its competitors due to two major reasons – JavaScript and Dapp Sidechains.
JavaScript
According to a survey by the 2018 LinkedIn Emerging Jobs Report, the job market for blockchain has seen a 33 percent growth in the last year. Lisk is trying to help the blockchain job marketplace by letting dapp developers use JavaScript, which has continuously been the most popular language for programming for the past 6 to 8 years. A large number of websites use Javascript, which gives allows Lisk apps to easily mesh and connect with most of the internet. Lisk might turn out to be the topmost option when developers are looking for a platform to build apps with if the demand for developers continues to expand and JavaScript is still as popular.
Lisk Software Development Kit (SDK)
Lisk also offers the Lisk App SDK in order to make dapp development easy for blockchain developers. The Lisk Too SDK is a framework to deploy sidechains and develop blockchain applications. JavaScript is used to write everything – which means that one can develop platform-independent social networks, contract execution platforms, games, messengers, exchanges, prediction markets, online shops, loT applications, online shops and much more on one’s own blockchain, fully decentralized and without the trouble of complex consensus protocols pr P2P
This points to another part of Lisk that differentiates it from its competitors: Sidechains.
Dapp Sidechains
One of the central reasons why blockchain training is built is to increase the scalability of blockchain technology. This needs a platform to manage large amounts of activity and transactions happening constantly on their blockchain which has to be thoroughly planned, as seen with Ethereum. Lisk is applying the use of sidechains to allow apps to be built on their blockchain, without the risk of a congested network. This allows, theoretically for infinite scalability and increased security.
To conclude the Lisk team comes off as one of the most professional projects in the current space and it is also backed by prominent advisors. They entered the market with a unique solution to a major problem and have proven their ability to make partnerships in the industry. Lisk is definitely worth taking the time to research and given a chance, it might outlast the current market and see significant gains in the next market.