QuAIT

Quantum Machine Learning Quantum computing is a vicinity of reckoning, focused on developing computer technology based on the principles of quantum physics.  Quantum physics is one among the foremost winning series of contemporary science describing the way our world works at the foremost elementary level.Quantum computing has become one of the most leading applications of quantum physics. It can solve some of the world’s impenetrable problems that are beyond the reach of even today’s most efficient supercomputers. We can assume that quantum computers are not going to replace classical computers but they are radically a different approach for operations that enables them to perform calculations that classical computers cannot. Let’s see how they differ: Classical computers encode the information in bits and each bit can represent 0 or 1 that ultimately translate into computer functions to perform simple calculations. Unlike classical computers, quantum computers encode information ...

Deep Learning may be a subfield of machine learning concerned with algorithms inspired by the structure and performance of the brain called artificial neural networks. If you are just starting a call at the sector of deep learning, otherwise you had some experience with neural networks a while ago, you'll be confused. Many across the globe learned and used neural networks within the 1990s and early 2000s. The leaders and experts within the field have ideas of what deep learning is and these specific and nuanced perspectives shed tons of sunshine on what deep learning is all about. Let’s dive in. Deep Learning is Large Neural Networks Andrew Ng from Coursera and Chief Scientist at Baidu Research formally founded Google Brain that eventually resulted in the productization of deep learning technologies across an outsized number of Google services. He has spoken and written tons about what deep learning may be and is a good place to start. In early talks on deep learning, Andrew descri...

Supervised Machine Learning Supervised learning is a type of machine learning, where the machine is trained with the labelled dataset; so that if any new data is given as input it can easily predict the output. You can train the data in such a way that if X is given as input, it should give Y as output. Here, the algorithm generalises the inputs with the help of trained data and predicts the accurate results. Let us understand it with an example, do  you want to know how long will it take for you to drive home from the workplace, when you know that it is heavily raining outside? First, you will train the machine with the data that includes ·       Traffic ·       Weather condition ·       Time ·       Route you choose Data will be your input. Now, you want to know the output, that is the amount of time it takes to drive home on that specific day. After you give the input, the data ...

  Deep Learning Deep Learning is considered as a subset and extendable technique to Machine learning.  It is also considered to be more Dominant and tensile compared to machine learning. It deals with a lot of things such as it performs the filtration of inputs through layers of computer and helps in predicting and classifying the information. The origin of Deep learning is inspired by the functioning of the human brains and information filter.  One of the cool features in deep learning is it attempts to mimic the activity layers of neurons Performed in the neocortex.  It is also simply known as the Artificial neural network. The performance scale of deep learning is High.  These algorithms mostly depend on high-end machines. Deep learning algorithms consist of a GPU because it plays a key role in its operation. It only undergoes a large number of datasets whereas the rest of the datasets are ignored. These functions are enhanced by utilizing GPU. The Graphics P...