In the figure above, two numbers are supposed to be added. Those numbers are stored in two variables, a and b. The two values are flowing through the graph and arrive at the square node, where they are being added. The result of the addition is stored into another variable, c. Actually, a, b and c can be considered as placeholders. Any numbers that are fed into a and b get added and are stored into c. This is exactly how TensorFlow works. The user defines an abstract representation of the model (neural network) through placeholders and variables. Afterwards, the placeholders get "filled" with real data and the actual computations take place.

In this article, we are going to develop a machine learning technique called Deep learning (Artificial Neural network) by using tensor flow and predicting stock price in python. At the end of this article you will learn how to build artificial neural network by using tensor flow and how to code a strategy using the predictions from the neural network. If you are new to Neural Networks and would like to gain an understanding of their working, I would recommend you to go through the following blogs before building a neural network. TensorFlow is an open-source software library for dataflow programming across a range of tasks. It is a symbolic math library, and is used for machine learning applications such as deep learning neural networks.

In this article, two basic feed-forward neural networks (FFNNs) will be created using TensorFlow deep learning library in Python. The reader should have basic understanding of how neural networks work and its concepts in order to apply them programmatically. This article will take you through all steps required to build a simple feed-forward neural network in TensorFlow by explaining each step in details. Before actual building of the neural network, some preliminary steps are recommended to be discussed. Here is the first classification problem that we are to solve using neural network.