Author: tayyaba

In this chapter, we will be focus on the network we will have to learn from known set of points called x and f(x). A single hidden layer will build this simple network. The code for the explanation of hidden layers of perceptron is as shown below − Output Following is the representation of function…

Multi-Layer perceptron defines the most complicated architecture of artificial neural networks. It is substantially formed from multiple layers of perceptron. The diagrammatic representation of multi-layer perceptron learning is as shown below − MLP networks are usually used for supervised learning format. A typical learning algorithm for MLP networks is also called back propagation’s algorithm. Now,…

Here, we will focus on MetaGraph formation in TensorFlow. This will help us understand export module in TensorFlow. The MetaGraph contains the basic information, which is required to train, perform evaluation, or run inference on a previously trained graph. Following is the code snippet for the same − One of the typical usage model for…

This chapter will focus on how to get started with distributed TensorFlow. The aim is to help developers understand the basic distributed TF concepts that are reoccurring, such as TF servers. We will use the Jupyter Notebook for evaluating distributed TensorFlow. The implementation of distributed computing with TensorFlow is mentioned below − Step 1 − Import…

Keras is compact, easy to learn, high-level Python library run on top of TensorFlow framework. It is made with focus of understanding deep learning techniques, such as creating layers for neural networks maintaining the concepts of shapes and mathematical details. The creation of freamework can be of the following two types − Consider the following…

In this chapter, we will focus on the difference between CNN and RNN − CNN RNN It is suitable for spatial data such as images. RNN is suitable for temporal data, also called sequential data. CNN is considered to be more powerful than RNN. RNN includes less feature compatibility when compared to CNN. This network…

TFLearn can be defined as a modular and transparent deep learning aspect used in TensorFlow framework. The main motive of TFLearn is to provide a higher level API to TensorFlow for facilitating and showing up new experiments. Consider the following important features of TFLearn − Install TFLearn by executing the following command − pip install…

In this chapter, we will focus on the basic example of linear regression implementation using TensorFlow. Logistic regression or linear regression is a supervised machine learning approach for the classification of order discrete categories. Our goal in this chapter is to build a model by which a user can predict the relationship between predictor variables…

For understanding single layer perceptron, it is important to understand Artificial Neural Networks (ANN). Artificial neural networks is the information processing system the mechanism of which is inspired with the functionality of biological neural circuits. An artificial neural network possesses many processing units connected to each other. Following is the schematic representation of artificial neural…

Word embedding is the concept of mapping from discrete objects such as words to vectors and real numbers. It is important for input for machine learning. The concept includes standard functions, which effectively transform discrete input objects to useful vectors. The sample illustration of input of word embedding is as shown below − Word2vec Word2vec…