Python Backend Professional Bundle

Unlock the Power of Parallel Python with Dask: A Perfect Learning Guide for Aspiring Data Scientists

Dask has revolutionized parallel computing for Python, empowering data scientists to accelerate their workflows. This comprehensive guide unravels the intricacies of Dask to help you harness its capabilities for machine learning and data analysis.

Across 10 chapters, you'll master Dask's fundamentals, architecture, and integration with Python's scientific computing ecosystem. Step-by-step tutorials demonstrate parallel mapping, task scheduling, and leveraging Dask arrays for NumPy workloads. You'll discover how Dask seamlessly scales Pandas, Scikit-Learn, PyTorch, and other libraries for large datasets.

Dedicated chapters explore scaling regression, classification, hyperparameter tuning, feature engineering, and more with clear examples. You'll also learn to tap into the power of GPUs with Dask, RAPIDS, and Google JAX for orders of magnitude speedups.

This book places special emphasis on practical use cases related to scalability and distributed computing. You'll learn Dask patterns for cluster computing, managing resources efficiently, and robust data pipelines. The advanced chapters on DaskML and deep learning showcase how to build scalable models with PyTorch and TensorFlow.

With this book, you'll gain practical skills to:

• Accelerate Python workloads with parallel mapping and task scheduling
• Speed up NumPy, Pandas, Scikit-Learn, PyTorch, and other libraries
• Build scalable machine learning pipelines for large datasets
• Leverage GPUs efficiently via Dask, RAPIDS and JAX
• Manage Dask clusters and workflows for distributed computing
• Streamline deep learning models with DaskML and DL frameworks

Packed with hands-on examples and expert insights, this book provides the complete toolkit to harness Dask's capabilities. It will empower Python programmers, data scientists, and machine learning engineers to achieve faster workflows and operationalize parallel computing.

Table of Content

1. Introduction to Dask
2. Dask Fundamentals
3. Batch Data Parallel Processing with Dask
4. Distributed Systems and Dask
5. Advanced Dask: APIs and Building Blocks
6. Dask with Pandas
7. Dask with Scikit-learn
8. Dask and PyTorch
9. Dask with GPUs
10. Scaling Machine Learning Projects with Dask

If you're a Python pro looking to get the most out of your code with GPUs, then Practical GPU Programming is the right book for you. This book will walk you through the basics of GPU architectures, show you hands-on parallel programming techniques, and give you the know-how to confidently speed up real workloads in data processing, analytics, and engineering.

The first thing you'll do is set up the environment, install CUDA, and get a handle on using Python libraries like PyCUDA and CuPy. You'll then dive into memory management, kernel execution, and parallel patterns like reductions and histogram computations. Then, we'll dive into sorting and search techniques, but with a focus on how GPU acceleration transforms business data processing. We'll also put a strong emphasis on linear algebra to show you how to supercharge classic vector and matrix operations with cuBLAS and CuPy. Plus, with batched computations, efficient broadcasting, custom kernels, and mixed-library workflows, you can tackle both standard and advanced problems with ease.

Throughout, we evaluate numerical accuracy and performance side by side, so you can understand both the strengths and limitations of GPU-based solutions. The book covers nearly every essential skill and modern toolkit for practical GPU programming, but it's not going to turn you into a master overnight.

Key Learnings

• Boost processing speed and efficiency for data-intensive tasks.
• Use CuPy and PyCUDA to write and execute custom CUDA kernels.
• Maximize GPU occupancy and throughput efficiency by using optimal thread block and grid configuration.
• Reduce global memory bottlenecks in kernels by using shared memory and coalesced access patterns.
• Perform dynamic kernel compilation to ensure tailored performance.
• Use CuPy to carry out custom, high-speed elementwise GPU operations and expressions.
• Implement bitonic and radix sort algorithms for large or batch integer datasets.
• Execute parallel linear search kernels to detect patterns rapidly.
• Scale matrix operations using Batched GEMM and high-level cuBLAS routines.

Table of Content

1. Introduction to GPU Fundamentals
2. Setting up GPU Programming Environment
3. Basic Data Transfers and Memory Types
4. Simple Parallel Patterns
5. Introduction to Kernel Optimization
6. Working with PyCUDA and CuPy Features
7. Practical Sorting and Search
8. Linear Algebra Essentials on GPU

"50+ App Features with Python" is for developers who want to build real solutions, not just read theory. The book will help you work with robust, feature-centric code that brings today's most popular app capabilities to life. This includes core data management and authentication, rich user experiences, notification systems, security layers, deployment, and modern testing pipelines.

