🟦 #data
—————
🟡 Name : Data Contracts
🟡 Edition: 1
🟡 Year : 2025
🟡 Publisher ‏ : O'Reilly
🟡 Author : B. E. Schmidt, Mark Freeman, Chad Sanderson
🟡 Paperback : 347
—————
▎Table of Contents

Part I. Introduction to the Data Contract Architecture
1. Why the Industry Now Needs Data Contracts
2. Data Quality Isn’t About Pristine Data
3. The Challenges of Scaling Data Infrastructure
4. An Introduction to Data Contracts

Part II. Implementation of the Data Contract Architecture
5. The Data Contract Components: Data Assets and Contract Definition
6. The Data Contract Components: Detection and Prevention
7. Implementing Data Contracts
8. Real-World Case Studies of Data Contracts in Production

Part III. Getting Leadership Buy-in for the Data Contract Architecture
9. Shift Left: The Cultural Change Needed for Data Contracts
10. Change Management: The Crux of People, Process, and Technology
11. Creating Your First Wins with Data Contracts
12. Measuring the Impact of Data Contracts
Conclusion: AI and the Future of Data Contracts
Appendix. Additional Resources

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2️⃣ #LLM
—————
🟡 Name : LLM Engineer's Handbook
🟡 Edition: 1
🟡 Year : 2024
🟡 Publisher ‏ : Packt
🟡 Author : Paul Iusztin, Maxime Labonne
🟡 Paperback : 522
—————
▎Table of Contents

1. Understanding the LLM Twin Concept and Its Architecture
2. Tooling and Installation
3. Data Engineering
4. RAG Feature Pipeline
5. Supervised Fine-Tuning
6. Fine-Tuning with Preference Alignment
7. Evaluating LLMs
8. Inference Optimization
9. RAG Inference Pipeline
10. Inference Pipeline Deployment
11. MLOps and LLMOps

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6️⃣ #python
—————
🟡 Name : Python Object-Oriented Programming
🟡 Edition: 5
🟡 Year : 2026
🟡 Publisher ‏ : Packt
🟡 Author : Steven F. Lott, Dusty Phillips
🟡 Paperback : 542
—————
▎Table of Contents

1. Object-Oriented Design
2. Objects in Python
3. When Objects Are Alike
4. Expecting the Unexpected
5. When to Use Object-Oriented Programming
6. Abstract Base Classes and Operator Overloading
7. Python Type Hints
8. Python Data Structures
9. The Intersection of Object-Oriented and Functional Programming
10. The Iterator Pattern
11. Common Design Patterns
12. Advanced Design Patterns
13. Testing Object-Oriented Programs
14. Concurrency

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7️⃣ #deep_learning
—————
🟡 Name : Deep Learning in Banking
🟡 Edition: 1
🟡 Year : 2026
🟡 Publisher ‏ : Wiley
🟡 Author : Sebastian Maldonado, Cristian Bravo
🟡 Paperback : 377
—————
▎Table of Contents

1. Introduction
2. Image Processing and Convolutional Neural Networks
3. Time Series and Recurrent Models
4. Text Data and Transformers
5. Financial Contagion and Network Models
6. Generative AI and Large Language Models
7. Multimodal Data and Information Fusion
8. Fairness, Accountability, Explainability, and Causality
9. Perspectives on the Future of AI in Banking

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🔑 #security
—————
🟡 Name : Learning Ransomware Response & Recovery
🟡 Edition: 1
🟡 Year : 2026
🟡 Publisher ‏ : O'Reilly
🟡 Author : Michael Saylor, W. Curtis Preston
🟡 Paperback : 520
—————
▎Table of Contents

Part I. Identify
Understanding the Threat Landscape and Your Critical Assets
1. What Is Ransomware?
2. Your Backup System Is Under Attack

Part II. Protect
Building Your Defenses Before the Attack
3. Backup and Recovery Basics
4. Stop Most Ransomware
5. Minimize the Blast Radius
6. Get Ready for Battle
7. Make Your Incident Response Plan

Part III. Detect
Spotting Ransomware Before It’s Too Late
8. Detection Tools

Part IV. Respond
Fighting Through the Crisis When Ransomware Strikes
9. The First 12 Hours
10. The Marathon
11. Analyzing the Breach
12. Advanced Analysis and Forensics
13. Contain the Attack
14. Eradicate the Threat

