What is the use of tensor calculus?

In mathematics, tensor calculus or tensor analysis is an extension of vector calculus to tensor fields (tensors that may vary over a manifold, e.g. in spacetime). Developed by Gregorio Ricci-Curbastro and his student Tullio Levi-Civita, it was used by Albert Einstein to develop his theory of general relativity.

Consequently, what is a tensor algebra?

In mathematics, the tensor algebra of a vector space V, denoted T(V) or T•(V), is the algebra of tensors on V (of any rank) with multiplication being the tensor product. Note: In this article, all algebras are assumed to be unital and associative. The unit is explicitly required to define the coproduct.

What is a tensor equation?

An equation is a mathematical statement of the fact that two expressions or quantities are equal . In certain cases (as for tensors of rank one , i.e vectors , and tensors of rank two), tensors can be represented by matrices , and tensor equations are represented by matrix equations .

Is a tensor A Matrix?

A matrix is a two dimensional array of numbers (or values from some field or ring). A 2-rank tensor is a linear map from two vector spaces, over some field such as the real numbers, to that field. If the vector spaces are finite dimensional then you can select a basis for each one and form a matrix of components.

What is Contravariant tensor?

Covariant tensors are a type of tensor with differing transformation properties, denoted . However, in three-dimensional Euclidean space, (6) for , 2, 3, meaning that contravariant and covariant tensors are equivalent. Such tensors are known as Cartesian tensor.

What is a covariant tensor?

Covariant Tensor. A covariant tensor, denoted with a lowered index (e.g., ) is a tensor having specific transformation properties. Therefore, raising and lowering indices is trivial, hence covariant and contravariant tensors have the same coordinates, and can be identified. Such tensors are known as Cartesian tensors.

What is tensor contraction?

In multilinear algebra, a tensor contraction is an operation on a tensor that arises from the natural pairing of a finite-dimensional vector space and its dual. The result is another tensor with order reduced by 2. Tensor contraction can be seen as a generalization of the trace.

Who developed vector calculus?

Vector calculus was developed from quaternion analysis by J. Willard Gibbs and Oliver Heaviside near the end of the 19th century, and most of the notation and terminology was established by Gibbs and Edwin Bidwell Wilson in their 1901 book, Vector Analysis.

What is keras Python?

Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation.

What is Tensorflow Python?

TensorFlow is a Python library for fast numerical computing created and released by Google. It is a foundation library that can be used to create Deep Learning models directly or by using wrapper libraries that simplify the process built on top of TensorFlow.

What is the Lstm?

Long short-term memory (LSTM) units (or blocks) are a building unit for layers of a recurrent neural network (RNN). A RNN composed of LSTM units is often called an LSTM network. A common LSTM unit is composed of a cell, an input gate, an output gate and a forget gate.

What is theano Python?

Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Theano features: tight integration with NumPy – Use numpy.ndarray in Theano-compiled functions.

What does RELU stand for?

rectified linear unit

What is RNNS?

A recurrent neural network (RNN) is a class of artificial neural network where connections between nodes form a directed graph along a sequence. Unlike feedforward neural networks, RNNs can use their internal state (memory) to process sequences of inputs.

What is CNN network?

In machine learning, a convolutional neural network (CNN, or ConvNet) is a class of deep, feed-forward artificial neural networks, most commonly applied to analyzing visual imagery.

What are the types of neural networks?

6 Types of Artificial Neural Networks Currently Being Used in Machine Learning

  • Feedforward Neural Network – Artificial Neuron:
  • Radial basis function Neural Network:
  • Kohonen Self Organizing Neural Network:
  • Recurrent Neural Network(RNN) – Long Short Term Memory:
  • Convolutional Neural Network:
  • Modular Neural Network:
  • What is an artificial neural network?

    Artificial neural networks (ANNs) or connectionist systems are computing systems vaguely inspired by the biological neural networks that constitute animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons which loosely model the neurons in a biological brain.

    What is meant by deep learning?

    Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, semi-supervised or unsupervised.

    What is the difference between supervised and unsupervised learning?

    Supervised learning is the Data mining task of inferring a function from labeled training data.The training data consist of a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called thesupervisory signal).

    What is deep learning in education?

    From Wikipedia, the free encyclopedia. In U.S. education, deeper learning is a set of student educational outcomes including acquisition of robust core academic content, higher-order thinking skills, and learning dispositions.

    What is unsupervised data?

    Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses. The most common unsupervised learning method is cluster analysis, which is used for exploratory data analysis to find hidden patterns or grouping in data.

    Is naive Bayes supervised or unsupervised?

    The supervised methods used are Naïve Bayes classifier, J48 Decision Trees and Support Vector Machines, whereas the unsupervised method is an adaptation of the K-means clustering method. The Naïve Bayes classifier is based on the Bayes rule of conditional probability.

    What are different types of supervised learning?

    The most widely used learning algorithms are:

  • Support Vector Machines.
  • linear regression.
  • logistic regression.
  • naive Bayes.
  • linear discriminant analysis.
  • decision trees.
  • k-nearest neighbor algorithm.
  • Neural Networks (Multilayer perceptron)
  • Why is naive Bayes so naive?

    It is a classification technique based on Bayes’ Theorem with an assumption of independence among predictors. In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature.