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"Build deep learning applications, such as computer vision, speech recognition, and chatbots, using frameworks such as TensorFlow and Keras. This book helps you to ramp up your practical know-how in a short period of time and focuses you on the domain, models, and algorithms required for deep learning applications. Deep Learning with Applications Using Python covers topics such as chatbots, natural language processing, and face and object recognition. The goal is to equip you with the concepts, techniques, and algorithm implementations needed to create programs capable of performing deep learning. This book covers intermediate and advanced levels of deep learning, including convolutional neural networks, recurrent neural networks, and multilayer perceptrons. It also discusses popular APIs such as IBM Watson, Microsoft Azure, and scikit-learn. You will: Work with various deep learning frameworks such as TensorFlow, Keras, and scikit-learn. Build face recognition and face detection capabilities Create speech-to-text and text-to-speech functionality Make chatbots using deep learning. "

"Summary Design and develop cutting-edge applications in C# using proven object-oriented methodologies and best practices--Cover.Contents Introduction -- Overview of object-oriented programming -- Designing OOP solutions : identifying the class structure -- Designing OOP solutions : modeling the object interaction -- Designing OOP solutions : a case study -- Introducing the .NET Framework and Visual Studio -- Creating classes -- Creating class hierarchies -- Implementing object collaboration -- Working with collections -- Implementing the data access layer -- Developing Windows applications -- Developing web applications -- Developing and consuming WCF services -- Developing the OSO application -- Wrapping up -- Appendix A: Fundamental programming concepts -- Appendix B: Exception handling in C# -- Appendix C: Installing the required software."

"Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is often acquired by practitioners by reading source code, manuals, and posting questions on community forums, which tends to be a slow and a painful process.Deep Learning with Python allows you to ramp up to such practical know-how in a short period of time and focus more on the domain, models, and algorithms. This book briefly covers the mathematical prerequisites and fundamentals of deep learning, making this book a good starting point for software developers who want to get started in deep learning. A brief survey of deep learning architectures is also included. Deep Learning with Python also introduces you to key concepts of automatic differentiation and GPU computation which, while not central to deep learning, are critical when it comes to conducting large scale experiments. You will: Leverage deep learning frameworks in Python namely, Keras, Theano, and Caffe Gain the fundamentals of deep learning with mathematical prerequisites Discover the practical considerations of large scale experiments Take deep learning models to production"

"Gain exclusive access to the secrets to building an enterprise AI start-up. AI innovation helps with every aspect of the business, from the supply chain, marketing, and advertising, customer service, risk management, operations to security. Industries from different verticals have been adopting AI and get real business values out of it. This book guides you through each step, from defining the business need and business model, all the way to registering IP and calculating your AI start-up valuation. You see how to perform market and technology validation, perform lean AI R&D, design AI architecture, AI product development and operationalization. The book also cover building and managing an AI team, along with attracting and keeping business and developer users, Building an Enterprise AI start-up is hard because Enterprise AI is an effort to build applications to mimic human intelligence to solve business problems. Hence it has a different challenge from building traditional non-AI applications, such as scouting, recruiting and managing AI talents; designing the most cost-efficient and scalable Enterprise AI; or establishing the best practice to operationalize AI in production As we are in the dawn of the AI-first product wave, AI-powered products for enterprises will be created for many years to come and AI Startup Strategy is the one-stop guide for it. You will: Match customers expectation VS technical feasibility Justify business values and ROI for customers Review the best business models for high valuation enterprise AI start-ups Design an AI product that gives a satisfactory experience for the user Register and value AI IP"

"Leverage the power of Artificial Intelligence (AI) to drive the growth and success of your organization. This book thoroughly explores the reasons why it is so hard to implement AI, and highlights the need to reconcile the motivations and goals of two very different groups of people, business-minded and technical-minded. Divided into five main parts (First Principles, The Why, The What, The How, The Future), you'll review case studies and examples from companies that have successfully implemented AI. Part 1 provides a comprehensive overview of the First Principles approach and its basic conventions. Part 2 provides an in-depth look at the current state of AI and why it is increasingly important to businesses of all sizes. Part 3 delves into the key concepts and technologies of AI. Part 4 shares practical guidance and actionable steps for businesses looking to implement AI. Finally, Part 5 explores how AI may continue to evolve. Grow Your Business with AI is a must-read for anyone looking to understand and harness the power of AI for business growth and to stay ahead of the curve."