Tensor Flow Tutorial

Understanding Tensors

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Tensors are a fundamental concept in TensorFlow. Tensors are a multidimensional array with a uniform type, and they can be used to represent all type of data. In this tutorial, we’ll learn the basics on tensors.

What are Tensors?

A tensor is a container for data that can be represented as an n-dimensional array.
tensors are a generalization of vectors and matrices, that can be used to represent any multidimensional data including images, audio and text.
tensors can have any number of dimensions and can be of different shapes and sizes.

Types of tensors -

Scalar tensor: A scalar tensor is a tensor with a single value. we can say it is a tensor with no dimensions.
Vector: A vector tensor is a tensor with a single dimension.
Matrix: A matrix tensor is a tensor with two dimensions.
Higher Order: it is a tensor with more than two dimensions.

Common Tensor Operations -

tf.add(): it is used to add two tensors.
tf.multiply(): it is used to multiply two tensors.
tf.reshape(): it is used to reshape a tensor
tf.concat(): it is used to concatenate tensors.

there are many more operations to perform on tensors.

Representation of Tensor -

import tensorflow as tf
tensor = tf.constant(42)

Here, we created a scalar tensor with the value of 42.

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Tensor Flow Tutorial

Tensor Flow Tutorial

Introduction to Machine Learning

Introduction to TensorFlow

Introduction to Neural Networks

Building and Training Machine Learning Models

Advanced TensorFlow Techniques

Additional Resources and Projects

Understanding Tensors

What are Tensors?

Types of tensors -

Common Tensor Operations -

Representation of Tensor -

Important Links