Part V. Recover
Rising from the Ashes Stronger Than Before
15. Restore and Recover
16. Post-Mortem Analysis

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6️⃣ #python
—————
🟡 Name : Effective Python
🟡 Edition: 3
🟡 Year : 2024
🟡 Publisher ‏ : Pearson
🟡 Author : Brett Slatkin
🟡 Paperback : 669
—————
▎Table of Contents

1 Pythonic Thinking
2 Strings and Slicing
3 Loops and Iterators
4 Dictionaries
5 Functions
6 Comprehensions and Generators
7 Classes and Interfaces
8 Metaclasses and Attributes
9 Concurrency and Parallelism
10 Robustness
11 Performance
12 Data Structures and Algorithms
13 Testing and Debugging
14 Collaboration

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⚫️ #kafka
—————
🟡 Name : Kafka for Architects
🟡 Edition: 1
🟡 Year : 2026
🟡 Publisher ‏ : Manning
🟡 Author : Katya Gorshkova
🟡 Paperback : 393
—————
▎Table of Contents

Part 1 Exploring Kafka building blocks
1 Getting to know Kafka as an architect
2 Kafka cluster data architecture
3 Kafka clients and message production
4 Creating consumer applications

Part 2 Solving problems with Kafka
5 Kafka in real-world use cases
6 Defining data contracts
7 Kafka interaction patterns
8 Designing streaming applications

Part 3 Delivering projects with Kafka
9 Managing Kafka within the enterprise
10 Organizing a Kafka project
11 Operating Kafka
12 What’s next for Kafka

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6️⃣ #python
—————
🟡 Name : Software Design for Python Programmers
🟡 Edition: 1
🟡 Year : 2026
🟡 Publisher ‏ : Manning
🟡 Author : Ronald Mak
🟡 Paperback : 456
—————
▎Table of Contents

Part 1 Introduction
1 The path to well-designed software
2 Iterate to achieve good design

Part 2 Design the right application
3 Get requirements to build the right application
4 Good class design to build the application right

Part 3 Design the application right
5 Hide class implementations
6 Don’t surprise your users
7 Design subclasses right

Part 4 Design patterns solve application architecture problems
8 The Template Method and Strategy Design Patterns
9 The Factory Method and Abstract Factory Design Patterns
10 The Adapter and Façade Design Patterns
11 The Iterator and Visitor Design Patterns
12 The Observer Design Pattern
13 The State Design Pattern
14 The Singleton, Composite, and Decorator Design Patterns

Part 5 Additional design techniques
15 Designing solutions with recursion and backtracking
16 Designing multithreaded programs

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3️⃣ #cpp #software_development
—————
🟡 Name : Building Programming Language Interpreters
🟡 Edition: 1
🟡 Year : 2026
🟡 Publisher ‏ : Packt
🟡 Author : Daniel Ruoso
🟡 Paperback : 371
—————
▎Table of Contents

Part 1. Modeling the Programming Language Runtime Environment
1. Defining the Scope
2. The Blurred Lines Between Native Code, Virtual Machines, and Interpreters
3. Instructions, Concurrency, Inputs, and Outputs
4. Native Types, User Types, and Extension Points
5. Putting It All Together. Making Trade-Off Decisions

Part 2. Modeling the Programming Language Syntax
6. Review of Programming Language Paradigms
7. Values, Containers, and the Language Meta-Model
8. Lexical Scopes
9. Putting It All Together and Creating a Coherent Vision

Part 3. Implementing the Interpreter Runtime
10. Initialization and Entry Point
11. Execution Frames, the Stack, and Continuations
12. Running and Testing Language Operators

Part 4. Interpreting Source Code
13. Lexing. Turning Text into a Stream of Tokens
14. Parsing. Turning a Stream of Tokens into a Parse Tree
15. Analyzing. Turning a Parse Tree into an Abstract Syntax Tree
16. Generating. Turning an Abstract Syntax Tree into Instructions
17. Proving That It Works
18. Unlock Your Exclusive Benefits

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7️⃣ #LLM
—————
🟡 Name : Privacy and Security for Large Language Models
🟡 Edition: 1
🟡 Year : 2026
🟡 Publisher ‏ : O'Reilly
🟡 Author : Baihan Lin
🟡 Paperback : 316
—————
▎Table of Contents

