Category: Tutorials

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…

TensorFlow includes a visualization tool, which is called the TensorBoard. It is used for analyzing Data Flow Graph and also used to understand machine-learning models. The important feature of TensorBoard includes a view of different types of statistics about the parameters and details of any graph in vertical alignment. Deep neural network includes up to…

Recurrent neural networks is a type of deep learning-oriented algorithm, which follows a sequential approach. In neural networks, we always assume that each input and output is independent of all other layers. These type of neural networks are called recurrent because they perform mathematical computations in sequential manner. Consider the following steps to train a…

After understanding machine-learning concepts, we can now shift our focus to deep learning concepts. Deep learning is a division of machine learning and is considered as a crucial step taken by researchers in recent decades. The examples of deep learning implementation include applications like image recognition and speech recognition. Following are the two important types…

In this chapter, we will learn about the basics of TensorFlow. We will begin by understanding the data structure of tensor. Tensor Data Structure Tensors are used as the basic data structures in TensorFlow language. Tensors represent the connecting edges in any flow diagram called the Data Flow Graph. Tensors are defined as multidimensional array…