Applied Computational Thinking with Python. Design algorithmic solutions for complex and challenging real-world problems

Sofía De Jesús, Dayrene Martinez

Computational thinking helps you to develop logical processing and algorithmic thinking while solving real-world problems across a wide range of domains. It's an essential skill that you should possess to keep ahead of the curve in this modern era of information technology. Developers can apply their knowledge of computational thinking to solve problems in multiple areas, including economics, mathematics, and artificial intelligence.This book begins by helping you get to grips with decomposition, pattern recognition, pattern generalization and abstraction, and algorithm design, along with teaching you how to apply these elements practically while designing solutions for challenging problems. You’ll then learn about various techniques involved in problem analysis, logical reasoning, algorithm design, clusters and classification, data analysis, and modeling, and understand how computational thinking elements can be used together with these aspects to design solutions. Toward the end, you will discover how to identify pitfalls in the solution design process and how to choose the right functionalities to create the best possible algorithmic solutions.By the end of this algorithm book, you will have gained the confidence to successfully apply computational thinking techniques to software development.

Applied Deep Learning and Computer Vision for Self-Driving Cars. Build autonomous vehicles using deep neural networks and behavior-cloning techniques

Sumit Ranjan, Dr. S. Senthamilarasu

Thanks to a number of recent breakthroughs, self-driving car technology is now an emerging subject in the field of artificial intelligence and has shifted data scientists' focus to building autonomous cars that will transform the automotive industry. This book is a comprehensive guide to use deep learning and computer vision techniques to develop autonomous cars. Starting with the basics of self-driving cars (SDCs), this book will take you through the deep neural network techniques required to get up and running with building your autonomous vehicle. Once you are comfortable with the basics, you'll delve into advanced computer vision techniques and learn how to use deep learning methods to perform a variety of computer vision tasks such as finding lane lines, improving image classification, and so on. You will explore the basic structure and working of a semantic segmentation model and get to grips with detecting cars using semantic segmentation. The book also covers advanced applications such as behavior-cloning and vehicle detection using OpenCV, transfer learning, and deep learning methodologies to train SDCs to mimic human driving.By the end of this book, you'll have learned how to implement a variety of neural networks to develop your own autonomous vehicle using modern Python libraries.

Applied Deep Learning with Python. Use scikit-learn, TensorFlow, and Keras to create intelligent systems and machine learning solutions

Alex Galea, Luis Capelo

Taking an approach that uses the latest developments in the Python ecosystem, you’ll first be guided through the Jupyter ecosystem, key visualization libraries and powerful data sanitization techniques before you train your first predictive model. You’ll then explore a variety of approaches to classification such as support vector networks, random decision forests and k-nearest neighbors to build on your knowledge before moving on to advanced topics.After covering classification, you’ll go on to discover ethical web scraping and interactive visualizations, which will help you professionally gather and present your analysis. Next, you’ll start building your keystone deep learning application, one that aims to predict the future price of Bitcoin based on historical public data. You’ll then be guided through a trained neural network, which will help you explore common deep learning network architectures (convolutional, recurrent, and generative adversarial networks) and deep reinforcement learning. Later, you’ll delve into model optimization and evaluation. You’ll do all this while working on a production-ready web application that combines TensorFlow and Keras to produce meaningful user-friendly results.By the end of this book, you’ll be equipped with the skills you need to tackle and develop your own real-world deep learning projects confidently and effectively.

Applied Supervised Learning with Python. Use scikit-learn to build predictive models from real-world datasets and prepare yourself for the future of machine learning

Benjamin Johnston, Ishita Mathur

Machine learning—the ability of a machine to give right answers based on input data—has revolutionized the way we do business. Applied Supervised Learning with Python provides a rich understanding of how you can apply machine learning techniques in your data science projects using Python. You'll explore Jupyter Notebooks, the technology used commonly in academic and commercial circles with in-line code running support.With the help of fun examples, you'll gain experience working on the Python machine learning toolkit—from performing basic data cleaning and processing to working with a range of regression and classification algorithms. Once you’ve grasped the basics, you'll learn how to build and train your own models using advanced techniques such as decision trees, ensemble modeling, validation, and error metrics. You'll also learn data visualization techniques using powerful Python libraries such as Matplotlib and Seaborn. This book also covers ensemble modeling and random forest classifiers along with other methods for combining results from multiple models, and concludes by delving into cross-validation to test your algorithm and check how well the model works on unseen data.By the end of this book, you'll be equipped to not only work with machine learning algorithms, but also be able to create some of your own!

Applied Unsupervised Learning with Python. Discover hidden patterns and relationships in unstructured data with Python

Benjamin Johnston, Aaron Jones, Christopher Kruger

Unsupervised learning is a useful and practical solution in situations where labeled data is not available.Applied Unsupervised Learning with Python guides you in learning the best practices for using unsupervised learning techniques in tandem with Python libraries and extracting meaningful information from unstructured data. The book begins by explaining how basic clustering works to find similar data points in a set. Once you are well-versed with the k-means algorithm and how it operates, you’ll learn what dimensionality reduction is and where to apply it. As you progress, you’ll learn various neural network techniques and how they can improve your model. While studying the applications of unsupervised learning, you will also understand how to mine topics that are trending on Twitter and Facebook and build a news recommendation engine for users. Finally, you will be able to put your knowledge to work through interesting activities such as performing a Market Basket Analysis and identifying relationships between different products.By the end of this book, you will have the skills you need to confidently build your own models using Python.

Applying Math with Python. Practical recipes for solving computational math problems using Python programming and its libraries

Sam Morley

Python, one of the world's most popular programming languages, has a number of powerful packages to help you tackle complex mathematical problems in a simple and efficient way. These core capabilities help programmers pave the way for building exciting applications in various domains, such as machine learning and data science, using knowledge in the computational mathematics domain.The book teaches you how to solve problems faced in a wide variety of mathematical fields, including calculus, probability, statistics and data science, graph theory, optimization, and geometry. You'll start by developing core skills and learning about packages covered in Python’s scientific stack, including NumPy, SciPy, and Matplotlib. As you advance, you'll get to grips with more advanced topics of calculus, probability, and networks (graph theory). After you gain a solid understanding of these topics, you'll discover Python's applications in data science and statistics, forecasting, geometry, and optimization. The final chapters will take you through a collection of miscellaneous problems, including working with specific data formats and accelerating code.By the end of this book, you'll have an arsenal of practical coding solutions that can be used and modified to solve a wide range of practical problems in computational mathematics and data science.

ArcGIS By Example. Develop three engaging ArcGIS applications to address your real-world mapping scenarios

Hussein Nasser

ArcGIS for Desktop Cookbook. Over 60 hands-on recipes to help you become a more productive ArcGIS for Desktop user

Daniela C Docan