1. Introduction
2. Understanding Large Language Models
3. Evaluating the Privacy and Security Risks of LLMs
4. Privacy-Preserving Training Techniques
5. Secure Deployment of LLMs
6. Adversarial Attacks and Defenses
7. Ethical Considerations in Fine-Tuning LLMs
8. Navigating the Cultural, Social, and Legal Landscapes
9. Building Privacy-Preserving AI Capabilities

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6️⃣ #python #machine_learning
—————
🟡 Name : Scikit-learn Cookbook
🟡 Edition: 1
🟡 Year : 2025
🟡 Publisher ‏ : Packt
🟡 Author : John Sukup
🟡 Paperback : 387
—————
▎Table of Contents

1. Common Conventions and API Elements of scikit-learn
2. Pre-Model Workflow and Data Preprocessing
3. Dimensionality Reduction Techniques
4. Building Models with Distance Metrics and Nearest Neighbors
5. Linear Models and Regularization
6. Advanced Logistic Regression and Extensions
7. Support Vector Machines and Kernel Methods
8. Tree-Based Algorithms and Ensemble Methods
9. Text Processing and Multiclass Classification
10. Clustering Techniques
11. Novelty and Outlier Detection
12. Cross-Validation and Model Evaluation Techniques
13. Deploying scikit-learn Models in Production
14. Unlock Your Exclusive Benefits

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6️⃣ #python #software_engineering
—————
🟡 Name : Hands-On Software Engineering with Python
🟡 Edition: 2
🟡 Year : 2025
🟡 Publisher ‏ : Packt
🟡 Author : Brian Allbee
🟡 Paperback : 627
—————
▎Table of Contents

1. Introduction
2. Programming Versus Software Engineering Revisited
3. The Software Development Life Cycle
4. System Modeling
5. Methodologies, Paradigms, and Practices
6. Code Style and Related Standards
7. Functional Code Standards
8. Revisiting Development Tools
9. Revising the hms_sys System Project
10. Updating Projects and Processes
11. Re-Examining Options for Business Objects
12. Reviewing Business Object Data Persistence
13. Data Persistence and BaseDataObject
14. Testing the Business Objects
15. CI/CD Options
16. API Options
17. Assembling the API
18. The Final API, Deployed to AWS
19. Unlock Your

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7️⃣ #data_science #LLM
—————
🟡 Name : The Kaggle Book
🟡 Edition: 2
🟡 Year : 2025
🟡 Publisher ‏ : Packt
🟡 Author : Luca Massaron, Bojan Tunguz, Konrad Banachewicz
🟡 Paperback : 708
—————
▎Table of Contents

Part 1. Your Kaggle Launchpad. Mastering the Essentials
1. Introducing Kaggle and Other Data Science Competitions
2. Organizing Data with Datasets
3. Working and Learning with Kaggle Notebooks
4. Kaggle Models
5. Leveraging Discussion Forums

Part 2. Elevating Your Game. Advanced Techniques for Competitive Success
6. Competition Tasks and Metrics
7. Designing Good Validation
8. Modeling for Tabular Competitions
9. Hyperparameter Optimization
10. Ensembling with Blending and Stacking Solutions
11. Modeling for Computer Vision
12. Modeling for NLP
13. Generative AI in Kaggle Competitions
14. Simulation and Optimization Competitions

Part 3. Kaggle for Your Career. Building Your Profile and Finding Opportunities
15. Creating Your Portfolio of Projects and Ideas
16. Finding New Professional Opportunities
17. Unlock Your Exclusive Benefits

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3️⃣ #dot_net #c_sharp #microservice
—————
🟡 Name : Microservices Design Patterns in .NET
🟡 Edition: 2
🟡 Year : 2025
🟡 Publisher ‏ : Packt
🟡 Author : Trevoir Williams
🟡 Paperback : 537
—————
▎Table of Contents

Part 1. Understanding Microservices and Design Patterns
1. Introducing Microservices – the Big Picture
2. Applying Domain-Driven Design Principles
3. Synchronous Communication between Microservices
4. Asynchronous Communication Between Microservices
5. Working with the Aggregator Pattern
6. Working with the CQRS Pattern
7. Applying Event-Sourcing Patterns

