Periyar, to many in Tamil Nadu, was an atheist and iconoclast who called out belief in gods, superstitions, and rituals. He, of course, was all of that. But despite his rejection, he had a close engagement with religion and his critique was rooted in a close reading of religious texts, practices, and the values they espoused. He also creatively drew upon extant critiques of religion, Vedic and Abrahamic, and scholarly debates of his times to propound an alternative humanist ethic rooted in justice and fraternity. This chapter maps the multiple sources of his critique of religion and outlines the contours of his call for an ethical life.

There was much overlap between Periyar's thoughts and the critiques of scripturally sanctioned hierarchies of caste by spiritual and secular thinkers, both those who preceded him and those who lived in his times. Even though he was influenced by modernist critiques of religion emerging from the West, it is important to note that his views were in line with a long lineage of materialist philosophical traditions in the subcontinent.

Periyar became a militant atheist only in his forties. It was his vehement criticism of Brahminical Hinduism that led to his position of atheism. Periyar, on several occasions, observed that he was least interested in talking about God and religion.

Machine learning (ML) algorithms work on cleaned data. Usually, the data we collect for building ML models suffers from noise, missing values, inconsistent data types, and different data scales. This makes pre-processing of data a very important phase in preparing the data for building ML models. Pre-processing is when we apply transformations over the data before feeding it to the ML algorithm. In short, data pre-processing symbolizes a set of procedures applied to the data to make it fit for ML algorithms. It generally involves the following steps:

• Step 1—Importing libraries: It involves importing the necessary libraries that are required to carry out the subsequent data manipulation and cleaning tasks.

• Step 2—Loading the dataset: The dataset that needs to be pre-processed must be loaded.

• Step 3—Handling the missing values: Dataset often contains missing or null values; these values need to be handled appropriately.

• Step 4—Handling the categorical data: In the data pre-processing phase, it is crucial to address categorical attributes that often contain multiple categories. Handling categorical data becomes an important step to ensure proper treatment and transformation of these attributes.

• Step 5—Splitting the dataset into training and testing datasets: Training and testing is the most important part of ML; thus, we need to split the dataset into training and testing subsets before building the ML models.

• Step 6—Feature scaling: In datasets, the range of data often varies, or data is often of different scales. Thus, feature scaling needs to be done to ensure uniformity in results.

It is important to note that it is not necessary to apply all of these steps to pre-process the data. However, based on the nature of the dataset, some of these steps may be skipped for building the model. In the coming sections, we will discuss the importance or need of these steps and discuss how to perform these steps in Python.

6A Radiation as a photon gas

In the nineteenth century, physicists applied classical electromagnetic theory to explain the experimental results of black body radiation but were unable to provide an adequate explanation. This was a major problem to the physicists as classical theory predicted an infinite amount of energy of the radiation emitted from a black body. This gross disagreement was called the “ultraviolet catastrophe”. Max Planck presented a paper on December 14, 1900, in which he guessed the answer to this problem of black body radiation. This guess marked the very beginning of quantum mechanics.

The electromagnetic radiation inside a black body chamber exists as patterns of standing waves or modes. A single mode is like a standing wave on a guitar string and is characterized by a frequency. Planck made a bold hypothesis that the energy of such a mode is quantized, ithat is, only certain energies of these oscillation modes are allowed. Thus, the energy 𝐸𝑛 of 𝑛th mode is given by 𝐸𝑛 = (𝑛 + 12 ) ℏ𝜔 = (𝑛 + 12 ) ℎ𝜈, where 𝜈 is the oscillation frequency, ℎ is Planck's constant, and 𝑛 is an integer starting with 0. Thus, each oscillation mode can exist in any one of an infinite number of energy states whose energies are equally separated by the energy ℏ𝜔. In the discussion of the properties of the photon gas, for the time being, we shall ignore 12 in the expression for 𝐸𝑛 as it has no effect on the results we seek. Therefore, we take 𝐸𝑛 = 𝑛ℏ𝜔 as the energy of the 𝑛th mode whose (angular) frequency is 𝜔. When the energy of a mode is 𝐸𝑛, we say that there are 𝑛 photons in the mode. Each photon has energy equal to ℏ𝜔. Thus, according to the prescription of Max Planck, a black body radiation chamber consists of a number of photons in various energy states with different amounts of energy.

