- Introduction to Tensorflow

- Deep Learning and Neural Networks

- Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN)

Machine learning software library.

TensorFlow, a powerful open-source software library for machine learning and artificial intelligence, uses data flow graphs where data moves through the graph in the form of tensors. In this article, we will delve into the core components of these graphs: constants, variables, and placeholders.

In TensorFlow, a constant is a type of tensor whose value can't be changed. Constants are used to store values that remain the same throughout the execution of a program. They are defined using the `tf.constant()`

function. For example:

`import tensorflow as tf a = tf.constant(1.0) b = tf.constant(2.0)`

In this example, `a`

and `b`

are constants with values 1.0 and 2.0 respectively.

Unlike constants, TensorFlow variables are tensors whose values can be changed. They are primarily used to hold and update parameters of a training model. Variables are defined using the `tf.Variable()`

function. For example:

`weight = tf.Variable(tf.random_normal([784, 200], stddev=0.35), name="weights") bias = tf.Variable(tf.zeros([200]), name="biases")`

In this example, `weight`

and `bias`

are variables that are initialized with random and zero values respectively.

Placeholders in TensorFlow are used to feed external data into a TensorFlow graph. They allow a graph to be parameterized to accept external inputs. A placeholder is defined using the `tf.placeholder()`

function. For example:

`x = tf.placeholder("float", shape=None) y = tf.placeholder("float", shape=[None, 784])`

In this example, `x`

and `y`

are placeholders that can be filled with real values at runtime.

The main difference between constants, variables, and placeholders lies in how they are initialized and used. Constants are initialized when you declare them, while variables need to be explicitly initialized by running an initialization operation. On the other hand, placeholders don't need to be initialized but you need to feed them with data at runtime.

In terms of usage, constants are used for values that don't change, variables are used for values that need to be updated, and placeholders are used for values that are to be supplied when you run the computation graph.

In conclusion, understanding constants, variables, and placeholders is fundamental to working with TensorFlow. They form the building blocks of any TensorFlow program and are crucial in defining the computation graph and the data flow.