Hyperplane A hyperplane depends on the space it is in, but it divides the space into two disconnected parts. For example, 1-dimensional space would just be a point, 2-d space a line, 3-d space a plane, and so on. How do we find the best hyperplane/line? You might be wondering that there could be multiple lines that split the data well. In fact, there is an infinite amount of lines that can divide two classes. As you can see in the gr...

Clustering Clustering is the process of grouping similar data and isolating dissimilar data. We want the data points in clusters we come up with to share some common properties that separate them from data points in other clusters. Ultimately, we’ll end up with a number of groups that meet these requirements. This probably sounds familiar because on the surface it sounds a lot like classification. But be aware that clustering and classification solve two very...

Introduction Principal component analysis is one technique used to take a large list of interconnected variables and choose the ones that best suit a model. This process of focusing in on only a few variables is called dimensionality reduction, and helps reduce complexity of our dataset. At its root, principal component analysis summarizes data. Ref: https://stats.stackexchange.com/questions/2691/making-sense-of-principal-component-analysis-eige...

TensorFlow - Introduction TensorFlow is Google’s Open Source Machine Learning Framework for dataflow programming across a range of tasks. Nodes in the graph represent mathematical operations, while the graph edges represent the multi-dimensional data arrays (tensors) communicated between them. Tensors are just multidimensional arrays, an extension of 2-dimensional tables to data with a higher dimension. There are many features of TensorF...

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 or list. Tensors are identified by the following three parameters − Rank Unit of dimensionality described within tensor is called rank. It identifies the number of dimensions of the tensor. A rank of a tensor can be descri...

TensorFlow for Deep Learning - Introduction 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 network − ...

Multilayer Preceptron - Introduction 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 − Fig. 1 MLP System Architecture MLP networks are usually used for supervised learning format. A typical learning algorithm for MLP networks i...

AutoEncoders An AE is a network with three or more layers, where the input layer and the output have the same number of neurons, and those intermediate (hidden layers) have a lower number of neurons. The network is trained to simply reproduce in the output, for each piece of input data, the same pattern of activity in the input. AEs are ANNs capable of learning efficient representations of the input data without any supervision (that is, the t...

Convolutional Neural Networks A convolutional neural network consists of several layers. Implicit explanation about each of these layers is given below. The Conv layer is the core building block of a Convolutional Neural Network.The primary purpose of Conv layer is to extract features from the input image. 2. Pooling Layer (Sub-sampling or Down-sampling) Fig.2 Max-Pooling and Average-Pooling The purpose of pooling is to re...

Introduction 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 recurrent neural network − Step 1 − Input a specific example fr...