Introduction to C programming / Reema Thareja
Content: Chapter 1: Introduction to Programming Chapter 2: Introduction to C Chapter 3: Decision Control and Looping Statements Chapter 4: Functions Chapter 5: Arrays Chapter 6: Strings Chapter 7: Pointers Chapter 8: Structure, Union, and Enumerated Data Types Chapter 9: Files Chapter 10: Preprocessor Directives Chapter 11: Linked Lists
Blockchain technology / J. Indumati
Content: 1. Fundamentals of blockchain- 2. Blockchain structure- 3. Types of blockchain networks- 4. Decentralization using blockchain- 5. Consensus mechanism- 6. History of bitcoin- 7. Technology behind bitcoin- 8. Structure of bitcoin- 9. Bitcoin mining and consensus- 10. Bitcoin networks. storage and cryptocurrencies- 11. Ethereum initiative- 12. Ethereum basics- 13. Ethereum transactions, gas- 14. Ethereum ecosystem- 15. ethereum core mechanics- 16. Hyperledger odyssey- 17. Hyperledger fabric basics- 18. Hyperledger fabric architecture- 19. Hyperledger fabric architectural components- 20. The mechanics within hyperledger fabric- 21. Use cases in blockchain
Joy with java : fundamentals of object oriented programming / Debasis Samanta and Monalisa Sarma
Content: Chapter 1. Getting Started Chapter 2. Java Language Basics Chapter 3. Encapsulation Chapter 4. Inheritance Chapter 5. Package and Interface Chapter 6. Exception Handling Chapter 7. Multithreading Chapter 8. IO and File Chapter 9. Generic Programming Index.
Fundamentals of software engineering / Rajib Mall
Content: 1. Introduction 2. Software Life Cycle Models 3. Software Project Management 4. Requirements Analysis and Specification 5. Software Design 6. Function-oriented Software Design 7. Object Modelling Using UML 8. Object-Oriented Software Development 9. User Interface Design 10. Coding and Testing 11. Software Reliability and Quality Management 12. Computer Aided Software Engineering 13. Software Maintenance 14. Software Reuse 15. Emerging Trends
Hands-on machine learning with scikit-learn, keras, and tensorflow : cocepts, tools, and techniques to build intelligent systems / Aurelien Geron
Content: 1. The Machine Learning Landscape-- 2. End-to-End Machine Learning Project-- 3. Classification-- 4. Training Models-- 5. Support Vector Machines-- 6. Decision Trees-- 7. Ensemble Learning and Random Forests-- 8. Dimensionality Reduction-- 9. Unsupervised Learning Techniques-- 10. Introduction to Artificial Neural Networks with Keras-- 11. Training Deep Neural Networks-- 12. Custom Models and Training with TensorFlow-- 13. Loading and Preprocessing Data with TensorFlow-- 14. Deep Computer Vision Using Convolutional Neural Networks-- 15. Processing Sequences Using RNNs and CNNs-- 16. Natural Language Processing with RNNs and Attention-- 17. Autoencoders, GANs, and Diffusion Models-- 18. Reinforcement Learning-- 19. Training and Deploying TensorFlow Models at Scale-- A. Machine Learning Project Checklist-- B. Autodiff-- C. Special Data Structures-- D. TensorFlow Graphs--
Machine learning design patterns : solutions to common challenges in data preparation, model building, and MLOPs / Valliappa Lakshmanan, Sara Robinson and Michael Munn
Content: 1. The need for machine learning design patterns -- 2. Data representation design patterns -- 3. Problem representation design patterns -- 4. Model training patterns -- 5. Design patterns for resilient serving -- 6. Reproducibility design patterns -- 7. Responsible AI -- 8. Connected patterns--
Understanding machine learning : from theory to algorithms / Shai-Shalev Shwatz and Shai Ben--David
Content: 1. Introduction Part I. Foundations: 2. A gentle start 3. A formal learning model 4. Learning via uniform convergence 5. The bias-complexity trade-off 6. The VC-dimension 7. Non-uniform learnability 8. The runtime of learning Part II. From Theory to Algorithms: 9. Linear predictors 10. Boosting 11. Model selection and validation 12. Convex learning problems 13. Regularization and stability 14. Stochastic gradient descent 15. Support vector machines 16. Kernel methods 17. Multiclass, ranking, and complex prediction problems 18. Decision trees 19. Nearest neighbor 20. Neural networks Part III. Additional Learning Models: 21. Online learning 22. Clustering 23. Dimensionality reduction 24. Generative models 25. Feature selection and generation Part IV. Advanced Theory: 26. Rademacher complexities 27. Covering numbers 28. Proof of the fundamental theorem of learning theory 29. Multiclass learnability 30. Compression bounds 31. PAC-Bayes Appendix A. Technical lemmas Appendix B. Measure concentration Appendix C. Linear algebra.
Algorithms in a nutshell / George T. Heineman, Gary Pollice and Stanley Selkow
Content: 1. Thinking in algorithms -- 2. The mathematics of algorithms -- 3. Algorithm building blocks -- 4. Sorting algorithms -- 5. Searching -- 6. Graph algorithms -- 7. Path finding in AI -- 8. Network flow algorithms -- 9. Computational geometry -- 10. Spatial tree structures -- 11. Emerging algorithm categories -- 12. Principles of algorithms -- 13. Benchmarking
Computational geometry : algorithms and applications / Mark de Berg ...[et al.]
Content: 1. Computational geometry- 2. Line segment intersection- 3. Polygon triangulation- 4. Linear programming- 5. Orthogonal range searching- 6. Point location- 7. Voronoi diagrams- 8. Arrangements and duality- 9. Delaunay triangulations- 10. More geometric data structures- 11. Convex hulls- 12. Binary space partitions- 13. Robot motion planning- 14. Quadtrees- 15. Visibility graphs- 16. Simplex range searching-
Generative AI with Python : the developer's guide to pretrained LLMs, vector databases, retrieval augmented generation, and agentic systems / Bert Gollnick
Content: 1. Introduction to Generative AI -- 2. Large Language Models -- 3. Pretrained models -- 4. Prompt engineering -- 5. Retrieval-Augmented Generation -- 6. Vector databases--