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DefinitionIn computer science and mathematics, a directed acyclic graph (DAG) refers to a directed graph which has no directed cycles.ExplanationIn graph theory, a graph refers to a set of vertices which are connected by lines called edges. In a directed graph or a digraph, each edge is associated with a direction from a start vertex to an end vertex. If we traverse along the direction of the edges and we find that no closed loops are formed along any path, we say that there are no directed cycles. The graph formed is a directed acyclic graph.A DAG is always topologically ... Read More
DefinitionThe Dijkstra’s algorithm finds the shortest path from a particular node, called the source node to every other node in a connected graph. It produces a shortest path tree with the source node as the root. It is profoundly used in computer networks to generate optimal routes with the aim of minimizing routing costs.Dijkstra’s AlgorithmInput − A graph representing the network; and a source node, sOutput − A shortest path tree, spt[], with s as the root node.Initializations −An array of distances dist[] of size |V| (number of nodes), where dist[s] = 0 and dist[u] = ∞ (infinite), where u ... Read More
Flooding and fixed routing are methods to transmit data packets from the source to the destination through a number of intermediate routers connected by transmission lines.Flooding is a non-adaptive routing technique following this simple method − when a data packet arrives at a router, it is sent to all the outgoing links except the one it has arrived on.Fixed routing algorithm is a procedure that lays down a fixed route or path to transfer data packets from source to the destination. The route is a mathematically computed best path, i.e. “least–cost path” that the packet can be routed through. The ... Read More
Tensorflow can be used with estimators for feature engineering by first defining the columns and iterating through the categorical columns. The unique names of features are obtained, and is appended to an empty list.Read More: What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?We will use the Keras Sequential API, which is helpful in building a sequential model that is used to work with a plain stack of layers, where every layer has exactly one input tensor and one output tensor.A neural network that contains at least one layer is known as a convolutional layer. We ... Read More
The titanic dataset can be visualized using the ‘hist’ method which visualizes a histogram. A horizontal bar graph can be generated by specifying the type of graph as ‘barh’. Read More: What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?We will use the Keras Sequential API, which is helpful in building a sequential model that is used to work with a plain stack of layers, where every layer has exactly one input tensor and one output tensor.TensorFlow Text contains collection of text related classes and ops that can be used with TensorFlow 2.0. The TensorFlow Text can ... Read More
The titanic dataset can be explored using the estimator with Tensorflow by using the ‘head’ method, the ‘describe’ method, and the ‘shape’ method. The head method gives the first few rows of the dataset, and the describe method gives information about the dataset, such as column names, types, mean, variance, standard deviation and so on. The shape method gives the dimensions of the data.Read More: What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?We will use the Keras Sequential API, which is helpful in building a sequential model that is used to work with a plain ... Read More
A linear model can be built with estimators to load the titanic dataset using the ‘read_csv’ method which is present in ‘Pandas’ package. This method takes google APIs that store the titanic dataset. The API is read and the data is stored in the form of a CSV file.Read More: What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?We will use the Keras Sequential API, which is helpful in building a sequential model that is used to work with a plain stack of layers, where every layer has exactly one input tensor and one output tensor.A ... Read More
The ‘predict’ method is called on never before seen data and the predictions and the actual value is displayed on console.Read More: What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?We will use the Keras Sequential API, which is helpful in building a sequential model that is used to work with a plain stack of layers, where every layer has exactly one input tensor and one output tensor.A neural network that contains at least one layer is known as a convolutional layer. We can use the Convolutional Neural Network to build learning model. TensorFlow Text contains collection ... Read More
Tensorflow can be used with the estimator to predict output on new data using the ‘predict’ method which is present in the ‘classifier’ method.Read More: What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?We will use the Keras Sequential API, which is helpful in building a sequential model that is used to work with a plain stack of layers, where every layer has exactly one input tensor and one output tensor.A neural network that contains at least one layer is known as a convolutional layer. We can use the Convolutional Neural Network to build learning model. TensorFlow ... Read More
Suppose following is the problem:We have a sequence of numbers starting from 1 and upto any arbitrary number, let's call it num. We have to pick two such numbers from the sequence (let's call them m and n), such that:sum(1 to num) - (m + n) = m * nAnd finally, we should return an array of groups of all such numbers.For example −If the input is −const num = 10;Then the output should be −const output = [ [7, 6] ];because sum(1 to 10) = 55and, 55 - (6 + 7) = 6 * 7 = 42ExampleThe code ... Read More