To get started, you'll set up your development environment and dive right into building APIs with FastAPI, making sure your data is validated with Pydantic, and checking out database management using SQLAlchemy. You'll get access to advanced features like CRUD endpoints, pagination, filtering, and bulk import/export without having to go back and relearn the basics. We'll go over how to set up authentication and authorization flows, including role-based access and two-factor authentication, in the context of secure, maintainable code. It then goes on to user-centric enhancements, showing how to implement drag-and-drop uploads, dynamic forms, custom error pages, and adaptive themes. Basically, it brings real interactivity to your projects. When it comes to hands-on experience, you'll be using real integrations like Celery for background tasks, Twilio for SMS, OAuth2 for social login, and webhook handling for event-driven workflows. Once it's time to deploy, you'll learn about containerization with Docker, orchestration with Kubernetes, log aggregation, and operational monitoring. Security and compliance are a big deal here, with heavy coverage of CSRF, CORS, encryption, CSP headers, and audit logging. The last few chapters are all about testing and CI/CD. You'll learn about unit and integration testing with Pytest, pipeline automation with GitHub Actions, and coverage reporting.

This book will help you level up your Python skills and get a better understanding of high-impact libraries. While it's not a roadmap to mastering every app capability, it's designed to give you hands-on experience and the confidence to build features you really want in your app.

Key Features

• Set up Python environments with reproducible workflows.
• Use FastAPI, Pydantic, and SQLAlchemy to build RESTful APIs.
• Implement CRUD, pagination, filtering, and scalable API endpoints.
• Develop authentication, password resets, roles, and two-factor security.
• Create interactive forms, uploads, error pages, and theme toggles.
• Integrate email, SMS, social logins, and webhook handling.
• Use Docker Compose and Kubernetes manifests to make it scalable.
• Secure your apps with CSRF, CORS, AES-GCM, and CSP headers.
• Monitor user actions with tamper-proof, timestamped audit trails.
• Use Pytest and GitHub Actions to automate testing and coverage.

Table of Content

1. Environment Setup & Core Data Management
2. Authentication & Authorization
3. User Experience Enhancements
4. Notifications & Integrations
5. Performance & Scalability
6. Data Processing Utilities
7. Deployment & Operations
8. Security & Compliance
9. Testing & CI/CD

This edition has been fully revised for Python 3.14, and it puts FastAPI at the centre, with a single real project (a University API) growing across every chapter. It'll be great to use Pydantic to design typed endpoints, work with PostgreSQL and MongoDB through modern async drivers, write concurrent code with structured concurrency, secure your services with PyJWT and OAuth2, and ship everything through Docker, Kubernetes, and CI/CD pipelines. After that, you'll get into microservices, AWS Lambda, gRPC, and message brokers like Kafka and RabbitMQ.

This book is a practical guide for you, not an exhaustive mastery manual. It'll give you the skills you need to get the job done, with all the knowledge you can use straight away. You won't get lost in the details, but the theory will be there when you need it. If you want to build, deploy, and operate Python backends with confidence, this is the place to start.

• Build typed, self-documenting REST APIs with FastAPI, Pydantic, and modern Python type hints.
• Structure growing services with routers, dependency injection, settings, and lifespan-managed resources.
• Model data in PostgreSQL and MongoDB using SQLAlchemy 2.0 and PyMongo async.
• Write concurrent backends with asyncio, TaskGroup, timeouts, and async database access.
• Secure applications using PyJWT, OAuth2, role-based access control, and two-factor authentication.
• Containerize and deploy services with Docker, Kubernetes, Nginx, and HTTPS.
• Automate testing and delivery through pytest and GitHub Actions pipelines.
• Decompose monoliths into microservices communicating over REST and gRPC.
• Run serverless workloads on AWS Lambda using the Python 3.14 runtime.
• Process events and background work with Kafka, Celery, and RabbitMQ.

1. Fundamentals of Backend Development
2. Getting Started with FastAPI
3. Advanced FastAPI Development
4. Flask Essentials
5. Working with Databases
6. Asynchronous Programming in Python
7. User Management and Security
8. Deploying Python Backend Applications
9. Microservices and Cloud Integration
10. Message Brokers and Asynchronous Task Processing