Part 2. Database and Storage Design Patterns
8. Database Design Strategies for Microservices
9. Implementing Transactions across Microservices Using the Saga Pattern

Part 3. Resiliency, Security, and Infrastructure Patterns
10. Building Resilient Microservices
11. Implementing the API and BFF Gateway Patterns
12. Micro Frontends — Extending Microservices to the Frontend
13. Securing Microservices

Part 4. Cloud Development Strategies
14. Container Hosting and Development Patterns
15. Serverless Microservices Development
16. Observability and Monitoring with Modern Tools
17. Wrapping It All Up

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#R_lang
—————
🟡 Name : The Book of R
🟡 Edition: 2
🟡 Year : 2025
🟡 Publisher ‏ : No Starch Press
🟡 Author : Tilman M. Davies
🟡 Paperback : 936 (2 جلد)
—————
▎Table of Contents

Part I. The Language
1. Getting Started
2. Numerics, Arithmetic, Assignment, and Vectors
3. Matrices and Arrays
4. Non-Numeric Values
5. Lists and Data Frames
6. Special Values, Classes, and Coercion
7. Basic Plotting
8. Reading and Writing Files
Part II. Programming
9. Calling Functions
10. Conditions and Loops
11. Writing Functions
12. Exceptions, Timings, and Visibility
Part III. Statistics and Probability
13. Elementary Statistics
14. Basic Data Visualization
15. Probability
16. Common Probability Distributions
Part IV. Statistical Testing and Modeling
17. Sampling Distributions and Confidence
18. Hypothesis Testing
19. Analysis of Variance
20. Simple Linear Regression
21. Multiple Linear Regression
22. Linear Model Selection and Diagnostics
Part V. Advanced Graphics
23. Advanced Plot Customization
24. Going Further with the Grammar of Graphics
25. Defining Colors and Plotting in Higher Dimensions
26. Interactive 3D Plots
Appendix A. Installing R and Contributed Packages
Appendix B. Working with RStudio

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2️⃣ #machine_learning #pytorch
—————
🟡 Name : Hands-On Machine Learning with Scikit-Learn and PyTorch
🟡 Edition: 1
🟡 Year : 2025
🟡 Publisher ‏ : O'Reilly
🟡 Author : Aurélien Géron
🟡 Paperback : 876
—————
▎Table of Contents

Part I. The Fundamentals of Machine Learning
1. The Machine Learning Landscape
2. End-to-End Machine Learning Project
3. Classification
4. Training Models
5. Decision Trees
6. Ensemble Learning and Random Forests
7. Dimensionality Reduction
8. Unsupervised Learning Techniques

Part II. Neural Networks and Deep Learning
9. Introduction to Artificial Neural Networks
10. Building Neural Networks with PyTorch
11. Training Deep Neural Networks
12. Deep Computer Vision Using Convolutional Neural Networks
13. Processing Sequences Using RNNs and CNNs
14. Natural Language Processing with RNNs and Attention
15. Transformers for Natural Language Processing and Chatbots
16. Vision and Multimodal Transformers
17. Speeding Up Transformers
18. Autoencoders, GANs, and Diffusion Models
19. Reinforcement Learning
Appendix A. Autodiff
Appendix B. Mixed Precision and Quantization

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2️⃣ #ai
—————
🟡 Name : Agentic AI For Dummies
🟡 Edition: 1
🟡 Year : 2025
🟡 Publisher ‏ : Wiley
🟡 Author : Pam Baker
🟡 Paperback : 350
—————
▎Table of Contents

Part 1 Understanding Agentic AI
1 Introducing Agentic AI
2 Peeking Inside the AI Agent Mind
3 Meeting Agentic AI Core Technologies
4 Interacting with Agentic AI

Part 2 Getting Started on the Agentic AI Path
5 Planning for the Shift to Agentic AI Systems
6 Sampling Sector Use Cases for AI Agents
7 Considering Risks, Ethics, and Hard Questions

Part 3 Agentic AI in the Real World
8 Reshaping Work with Agentic AI
9 Predicting Agentic AI’s Economic Impact
10 Building Agentic Systems Responsibly

Part 4 Exploring Myths and Realities
11 Dispelling Common Agentic AI Misconceptions
12 Upskilling for the Agentic Age
13 Scoping the Future of Agency

