Python Advanced Guide
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Python: Advanced Guide to Artificial Intelligence
Author: Giuseppe Bonaccorso
language: en
Publisher: Packt Publishing Ltd
Release Date: 2018-12-21
Demystify the complexity of machine learning techniques and create evolving, clever solutions to solve your problems Key FeaturesMaster supervised, unsupervised, and semi-supervised ML algorithms and their implementation Build deep learning models for object detection, image classification, similarity learning, and moreBuild, deploy, and scale end-to-end deep neural network models in a production environmentBook Description This Learning Path is your complete guide to quickly getting to grips with popular machine learning algorithms. You'll be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this Learning Path will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries. You'll bring the use of TensorFlow and Keras to build deep learning models, using concepts such as transfer learning, generative adversarial networks, and deep reinforcement learning. Next, you'll learn the advanced features of TensorFlow1.x, such as distributed TensorFlow with TF clusters, deploy production models with TensorFlow Serving. You'll implement different techniques related to object classification, object detection, image segmentation, and more. By the end of this Learning Path, you'll have obtained in-depth knowledge of TensorFlow, making you the go-to person for solving artificial intelligence problems This Learning Path includes content from the following Packt products: Mastering Machine Learning Algorithms by Giuseppe BonaccorsoMastering TensorFlow 1.x by Armando FandangoDeep Learning for Computer Vision by Rajalingappaa ShanmugamaniWhat you will learnExplore how an ML model can be trained, optimized, and evaluatedWork with Autoencoders and Generative Adversarial NetworksExplore the most important Reinforcement Learning techniquesBuild end-to-end deep learning (CNN, RNN, and Autoencoders) modelsWho this book is for This Learning Path is for data scientists, machine learning engineers, artificial intelligence engineers who want to delve into complex machine learning algorithms, calibrate models, and improve the predictions of the trained model. You will encounter the advanced intricacies and complex use cases of deep learning and AI. A basic knowledge of programming in Python and some understanding of machine learning concepts are required to get the best out of this Learning Path.
Advanced Guide to Python 3 Programming
Advanced Guide to Python 3 Programming 2nd Edition delves deeply into a host of subjects that you need to understand if you are to develop sophisticated real-world programs. Each topic is preceded by an introduction followed by more advanced topics, along with numerous examples, that take you to an advanced level. This second edition has been significantly updated with two new sections on advanced Python language concepts and data analytics and machine learning. The GUI chapters have been rewritten to use the Tkinter UI library and a chapter on performance monitoring and profiling has been added. In total there are 18 new chapters, and all remaining chapters have been updated for the latest version of Python as well as for any of the libraries they use. There are eleven sections within the book covering Python Language Concepts, Computer Graphics (including GUIs), Games, Testing, File Input and Output, Databases Access, Logging, Concurrency and Parallelism, Reactive Programming, Networking and Data Analytics. Each section is self-contained and can either be read on its own or as part of the book as a whole. It is aimed at those who have learnt the basics of the Python 3 language but wish to delve deeper into Python’s eco system of additional libraries and modules.
Advanced Guide to Dynamic Programming in Python: Techniques and Applications
Elevate your programming skills with the "Advanced Guide to Dynamic Programming in Python: Techniques and Applications." This comprehensive resource empowers you to solve complex problems using one of the most potent algorithmic techniques. Designed for both beginners venturing into dynamic programming and seasoned programmers seeking to refine their problem-solving abilities, this book is your pathway to enhanced programming acumen. Delve into the essentials of dynamic programming by exploring core principles, memoization techniques, and tabulation methods for optimization. Traverse through in-depth chapters on sequence challenges, graph problems, and various optimization tasks, all enriched with Python code examples, practical solutions, and insights into tackling both common and intricate problems. The "Advanced Guide to Dynamic Programming in Python" straddles theoretical understandings and practical executions, equipping you with the comprehensive knowledge needed to apply dynamic programming in diverse real-world scenarios. From grasping foundational concepts to discovering advanced strategies, this book unlocks the full potential of dynamic programming for solving intricate problems efficiently and with confidence. Whether preparing for coding interviews, improving algorithmic thinking, or expanding your programming toolkit, this guide serves as an invaluable asset in your development journey. With the "Advanced Guide to Dynamic Programming in Python," you're not just learning a technique—you're mastering an essential skill set crucial for today's complex programming challenges.