This book aspires young graduates and programmers to become AI engineers and enter the world of artificial intelligence by combining powerful Python programming with artificial intelligence. Beginning with the fundamentals of Python programming, the book gradually progresses to machine learning, where readers learn to implement Python in developing predictive models.

The book provides a clear and accessible explanation of machine learning, incorporating practical examples and exercises that strengthen understanding. We go deep into deep learning, another vital component of AI. Readers gain a thorough understanding of how Python's frameworks and libraries can be used to create sophisticated neural networks and algorithms, which are required for tasks such as image and speech recognition. Natural Language Processing is also covered in the book, with fundamental concepts and techniques for interpreting and generating human-like language covered.

The book's focus on computer vision and reinforcement learning is distinctive, presenting these cutting-edge AI fields in an approachable manner. Readers will learn how to use Python's intuitive programming paradigm to create systems that interpret visual data and make intelligent decisions based on environmental interactions. The book focuses on ethical AI development and responsible programming, emphasizing the importance of developing AI that is fair, transparent, and accountable.

Each chapter is designed to improve learning by including practical examples, case studies, and exercises that provide hands-on experience. This book is an excellent starting point for anyone interested in becoming an AI engineer, providing the necessary foundational knowledge and skills to delve into the fascinating world of artificial intelligence.

Key Learnings

• Explore Python basics and AI integration for real-world application and career advancement.
• Experience the power of Python in AI with practical machine learning techniques.
• Practice Python's deep learning tools for innovative AI solution development.
• Dive into NLP with Python to revolutionize data interpretation and communication strategies.
• Simple yet practical understanding of reinforcement learning for strategic AI decision making.
• Uncover ethical AI development and frameworks, and concepts of responsible and trustworthy AI.
• Harness Python's capabilities for creating AI applications with a focus on fairness and bias.

Table of Content

1. Introduction to Artificial Intelligence
2. Python for AI
3. Data as Fuel for AI
4. Machine Learning Foundation
5. Essentials of Deep Learning
6. NLP and Computer Vision
7. Hands-on Reinforcement Learning
8. Ethics to AI

"Learning PyTorch 2.0, Second Edition" is a fast-learning, hands-on book that emphasizes practical PyTorch scripting and efficient model development using PyTorch 2.3 and CUDA 12. This edition is centered on practical applications and presents a concise methodology for attaining proficiency in the most recent features of PyTorch.

The book presents a practical program based on the fish dataset which provides step-by-step guidance through the processes of building, training and deploying neural networks, with each example prepared for immediate implementation. Given your familiarity with machine learning and neural networks, this book offers concise explanations of foundational topics, allowing you to proceed directly to the practical, advanced aspects of PyTorch programming. The key learnings include the design of various types of neural networks, the use of torch.compile() for performance optimization, the deployment of models using TorchServe, and the implementation of quantization for efficient inference.

Furthermore, you will also learn to migrate TensorFlow models to PyTorch using the ONNX format. The book employs essential libraries, including torchvision, torchserve, tf2onnx, onnxruntime, and requests, to facilitate seamless integration of PyTorch with production environments. Regardless of whether the objective is to fine-tune models or to deploy them on a large scale, this second edition is designed to ensure maximum efficiency and speed, with practical PyTorch scripting at the forefront of each chapter.

Key Learnings

• Master tensor manipulations and advanced operations using PyTorch's efficient tensor libraries.
• Build feedforward, convolutional, and recurrent neural networks from scratch.
• Implement transformer models for modern natural language processing tasks.
• Use CUDA 12 and mixed precision training (AMP) to accelerate model training and inference.
• Deploy PyTorch models in production using TorchServe, including multi-model serving and versioning.
• Migrate TensorFlow models to PyTorch using ONNX format for seamless cross-framework compatibility.
• Optimize neural network architectures using torch.compile() for improved speed and efficiency.
• Utilize PyTorch's Quantization API to reduce model size and speed up inference.
• Setup custom layers and architectures for neural networks to tackle domain-specific problems. 
• Monitor and log model performance in real-time using TorchServe's built-in tools and configurations.

Table of Content

1. Introduction To PyTorch 2.3 and CUDA 12
2. Getting Started with Tensors
3. Building Neural Networks with PyTorch
4. Training Neural Networks
5. Advanced Neural Network Architectures
6. Quantization and Model Optimization
7. Migrating TensorFlow to PyTorch
8. Deploying PyTorch Models with TorchServe

This book's got a bunch of handy recipes for data science pros to get them through the most common challenges they face when using Python tools and libraries. Each recipe shows you exactly how to do something step-by-step. You can load CSVs directly from a URL, flatten nested JSON, query SQL and NoSQL databases, import Excel sheets, or stream large files in memory-safe batches.

Once the data's loaded, you'll find simple ways to spot and fill in missing values, standardize categories that are off, clip outliers, normalize features, get rid of duplicates, and extract the year, month, or weekday from timestamps. You'll learn how to run quick analyses, like generating descriptive statistics, plotting histograms and correlation heatmaps, building pivot tables, creating scatter-matrix plots, and drawing time-series line charts to spot trends. You'll learn how to build polynomial features, compare MinMax, Standard, and Robust scaling, smooth data with rolling averages, apply PCA to reduce dimensions, and encode high-cardinality fields with sparse one-hot encoding using feature engineering recipes.

As for machine learning, you'll learn to put together end-to-end pipelines that handle imputation, scaling, feature selection, and modeling in one object, create custom transformers, automate hyperparameter searches with GridSearchCV, save and load your pipelines, and let SelectKBest pick the top features automatically. You'll learn how to test hypotheses with t-tests and chi-square tests, build linear and Ridge regressions, work with decision trees and random forests, segment countries using clustering, and evaluate models using MSE, classification reports, and ROC curves. And you'll finally get a handle on debugging and integration: fixing pandas merge errors, correcting NumPy broadcasting mismatches, and making sure your plots are consistent.

Key Learnings

• You can load remote CSVs directly into pandas using read_csv, so you don't have to deal with manual downloads and file clutter.
• Use json_normalize to convert nested JSON responses into simple tables, making it a breeze to analyze.
• You can query relational and NoSQL databases directly from Python, and the results will merge seamlessly into Pandas.
• Find and fill in missing values using IGNSA(), forward-fill, and median strategies for all of your data over time.
• You can free up a lot of memory by turning string columns into Pandas' Categorical dtype.
• You can speed up computations with NumPy vectorization and chunked CSV reading to prevent RAM exhaustion.
• You can build feature pipelines using custom transformers, scaling, and automated hyperparameter tuning with GridSearchCV.
• Use regression, tree-based, and clustering algorithms to show linear, nonlinear, and group-specific vaccination patterns.
• Evaluate models using MSE, R², precision, recall, and ROC curves to assess their performance.
• Set up automated data retrieval with scheduled API pulls, cloud storage, Kafka streams, and GraphQL queries.

Table of Content

1. Data Ingestion from Multiple Sources
2. Preprocessing and Cleaning Complex Datasets
3. Performing Quick Exploratory Analysis
4. Optimizing Data Structures and Performance
5. Feature Engineering and Transformation
6. Building Machine Learning Pipelines
7. Implementing Statistical and Machine Learning Techniques
8. Debugging and Troubleshooting
9. Advanced Data Retrieval and Integration