20.1 Neural Networks and Human Brain

The inspiration to build artificial neural networks (ANNs) has come from the deep desire to simulate the working of the human brain. As our understanding of the human brain is getting enriched and improved daily due to the ongoing research on this topic, researchers are also improving the ANNs accordingly. One such recent addition is the recurrent neural networks (RNNs) and the long short-term memory (LSTM). In this chapter, we will discuss these developments in a simplified manner so that our readers can understand this amazing technology and use it for their development projects.

Sections 20.1 and 20.2 draw their inspiration from the blog titled “The Ultimate Guide to Recurrent Neural Networks (RNN)” published by SuperDataScience Team. Most of the figures used in these sections are adapted from this blog.

For detailed information on the blog, please refer to the “Additional Resources” section of this chapter.

Research on the human brain gives us the idea that the human brain has three main parts: cerebrum, cerebellum, and brainstem, as shown in Figure 20.1. Let us briefly discuss the main functions of these parts and their components to understand the association between neural networks and the human brain.

Cerebrum

From Figure 20.1, we can understand that the cerebrum has four lobes. These are as follows.

Introduction

In 1935, at a conference of Senguntha Mudaliars in Tiruppur, C. N. Annadurai (Anna, 1909–1969), then a twenty-six-year-old graduate, met E. V. Ramasamy (Periyar, 1879–1973). Impressed by the non-Brahmin youth who wanted to enter public life rather than seek a government job, Periyar was quick to take him under his wing. In less than three years, Anna was playing a major role in the Self-Respect Movement (SRM), becoming one of Periyar's chief lieutenants in the 1938–1939 anti-Hindi agitation which made the Dravidian movement a mass organization and effectively put Tamil assertion at the centre stage of politics. It was in the course of this mass-based agitation that the Justice Party was absorbed by the SRM and, in 1944, rechristened the Dravidar Kazhagam (DK). In 1949, Periyar's most brilliant protégé became his rival, breaking away to form the Dravida Munnetra Kazhagam (DMK). In 1967, the DMK dethroned the Indian National Congress (Congress). The intervening decades were marked by bitter hostility and rivalry between the DK and the DMK.

Immediately after the DMK's 1967 victory, there was a rapprochement. Since then, it has been customary to collapse the two into a unified Dravidian movement. The rivalry between the DK and the DMK has been completely elided by party ideologues, chroniclers, and historians.

rystallography

The science that deals with the geometrical structure and physical properties of crystalline solids is called crystallography.

Solids are classified into two categories:

• 1. Crystalline solids

• 2. Amorphous solids

CRYSTALLINE SOLIDS

Crystalline solids are those that contain the regular repeated pattern of atoms or molecules, as shown in Figure 10.1. The physical properties of crystalline solids are different in different directions. Therefore, crystalline solids are anisotropic. Examples are rock salt, quartz, calcite, sugar, and so on.

AMORPHOUS SOLIDS

Figure 10.2 illustrates an amorphous material, which lacks the regular arrangement of atoms or molecules. The amorphous solid's physical characteristics are uniform throughout. As a result, amorphous solids are isotropic. Examples are glass, rubber, polymers, and so on.

SPACE LATTICES

A crystal is made up of identical structural units (atoms, molecules, or ions) that are infinitely repeated in space; each unit can be replaced by a geometrical point. The outcome is a pattern of dots with crystal-like geometrical characteristics. The crystal lattice or space lattice is this geometric arrangement. Lattice points are the name given to the geometrical points.