Part 5 The Part of Tens
14 Ten Surprising Ways Agentic AI Can Change Daily Life
15 Ten Things Agentic AI Is Terrible at Doing
16 Ten Bold Predictions about the Future of Agentic AI

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💡 #leadership
—————
🟡 Name : Leading High-Performance Teams
🟡 Edition: 1
🟡 Year : 2025
🟡 Publisher ‏ : Wiley
🟡 Author : Shirley Davis
🟡 Paperback : 368
—————
▎Table of Contents

Part 1 Getting Started with High Performance Teams
1 Understanding the Science of High Performance
2 Exploring Common Models of Teams
3 Identifying Key Attributes of High-Performance Teams
4 Detailing the Business Benefits of Exceptional Teams

Part 2 Becoming a High-Performance Leader
5 Developing Core Leadership Competencies
6 Advanced Skills and Strategic Behaviors for High Performance
7 Moving from Being Risk Averse to Risk Ready
8 Leading the Team through Change and Transitions

Part 3 Recruiting, Selecting, and Onboarding a High-Performance Team
9 Getting Clear on Changing Workforce Demographics and Needs
10 Sourcing and Selecting Top Talent
11 Orienting and Onboarding New Team Members

Part 4 Creating and Sustaining a World-Class Culture Where Teams Can Thrive
12 Understanding Company Culture and How It’s Established
13 Identifying and Addressing Work Culture Challenges
14 Promoting Mental Health and Well-Being on the Team
15 Navigating Team Conflict Effectively
16 Measuring and Maintaining High Performance
17 Leading Hybrid and Remote Teams
18 Operating as a Savvy Financial Business Leader

Part 5 The Part of Tens
19 Ten Best Practices for Leading a Multigenerational Team
20 Ten Team Activities That Build Trust, Collaboration, and Communication
21 Ten Practices for Productive Team Meetings
22 Ten Ways High-Performing Teams Will Evolve in Ten Years

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🔑 #security #cryptography
—————
🟡 Name : File System Forensics
🟡 Edition: 1
🟡 Year : 2025
🟡 Publisher ‏ : Wiley
🟡 Author : Fergus Toolan
🟡 Paperback : 487
—————
▎Table of Contents

Part I Preliminaries
1 Introduction
2 Linux as a Forensic Platform
3 Mathematical Preliminaries
4 Disks, Partitions and File Systems

Part II Windows File Systems
5 The FAT File System
6 The ExFAT File System
7 The NTFS File System

Part III Linux File Systems
8 The EXT2 File System
9 The EXT3/EXT4 File Systems
10 The XFS File System
11 The Btrfs File System

Part IV Apple File Systems
12 The HFS+ File System
13 The APFS File System

Part V The Future
14 Future Challenges in Digital Forensics

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🔑 #security
—————
🟡 Name : The Cybersecurity Control Playbook
🟡 Edition: 1
🟡 Year : 2025
🟡 Publisher ‏ : Wiley
🟡 Author : Jason Edwards
🟡 Paperback : 522
—————
▎Table of Contents

1 Understanding Cybersecurity Controls
2 The Risk‐Based Approach
3 Small Business Implementation
4 Medium‐Sized Enterprises
5 Large Enterprises
6 Introduction to MITRE ATT&CK & DEFEND
7 Mapping Threats to Controls Using MITRE ATT&CK
8 Enhancing Defenses with MITRE DEFEND
9 Cybersecurity Frameworks Overview
10 NIST 800‐53
11 Center for Internet Security (CIS) 18 Controls
12 Agile Implementation of Controls and
Control Frameworks
13 Adaptive Control Testing &
Continuous Improvement
14 Testing Controls in Small and Medium Enterprises
15 Control Testing in Larger and Complex Enterprises
16 Control Failures: Identification,
Management, and Reporting
17 Control Testing for Regulated Companies
18 Emerging Threats and Technologies
A: Glossary of Terms
B: Creating and Using a
Cybersecurity Risk Register
C: Creating and Using a
Cybersecurity Risk Taxonomy
D: SME Security Team Structures
E: Developing Process Maps
F: Establishing a Regulatory Change
Management Program
G: Recommended Metrics for
MITRE ATT&CK Techniques

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