New Arrival - CSE, DS, AIML & IT

Internet of things: architecture and design principles / Raj Kamal. - 2nd ed. - Chennai: MGH, 2022.

Content: 1. Introduction to Internet of Things- 2. Design Principles for Connected Devices- 3. Business Processes, XaaS Cloud, and Security Aspects- 4. Arduino and Raspberry Pi Communication, sensing, actuating and IOs- 5. Programming APIs for M2M/IoT Applications- 6. IoT Applications development Framework- 7. IoT Case Studies in Industrial Automation, Transportation and Agriculture- 8. IoT Case Studies in Healthcare and Home Automation- 9. IoT Practical Learning and Exercises-.

Raspberry pi hardware reference / Warren W. Gay. - New Yourk: Apress, 2014.

Content: 1. The Raspberry Pi- 2. Power- 3. Header Strips, LEDs, and Reset- 4. SDRAM- 5. CPU- 6. USB- 7. Ethernet- 8. SD Card Storage- 9. UART- 10. GPIO- 11. 1-Wire Driver- 12. 12C Bus- 13. SPI Bus-.

Raspberry pi computer architecture essentials: explore raspberry pi's architecture through innovative and fun projects / Andrew K. Dennis. - Mumbai: Packt, 2016.

Content: 1. Introduction to the Raspberry Pi's Architecture and Setup- 2. Programming on Raspbian- 3. Low-Level Development with Assembly Language- 4. Multithreaded Applications with C/C++- 5. Expanding on Storage Options- 6. Low-Level Graphics Programming- 7. Exploring the Raspberry Pi's GPIO Pins- 8. Exploring Sound with the Raspberry Pi 2- 9. Building a Web Server- 10. Integrating with Third-Party Microcontrollers- 11. Final Projects-.

Web technologies for beginners / Ashwin I. Mehta. - Navi Mumbai: SPD, 2026.

Content: 1. Introduction to Internet- 2. Introduction to HTML- 3. HTML Frames, Tables, Lists and Forms 4. HTML Tables- 5. HTML Attributes- 6. Introduction to DHTML- 7. Introduction to CSS- 8. CSS Advanced- 9. Introduction to Java Script- 10. Loops and Control Statement- 11. Java Script Events- 12. Javascript Object- 13. Java Script Cookies- 14. Introduction to XML- 15. PHP- 16. PHP and MYSQL-.

Introduction to the design and & analysis of algorithms / Anany Levitin. - 3rd ed. - Noida: Person, 2017

Content: 1. Introduction- 2. Fundamentals of the Analysis of Algorithm Efficiency- 3. Brute Force and Exhaustive Search- 4. Decrease-and-Conquer- 5. Divid-and-Conquer- 6. Transform-and-Conquer- 7. Space and Time Trade-Offs- 8. Dynamic Programming- 9. Greedy Technique- 10. Iterative Improvement- 11. Limitations of Algorithms Power- 12. Coping with the Limitations of Algorithm Power 13. Useful Formulas for the Analysis of Algorithms- 14. Short Tutorial on Recurrence Relations-.

Analysis of algorithms / Subhash K. Shinde, Monika Mangla and Ninita Sharma. - Noida: Pearson, 2022.

Content: 1. Introduction to Algorithms- 2. Analyzing Algorithms- 3. Divide and Conquer- 4. Greedy Method Approach- 5. Dynamic Programming Approach- 6. Backtracking- 7. Branch and Bound- 8. Maximum Flow- 9. Sting Matching- 10. Approximation Algorithms- 11. List of Programs-.

সমস্যা সমাধানের জন্য প্রোগ্রামিং (Programming for problem solving): (ল্যাব ম্যানুয়াল সহ) / আর এস সালারিয়া (R. S. Salaria); অনুবাদক শিবকালী গুপ্ত এবং ইন্দ্রজিৎ পান. - New Delhi: Khanna Book, 2025.

