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Understanding the LLM Twin Concept and Architecture Tooling and Installation Data Engineering RAG Feature Pipeline Supervised Fine-Tuning Fine-Tuning with Preference Alignment Evaluating LLMs Inference Optimization RAG Inference Pipeline Inference Pipeline Deployment MLOps and LLMOps Other Books You May Enjoy Index

Hello, C#! Welcome, .NET! Speaking C# Controlling Flow, Converting Types, and Handling Exceptions Writing, Debugging, and Testing Functions Building Your Own Types with Object-Oriented Programming Implementing Interfaces and Inheriting Classes Packaging and Distributing .NET Types Working with Common .NET Types Working with Files, Streams, and Serialization Working with Data Using Entity Framework Core Querying and Manipulating Data Using LINQ Introducing Modern Web Development Using .NET Building Websites Using ASP.NET Core Building Interactive Web Components Using Blazor Building and Consuming Web Services Epilogue Index

Getting Started with Machine Learning and Python Building a Movie Recommendation Engine with Naïve Bayes Predicting Online Ad Click-Through with Tree-Based Algorithms Predicting Online Ad Click-Through with Logistic Regression Predicting Stock Prices with Regression Algorithms Predicting Stock Prices with Artificial Neural Networks Mining the 20 Newsgroups Dataset with Text Analysis Techniques Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling Recognizing Faces with Support Vector Machine Machine Learning Best Practices Categorizing Images of Clothing with Convolutional Neural Networks Making Predictions with Sequences Using Recurrent Neural Networks Advancing Language Understanding and Generation with the Transformer Models Building an Image Search Engine Using CLIP: a Multimodal Approach Making Decisions in Complex Environments with Reinforcement Learning Other Books You May Enjoy Index

Acquire Free Financial Market Data with Cutting-edge Python Libraries Analyze and Transform Financial Market Data with pandas Visualize Financial Market Data with Matplotlib, Seaborn, and Plotly Dash Store Financial Market Data on Your Computer Build Alpha Factors for Stock Portfolios Vector-Based Backtesting with VectorBT Event-Based Backtesting Factor Portfolios with Zipline Reloaded Evaluate Factor Risk and Performance with Alphalens Reloaded Assess Backtest Risk and Performance Metrics with Pyfolio Set Up the Interactive Brokers Python API Manage Orders, Positions, and Portfolios with the IB API Deploy Strategies to a Live Environment Advanced Recipes for Market Data and Strategy Management

Solutions Architects in Organizations Principles of Solution Architecture Design Cloud Migration and Cloud Architecture Design Solution Architecture Design Patterns Cloud-Native Architecture Design Patterns Performance Considerations Security Considerations Architectural Reliability Considerations Operational Excellence Considerations Cost Considerations DevOps and Solution Architecture Framework Data Engineering for Solution Architecture Machine Learning Architecture Generative AI Architecture Rearchitecting Legacy Systems Solution Architecture Document Learning Soft Skills to Become a Better Solutions Architect

Why Retrieval Augmented Generation? RAG Embedding Vector Stores with Deep Lake and OpenAI Building Index-Based RAG with LlamaIndex, Deep Lake, and OpenAI Multimodal Modular RAG for Drone Technology Boosting RAG Performance with Expert Human Feedback Scaling RAG Bank Customer Data with Pinecone Building Scalable Knowledge-Graph-Based RAG with Wikipedia API and LlamaIndex Dynamic RAG with Chroma and Hugging Face Llama Empowering AI Models: Fine-Tuning RAG Data and Human Feedback RAG for Video Stock Production with Pinecone and OpenAI Other Books You May Enjoy Index

Section 1: Fundamentals and Core Algorithms Overview of Algorithms Data Structures Used in Algorithms Sorting and Searching Algorithms Designing Algorithms Graph Algorithms Section 2: Machine Learning Algorithms Unsupervised Machine Learning Algorithms Traditional Supervised Learning Algorithms Neural Network Algorithms Algorithms for Natural Language Processing Understanding Sequential Models Advanced Sequential Modeling Algorithms Section 3: Advanced Topics Recommendation Engines Algorithmic Strategies for Data Handling Cryptography Large-Scale Algorithms Practical Considerations Other Books You May Enjoy Index

Introduction to Large Language Models LLMs for AI-Powered Applications Choosing an LLM for Your Application Prompt Engineering Embedding LLMs within Your Applications Building Conversational Applications Search and Recommendation Engines with LLMs Using LLMs with Structured Data Working with Code Building Multimodal Applications with LLMs Fine-Tuning Large Language Models Responsible AI Emerging Trends and Innovations Other Books You May Enjoy Index

Giving Computers the Ability to Learn from Data Training Simple Machine Learning Algorithms for Classification A Tour of Machine Learning Classifiers Using Scikit-Learn Building Good Training Datasets – Data Preprocessing Compressing Data via Dimensionality Reduction Learning Best Practices for Model Evaluation and Hyperparameter Tuning Combining Different Models for Ensemble Learning Applying Machine Learning to Sentiment Analysis Predicting Continuous Target Variables with Regression Analysis Working with Unlabeled Data – Clustering Analysis Implementing a Multilayer Artificial Neural Network from Scratch Parallelizing Neural Network Training with PyTorch Going Deeper – The Mechanics of PyTorch Classifying Images with Deep Convolutional Neural Networks Modeling Sequential Data Using Recurrent Neural Networks Transformers – Improving Natural Language Processing with Attention Mechanisms Generative Adversarial Networks for Synthesizing New Data Graph Neural Networks for Capturing Dependencies in Graph Structured Data Reinforcement Learning for Decision Making in Complex Environments Other Books You May Enjoy Index

A Gentle Introduction to Python Built-In Data Types Conditionals and Iteration Functions, the Building Blocks of Code Comprehensions and Generators OOP, Decorators, and Iterators Exceptions and Context Managers Files and Data Persistence Cryptography and Tokens Testing Debugging and Profiling Introduction to Type Hinting Data Science in Brief Introduction to API Development CLI Applications Packaging Python Applications Programming Challenges Other Books You May Enjoy Index