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Machine Learning is a field of study of smart and efficient use of data and existing algorithms to make machines imitate humans in performing tasks with greater accuracy. It is one of the fastest growing branches in Computer Science , along with Artificial Intelligence. Machine Learning can be rightly called as a subset of artificial intelligence, as a lot of ML algorithms and results are of AI . Automation of Routine Tasks We are living in a fast moving world where people have no time to carry out their regular chores manually. Nearly half of the population ... Read More
Python is a powerful language to be used for performing various statistical tests. One such statistical test is the Welch’s t-test. When there are two datasets with equal variances and you have to find out whether their means are the same or not, then using a two sample t-test would be wise enough. However, if the variances of the two datasets are not the same, then Welch’s t-test should be used to compare the means. Syntax stats.ttest_ind(dataset_one, dataset_two, equal_var = False/True) Here, ttest_ind is the function that performs Welch’s t-test. It takes three parameters, The first ... Read More
In this article, we will learn to configure the Run button for functions in MATLAB. In MATLAB, the `Run` button is used execute a MATLAB code. Also, MATLAB allows us to configure the `Run` button to execute a specific user defined function. Algorithm to Configure Run Button The step−by−step process to configure the Run button to execute a specific function in MATLAB is explained below: Step 1 − Firstly, create a new script to open the MATLAB editor. Step 2 − Define or create a function for which you want to configure the Run button. For example, we create ... Read More
ANN or artificial neural networks are the computing systems developed by taking inspiration from the biological neural networks; the human brain being its major unit. These neural networks are made functional with the help of training that follows some kind of a learning rule. A learning rule in ANN is nothing but a set of instructions or a mathematical formula that helps in reinforcing a pattern, thus improving the efficiency of a neural network. There are 6 such learning rules that are widely used by neural networks for training. Hebbian Learning Rule Developed by Donald Hebb in 1949, ... Read More
Intelligence can be defined as the ability to learn, understand and apply knowledge and skills. Human Intelligence is a multifaceted concept that encompasses many types. Before understanding the types of human intelligence let’s have a look at the basic difference between Human intelligence and Artificial intelligence − Human intelligence is associated with creativity, emotion, flexibility, and the ability to learn from a wide range of experiences. Unlike human intelligence, artificial intelligence cannot think creatively, experience emotions, adapt to new circumstances, or understand ethical and moral issues. While machine learning and human learning share some similarities, reinforcement learning uses ... Read More
Introduction Data analysis plays a crucial role in today's data-driven world. It involves extracting valuable insights from large and complex datasets to make informed decisions. R is a powerful programming language and software environment widely used for statistical computing and graphics. In this article, we will explore the fundamentals of data analysis with R, its benefits, and various techniques used in the process. What is R? R is an open-source programming language and software environment specifically designed for statistical computing and graphics. It provides a wide range of tools for data manipulation, visualization, and statistical analysis. R is highly extensible ... Read More
A transfer neural network is a deep learning architecture that handles long-range dependencies well, as was first described in Vaswani et al's 2017 paper "All you need is attention.". The self-attention mechanism of transformer networks allows them to identify relevant parts of input sequences. What are Recurrent Neural Networks? Recurrent neural networks are artificial neural networks that have memory or feedback loops. They are designed to process and classify sequential data in which the order of the data points is important. The network works by feeding the input data into a hidden layer, allowing the network to maintain information ... Read More
Encoding and decoding Base64 strings is a common task in Python programming. Base64 encoding is used to convert binary data into a format that can be transmitted over the internet, while decoding is used to convert the Base64-encoded data back to its original format. In this article, we will explore how to encode and decode Base64 strings in Python, including the different methods and libraries available. Understanding Base64 Encoding Base64 encoding is a binary-to-text encoding scheme that represents binary data in an ASCII string format. It is often used to transmit data over the internet or store data in a ... Read More
TFlearn is an open-source deep-learning library built on the TensorFlow framework. It provides a high-level API with which it is easy to create and train different neural network models. It provides an array of pre-existing models such as Convolutional Neural Network (CNN), Deep Neural Networks (DNN), and many other models. It also includes a variety of activation functions such as ReLU (Rectified Linear Units), softmax, and also loss functions such as categorical cross-entropy and so on. TfLearn is an ideal library for beginners due to not requiring extensive knowledge of Neural Network APIs in TensorFlow. It is a simple, easy-to-use ... Read More
PyTorch, an open-source machine learning framework, is widely used in academic research and industries. It offers ample tools and libraries to build and train neural networks effectively. Tensors in PyTorch In PyTorch, a tensor is a multidimensional array of values that can be used to represent data for machine learning models. Dimensions may vary as 1D, 2D, 3D, and so on, totally depending on the complexity of the data they represent. Let's take an example, a 1D tensor can be used to represent a sequence of values, such as time-series data, whereas a 2D tensor can be used to represent ... Read More