Content: 1. প্রোগ্রামিং পরিচিতি- 2. গাণিতিক অভিব্যক্তি এবং অগ্রগণ্যতা- 3. শর্তাধীন শাখাবিন্যাস এবং লুপ- 4. অ্যারে- 5. মৌলিক অ্যালগোরিদম- 6. ফাংশন- 7. রিকারশন- 8. কাঠামো- 9. নির্দেশক- 10. ফাইল হ্যান্ডলিং-.

Python 3: crash course / Sharanam Shah and Vaishali Shah. - Kolkata: SPD, 2025.

Content: 1. The Workshop (Initialization)- 2. The Core (Think Like a Computer)- 3. Data Structure (Organize Information)- 4. Architecture (Professional Code Structure)- 5. The Creator Track (Game Development)- 6. The Analyst Track (Data Visualization)- 7. The Web Architect Track (Django Monolith) 8. The AI Engineer Track (Modern AI)- 9. The Polyglot Expert Track (Microservices)- 10. Reference Essential Tools & Troubleshooting Guides)- 11. Bonus - The Future (Deep Learning Preview)-.

Big data analytics: introduction to Hadoop, Spark, and Machine-Learning / Raj Kamal and Preeti Saxena.- Chennai: MGH, 2019.

Content: 1. Introduction to Big data analytics- 2. Introduction to Hadoop- 3. NoSQL Big Data Management, MongoDB and Cassandra- 4. MapReduce, Hive and Pig- 5. Spark and Big data analytics 6. Machine learning Algorithms for Big Data Analytics- 7. Data Stream Mining and Real-Time Analytics Platform - SparkStreaming- 8. Graph Analytics for Big Data and Spark GraphX Platform- 9. Text, Web Content, Link, and Social Network analytics- 10. Programming Examples in Analytics and Machine Learning using Hadoop, Spark and Python-

Database management systems / J. Suganthi, K. Sasi Kala Rani and D. Rasi. - Kolkata: SPD, 2017.

Content: 1. Introduction to DBMS- 2. Relational Model and Database Design- 3. SQL - Structured Query Language- 4. Query Processing and Query Optimization- 5. Transaction Processing, Concurrency Control and Security- 6. Trends in Database Storage Media- 7. System Architecture- 8. Special Database- 9. Data Warehousing and Data Mining-.

Artificial intelligence and machine learning / Pradeep Singh, Tapan K Gandhi and Balasubramanian Raman. - Chennai: MGH, 2026.

Content: 1. Foundations of Artificial Intelligence- 2. Intelligent Agents and Environments- 3. Search and Problem Solving- 4. Knowledge Representation and Logical Learning- 5. Probabilistic Reasoning and Uncertainty- 6. Machine Learning Basics and Techniques- 7. Supervised Learning Techniques- 8. Unsupervised Learning Techniques- 9. Neural Networks and Deep Learning- 10. Reinforcement Learning- 11. Advanced Machine Learning Techniques- 12. Natural Language Processing- 13. Data mining and Predictive Analysis- 14. Data Warehousing and Business Intelligence- 15. Ethics in AI and Future Trends-.

Artificial intelligence: a practical approach / Rajib Chopra. - 2nd rev. ed. - Noida: S.Chand, 2013.

Content: 1. An Introduction- 2. AI Approaches- 3. Games Solving (Adverbial Search)- 4. Knowledge Representation- 5. Reasoning Uncertainty- 6. Natural Language Processing- 7. Learning- 8. Expert Systems- 9. I Programming Languages - LISP and Prolog- 10. Testing AI Programming-.

Generative deep learning: teaching machines to paint, write, compose, and play / David Foster. - 2nd ed.- Navi Mumbai: SPD, 2023.

