There’s A Captain In You is an inspiring memoir by Captain Marian Schneider who created a business based on the dream of sharing and preserving the Gulf Coast environment in and around Boca Grande, Florida. Her father’s shared passion for nature, his repeated teaching “if you teach people to love something, they’ll take care of it,” and a boat were the foundation upon which Marian built her business, Grande Tours.A self-proclaimed steward of the environment, Captain Marian ushered in the concept of ecotourism before it was widely understood or appreciated. From the onset her goal was to share the immense beauty of her immediate surroundings while underscoring the urgency of preserving it for future generations. Captain Marian is quick to say that introducing the first kayaks to Charlotte County ranks among her proudest achievements. They were the vehicle through which people were able to connect immediately with nature and provided an explosion in the popularity of her enterprise. More importantly, though, she touched many thousands of lives and spread the legacy she had inherited from her father. She empowered many to enjoy, respect and protect the bounty nature had blessed them with.

THERE'S A CAPTAIN IN YOU

Biographies & Memoirs

SCHNEIDER, MARIAN

Professionals & Academics

Bowker

Develop Bayesian Deep Learning models to help make your own applications more robust. Key Features Gain insights into the limitations of typical neural networks Acquire the skill to cultivate neural networks capable of estimating uncertainty Discover how to leverage uncertainty to develop more robust machine learning systems Book Description 
Deep learning has an increasingly significant impact on our lives, from suggesting content to playing a key role in mission- and safety-critical applications. As the influence of these algorithms grows, so does the concern for the safety and robustness of the systems which rely on them. Simply put, typical deep learning methods do not know when they don't know. 
The field of Bayesian Deep Learning contains a range of methods for approximate Bayesian inference with deep networks. These methods help to improve the robustness of deep learning systems as they tell us how confident they are in their predictions, allowing us to take more care in how we incorporate model predictions within our applications. 
Through this book, you will be introduced to the rapidly growing field of uncertainty-aware deep learning, developing an understanding of the importance of uncertainty estimation in robust machine learning systems. You will learn about a variety of popular Bayesian Deep Learning methods, and how to implement these through practical Python examples covering a range of application scenarios. 
By the end of the book, you will have a good understanding of Bayesian Deep Learning and its advantages, and you will be able to develop Bayesian Deep Learning models for safer, more robust deep learning systems. What you will learn Understand advantages and disadvantages of Bayesian inference and deep learning Understand the fundamentals of Bayesian Neural Networks Understand the differences between key BNN implementations/approximations Understand the advantages of probabilistic DNNs in production contexts How to implement a variety of BDL methods in Python code How to apply BDL methods to real-world problems Understand how to evaluate BDL methods and choose the best method for a given task Learn how to deal with unexpected data in real-world deep learning applications Who this book is for 
This book will cater to researchers and developers looking for ways to develop more robust deep learning models through probabilistic deep learning. You're expected to have a solid understanding of the fundamentals of machine learning and probability, along with prior experience working with machine learning and deep learning models. Table of Contents Bayesian Inference in the Age of Deep Learning Fundamentals of Bayesian Inference Fundamentals of Deep Learning Introducing Bayesian Deep Learning Principled Approaches for Bayesian Deep Learning Using the Standard Toolbox for Bayesian Deep Learning Practical considerations for Bayesian Deep Learning Applying Bayesian Deep Learning Next Steps in Bayesian Deep Learning

Enhancing Deep Learning with Bayesian Inference: Create more powerful, robust deep learning systems with Bayesian deep learning in Python

Computers & Technology

Benatan, Matt

Gietema, Jochem

Computer Science

Packt Publishing

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Books by SCHNEIDER, MARIAN