The regular pattern of points that describes the three-dimensional arrangement of points (atoms, molecules, or ions) in the crystal structure is called the crystal lattice or space lattice.

BASIS

The unit assembly of atoms, molecules, or ions identical in the composition, arrangement, and orientation is called basis. If we add the basis to every lattice point, then it forms a crystal structure, as shown in Figure 10.3.

Space lattice + Basis = Crystal structure.

Engineering physics plays a crucial role in providing the foundational knowledge necessary for the development of innovative technologies. It is an essential part of the curriculum for students in various streams of science and engineering at the undergraduate level.

The goal of this book is to develop a solid understanding of the basic principles of physics and highlight their relevance to engineering. The content is structured to progressively build the knowledge and skills necessary for further studies in both theoretical and applied sciences. Each chapter begins with the basic concepts and gradually moves to more advanced topics, supported by numerical examples, illustrations, and problem sets that reinforce learning. The problems included are designed to improve the problem-solving skills of students and provide practical insight into the engineering applications of physics.

The manuscript includes 14 chapters that were prepared in accordance with the syllabus taught in various Indian colleges and universities. In addition to core topics, the manuscript also covers advanced topics such as relativistic mechanics, quantum mechanics, optical fiber, lasers, semiconducting materials, superconducting materials, and nanomaterials. Students who want to pursue higher education and a career in research, as well as instructors who instruct postgraduate courses at universities, will find these topics helpful for building a solid foundational understanding and developing problem-solving abilities.

Welcome to the World of Machine Learning!

In this ever-evolving era of technology, the field of machine learning (ML) stands at the forefront of innovation, promising unprecedented insights and solutions to complex problems. This textbook provides a comprehensive understanding of the theoretical foundations of ML algorithms, coupled with practical implementation using Python, designed to be a companion for both beginners venturing into the field and seasoned practitioners seeking to deepen their understanding.

Why Machine Learning?

ML is not just a technical endeavor; it is a transformational force shaping the future of how we interact with data. From enhancing search engines and revolutionizing social media to powering self-driving cars and advancing artificial intelligence (AI), the applications of ML are boundless. ML has paved the way for developing sophisticated chatbots, generative AI models, and AI copilots. This book aims to demystify the intricate concepts of ML, making them accessible to learners of all backgrounds.

What This Book Offers

• - Foundations: We begin with the fundamentals, laying a solid groundwork to help you grasp the core concepts of ML.

• - “Learning by Doing” Approach: This book adopts a “learning by doing” approach, offering step-by-step coding instructions for various ML techniques. The aim is to empower you with the knowledge and skills to implement these principles effectively.

• - Real-World Applications: Understanding theory is essential, but applying it to real-world scenarios is where the true power of ML unfolds. This book bridges theory and application, ensuring a holistic learning experience.

For Whom This Book Is Intended

Whether you are a student exploring the realms of ML for the first time, a data professional aiming to expand your skill set, or a business leader seeking insights into how ML can elevate your organization, this book is crafted with you in mind.

How to Use This Book

Feel free to navigate the chapters based on your familiarity with the subject. If you’re a beginner, start from the beginning, and if you’re seeking advanced knowledge, dive into specific sections of interest. Explore GitHub resources that provide access to datasets, sample code, and examples used in this book for hands-on learning. For enhanced visual understanding, this book is complemented by an online video course available at learncompscience.com, allowing you to reinforce your knowledge through engaging video sessions.

Periyar's writings on women were at the heart of his commitment to a radical concept of freedom. Periyar is known most not only for his atheism and radical critique of religion (Manoharan, 2022a) but also for his commitment and contribution to anti-caste thought and politics (Manoharan, 2020; 2022b). However, crucial, perhaps even central, to Periyar's politics of Self-Respect was his approach to the women's question. In this chapter, we discuss how Periyar's approach to the women's question was grounded not only in a rights-based discourse, but also in a freedom-based discourse; not just freedom from patriarchy, but also sexual freedom in a radically libertarian sense. More importantly, Periyar argued that freedom for women took priority over freedom from colonialism, and challenged patriarchal tendencies within Indian nationalism.