Content: 1. Generative Modeling- 2. Deep Learning- 3. Variational Autoencoders- 4. Generative Adversarial Networks- 5. Autoregressive Models- 6. Normalizing Flow Models- 7. Energy-Based Models 8. Diffusion Models- 9. Transformers- 10. Advanced GANs- 11. Music Generation- 12. World Models- 13. Multimodal Models- 14. Conclusion-.

Applied machine learning / M. Gopal. - Chennai: MGH, 2022.

Content: 1. Introduction- 2. Supervised Learning: rational and basics- 3. Statistical Learning- 4. Learning wit Support Vector Machines (SVMs)- 5. Learning with Neural Networks (NNs)- 6. Decision Tree Learning and Tree-Based Ensembles- 7. data Clustering- 8. Data Transformations- 9. Understanding Machine Learning by application-.

Data analytics / Anil Maheshwari. - 2nd ed. - Chennai: MGH, 2023.

Content: 1. Wholeness of Data Analytics- 2. Business Intelligence Concepts and Applications- 3. Data Warehousing- 4. Data Mining- 5. Data Visualization- 6. Decision Trees- 7. Regression- 8. Artificial Neural Networks- 9. Cluster analysis- 10. Association Rule Mining- 11. Text Mining- 12. Naive Bayes Analysis 13. Support Vector Machines- 14. Web Mining- 15. Social Network Analysis- 16. Big Data- 17. Artificial Intelligence Primer- 18. Data Ownership and Privacy- 19. data Science Careers- 20. Data Wrangling and Dimensionality Reduction-.

Hands-on large language models: language understanding and generation / Jay Alammar and Maarten Grootendorst. - Navi Mumbai: SPD, 2024.

Content: 1. An Introduction to Large Language Models- 2. Tokens and Embeddings- 3. Looking Inside Large Language Models- 4. Text Classification- 5. Text Clustering and Top Modeling- 6. Prompt Engineering- 7. Advanced Text Generation Techniques and Tools- 8. Semantic Search and Retrieval Augmented Generation- 9. Multimodal Large Language Models- 10. Creating Text Embedding Models 11. Fine-Tuning Representation Models for Classification- 12. Fin-Tuning Generation Models-.

Mastering Big Data Analytics with Hadoop: from basics to advanced - a practical data analytics guide by Brinthakumari S ...[et al.]; Pub: Clever Fox

Content: 1. Introduction to big data- 2. Apache hadoop- 3. Data analytics with "R"- 4. Interview questions, case studies, and hadoop essentials-

Fundamentals of Digital Image Processing by Anil K. Jain; Pub: Pearson

Content: 1. Introduction- 2. Two-Dimensional systems and mathematical prelimanaries- 3. Image Perception- 4. Image Sampling and Quantization- 5. Image Transforms- 6. Image Representation by Stochastic Models- 7. Image Enhancement- 8. Image Filtering and Restoration- 9. Image Analysis and Computer Vision- 10. Image Reconstruction from Projections- 11. Image Data Compression-

Learning Computer Architecture with Raspberry Pi by Eben Upton; Pub: Wiley, 2016

Content: 1. Introduction- 2. The Shape of a Computer Phenomenon- 3. Recapping Computing- 4. Electronic Memory- 5. ARM Processors and Systems-on-a Chip- 6. Programming- 7. Non-Volatile Storage- 8. Wired and Wireless Ethernet- 9. Operating Systems- 10. Video Codes and Video Compressions- 11. 3D Graphics- 12. Audio- 13. Input/Output-

Problem Solving and programming in C: a computational thinking approach by S. Sridhar; Pub: Pearson, 2025

Content: 1. Introduction to C Programming- 2. Problem solving and computational thinking- 3. Elements of C- 4. Operators and Expressions- 5. Conditional statements- 6. Looping Statements- 7. Introduction to functions- 8. Arrays- 9. Strings- 10. Pointers- 11. Advanced pointers- 12. Structures and Unions- 13. Advanced functions- 14. Exploring standard libraries- 15. Files- 16. Preprocessors directives- 17. Linux and C programming-

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