"Many researchers … speculate that the information-processing abilities of biological neural systems must follow from highly parallel processes operating on representations that are distributed over many neurons. [Artificial neural networks] capture this kind of highly parallel computation based on distributed representations"
– from Machine Learning (Section 4.1.1; page 82) by Tom M. Mitchell, McGraw Hill Companies, Inc. (1997).
IonQ, Inc, the leader in quantum computing, announced the release of a new paper in collaboration with Fidelity Center for Applied Technology (FCAT) that demonstrates how its quantum computers can outperform classical computers to generate high-quality data for use in testing financial models. Financial institutions commonly use models for asset allocation, electronic trading, and pricing, and require testing data to validate the accuracy of these models. The new technique, demonstrated by FCAT on IonQ's latest quantum computers, has the potential to be the first class of quantum machine learning models to be deployed for broad commercial use. "At FCAT, we track new and emerging technologies and trends to help Fidelity meet the changing needs of our customers and ass These classical approaches are often limited because real-world dependencies between variables–for example, in a portfolio of stocks–are too complex for them to model. IonQ and FCAT demonstrated that data generated with quantum machine learning algorithms is more representative of these real-world dependencies and is therefore better at accounting for edge cases like black swan events. The technique invented by IonQ and FCAT leverages copulas, a method often used in statistical models to describe relationships between large numbers of variables. For instance, large financial institutions use copulas to understand relationships between stock prices (if the price of X is within a particular range, then the price of Y tends to go up). By using quantum computers to implement copulas, IonQ and FCAT demonstrated the ability to construct complex models beyond the capability of classical computers. "This research, performed on IonQ hardware, shows quite clearly that leveraging quantum computing can lead to superior financial modeling results.
Techniques, tools, best practices and everything you need to to learn machine learning! This is a comprehensive repository containing 30 notebooks on Python programming, data manipulation, data analysis, data visualization, data cleaning, classical machine learning, Computer Vision and Natural Language Processing(NLP). All notebooks were created with the readers in mind. Every notebook starts with a high-level overview of any specific algorithm/concepts being covered. Wherever possible, visuals are used to make things clear.
Machine Learning and Deep Learning are both becoming well-known phrases in the current era -- but details of the specific tools they require are less ubiquitous. I'd like to discuss one of the most popular Machine Learning tools and how it compares to the others. TensorFlow is probably the most popular Machine Learning tool among researchers today. The Data Incubator calculated that the rating for TensorFlow is nine standard deviations higher than the rating for the second highest machine learning tool, Keras. TensorFlow was written by the Google Brain Team in 2015, and its front end is written in python, while its backend is written in C .
Geometric deep learning is a "program" that aspires to situate deep learning architectures and techniques in a framework of mathematical priors. The priors, such as various types of invariance, first arise in some physical domain. A neural network that well matches the domain will preserve as many invariances as possible. In this post, we present a very conceptual, high-level overview, and highlight a few applications.
In this course,we will learn to create our own neural networks with python. Details are: Introduction to artificial neural network. Artificial neural networks (ANNs), also known as neural networks (NNs), are computer systems that are modelled after the biological neural networks that make up animal brains. In this course,we will learn to create our own neural networks with python. Introduction to artificial neural network: Artificial neural networks simulates the functioning of human brain .This section,we will learn the basics of artificial neural network.We will also learn various types of neural network.,techniques of neural networks.
A neural network is a series of algorithms that helps us to recognise relationships in a dataset through a process by mimicking human brains. It can adapt to changing input and generate the best results. The basic building of neural network is neuron. A neuron in a neural network is a mathematical function, which collects and classifies information according to a defined architecture. A neural network consists of 3 major components.
Electrification was, without a doubt, the greatest engineering marvel of the 20th century. The electric motor was invented way back in 1821, and the electrical circuit was mathematically analyzed in 1827. But factory electrification, household electrification, and railway electrification all started slowly several decades later. The field of AI was formally founded in 1956. But it's only now--more than six decades later--that AI is expected to revolutionize the way humanity will live and work in the coming decades.
Instead of computing the success or failure of AI initiatives on a project-by-project basis, companies using the portfolio approach compute the ROI for all their AI initiatives. A portfolio approach works in other areas of business, and the same principles apply here. Take a look at three relevant examples and the lessons for AI. In the pharmaceutical world, developing a new drug takes an average of at least ten years and costs over $2.6 billion. Literally thousands and even millions of molecules and investigative drugs are studied during the initial drug discovery and preclinical trial phases of the R&D process.
The aim of this project is to identify various diseases on tomatoes based on their leaves. It is very important in agriculture to identify diseases immediately. To detect the problem in real-time, we develop a Deep Learning model that can be installed on embedded devices and can be used in greenhouses, or by adding the model to some apps people can manually upload a photo of tomato leaves and check how healthy it is. Our model is able to find healthy tomatoes and 9 different diseases. The public dataset, which is available on Kaggle has been used to train and test the model.
Word embedding is the approach of learning word and their relative meanings from a corpus of text and representing the word as a dense vector. The word vector is the projection of the word into a continuous feature vector space, see Figure 1 (A) for clarity. Words that have similar meaning should be close together in the vector space as illustrated in see Figure 1 (B). Word2vec is one of the most popular words embedding in NLP. Word2vec has two types, Continuous Bag-of-Words Model (CBOW) and Continuous Skip-gram Model , the model architectures are shown in Figure 2. CBOW predicts the word according to the given context, where Skip-gram predicts the context according to the given word, which increases the computational complexity .