Scholars engaged with feminist politics have looked at the critical importance given to the women's question and gender in the Self-Respect Movement (SRM). In their readings on gender politics in India, Anandhi and Velayuthan (2010) highlight the ‘limitations in theory itself in dealing with diversities and subalternity’ and argue that in a scenario where gender intersects with caste and class, the theory and methods used ‘should generate knowledge from the margins’. While feminist scholars such as Uma Chakravarti (2018) and Sharmila Rege (2013) have discussed the intersections of caste and patriarchy, others who have studied the Periyarist politics of gender—Anandhi (1991), Geetha (1998), and Hodges (2005)—have meticulously captured what we very broadly call Self-Respect perspectives and made important contributions to the study of women’s politics of and from the margins of Tamil Nadu.

DIFFRACTION

Diffraction is a phenomenon in which a light beam bends around the corner of an obstacle and spreads into the geometric shadow of that obstacle.

FRESNEL AND FRAUNHOFER DIFFRACTION

Diffraction can be classified into two categories:

• 1. Fresnel diffraction

• 2. Fraunhofer diffraction

The distinction between these two categories is as follows:

• a. In Fresnel diffraction, the screen and source are at a finite distance from an obstacle. The distances are important in this class. In Fraunhofer diffraction, the source and screen are at an infinite distance from an obstacle. Therefore, inclination is important.

• b. The incident wavefront in Fresnel diffraction is either spherical or cylindrical, whereas the incident wavefront in Fraunhofer diffraction is planar.

• c. In Fresnel diffraction, the central point of the screen is either bright or dark depending on the number of zones, whereas in Fraunhofer diffraction, the central point of the screen is always bright.

FRAUNHOFER DIFFRACTION DUE TO SINGLE SLIT

Let us consider a monochromatic light source of wavelength ƛ placed at the focus of convex lens L1. The collimated rays of plane wavefront are incident on a single-slit AB of width “e.” The un-deviated rays from the slit reaches at point O, and the rays diffracted by an angle θ reach at P on the screen, as shown in Figure 12.1.

19.1 Building Image Classifier with CNN

The convolutional neural network (CNN) is the perfect choice for building an image classifier system. In this chapter, we will implement various CNN classifiers in Python by considering various case studies.

19.2 Dog–Cat Classifier

Consider an image classification problem that involves identifying photos as either containing a cat or dog. Our task is to develop a CNN model that can identify the object in the image as a dog or cat. The CNN model will be trained on a dataset of images containing a dog or cat, and then this trained model will be used for classification. Once the model is built, it can be used as a template to build other models for predicting the class of any image on the trained dataset. It means we can use the same model to classify the tumors by training them on medical images.

Preparing the Dataset

To build a dog–cat classifier, we will use 11000 manually annotated photos of dogs and cats, where 5500 photos are of dogs, and the remaining ones are of cats. This is the partial dataset that has been created from the dogs-vs-cats dataset available at Kaggle. The link for this dataset is given below.

https://www.kaggle.com/competitions/dogs-vs-cats/data

This full dataset contains 37500 images (24000 in the train and 13500 in the test folders), which must be manually segregated into dog and cat folders. Since the dataset is huge and segregation is time-consuming, we took 10000 images (5000 images of dogs and 5000 images of cats) in the train folder and 1000 images in the test folder (500 images of dogs and 500 images of cats).

12.1 Introduction to Clustering

In machine learning (ML), labeling the data is one of the crucial tasks. But sometimes, we do not have the labeled data. Even though the data is not labeled, we can still analyze it using clustering techniques. As you know, algorithms in ML are broadly classified as supervised and unsupervised techniques. In the case of supervised learning, the input data points/examples are labeled, while in unsupervised learning, the input data points/examples are not labeled. Cluster analysis, also called clustering, comes under unsupervised learning. Here, the input data points are not labeled. Clustering is the most popular technique in unsupervised learning.

Clustering is defined as grouping the input data points into various clusters/groups based on their similarity.

A cluster contains objects that are more similar to each other. In other words, during cluster analysis, the data is grouped into classes or clusters, so that records within a cluster (intra-cluster) have high similarity with one another but have high dissimilarities in comparison to objects in other clusters (inter-cluster).

The clustering algorithm aims to minimize the intra-cluster distance and maximize the inter-cluster distance, as shown in Figure 12.1.

An example of clustering is shown in Figure 12.2. Here, records in the input have different shapes. Here, we only have the input data without any label of shape. After applying the clustering algorithm, they were classified into three types of clusters. Here, the clustering algorithm considers the dimensions of the object and its color as the input features. Records whose features are highly similar are gathered to form a single cluster. In this case, we get three clusters representing three types of records, i.e., it clusters rhombus, circle, and triangle separately, as shown in Figure 12.2.

Learning Outcomes

After reading this chapter, the reader will be able to

• Understand the distribution of energy density in a black body radiation as a function of wavelength and temperature

• Derive classical laws of black body radiation such as Wien distribution law and Rayleigh–Jeans law

• Get an idea about the development of quantum theory of radiation

• Understand Planck's quanta postulates and explain the black body radiation spectrum

• Derive Planck's law of black body radiation

• Verify Planck's law of black body radiation experimentally

• Derive Wien distribution law and Rayleigh–Jeans law and explain ultraviolet catastrophe from Planck's law of black body radiation

• Determine the temperature of cosmic microwave background radiation using Planck's law of black body radiation

• Solve numerical problems and multiple choice questions on black body Radiation

11.1 Introduction

Figure 11.1 The whole electromagnetic spectrum. Thermal radiation ranges in frequency from the shortest infrared rays through the visible-light spectrum to the longest ultraviolet rays.

Radiation emitted from the surface of a heated source is known as thermal radiation. In this process, thermal energy is spread out in all directions in the form of electromagnetic radiation and travels directly to its point of absorption at the speed of light. It does not require an intervening medium for its propagation. The wavelength of thermal radiation ranges from the longest infrared rays through the visible-light spectrum to the shortest ultraviolet rays. Such electromagnetic spectrum is shown in Figure 11.1 as a function of frequency. The distribution of radiant energy with their corresponding intensities within various ranges of wavelengths is governed by the temperature of the emitting surface .

In the Korean drama My Liberation Notes (Netflix, 2022), written by Park Hay-Young, three office workers sit in the human resources (HR) manager's room. They have been asked to meet with the HR manager to address a specific issue related to the company's HR policy. As part of a neoliberal workplace well-being initiative, the company encourages employees to join a club to explore their hobbies and other interests. According to its institutional logic, if employees are allowed to pursue their personal interests at the workplace, it will make them ‘happy’ and creative, eventually leading to greater productivity. The HR manager regularly emails employees about various clubs, such as photography, hiking, and pottery to encourage them to choose a club.

Three colleagues from different departments receive regular club invitations via email, but none of them find the clubs interesting enough to join. To them, the exercise seems absurd, especially given their challenges, such as the high cost of living, normalized overwork, and dignity violations in the workplace. The HR manager's attempts to persuade them to join a club seem meaningless and futile in the face of their existential crisis. They are tired of the monotony of their lives, which limits their hope and possibilities. Consequently, they frequently reject club suggestions. Eventually, the HR manager asks them to meet her in the office and they provide vague answers, but as they leave, they realize they can create their own club to avoid the pressure of joining one. They name it the ‘Liberation Club’, whose objective is to journal their existential struggles to overcome personal and social reifications.