Develop real-world applications powered by the latest advances in intelligent systems

Key Features

• Gain real-world contextualization using deep learning problems concerning research and application
• Get to know the best practices to improve and optimize your machine learning systems and algorithms
• Design and implement machine intelligence using real-world AI-based examples

Book Description

This Learning Path offers practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis. You will be introduced to various machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples. You will learn to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open-source Python libraries.

Throughout the Learning Path, you'll learn how to develop deep learning applications for machine learning systems. Discover how to attain deep learning programming on GPU in a distributed way.

By the end of this Learning Path, you know the fundamentals of AI and have worked through a number of case studies that will help you apply your skills to real-world projects.

This Learning Path includes content from the following Packt products:

• Artificial Intelligence By Example by Denis Rothman
• Python Deep Learning Projects by Matthew Lamons, Rahul Kumar, and Abhishek Nagaraja
• Hands-On Artificial Intelligence with TensorFlow by Amir Ziai, Ankit Dixit

What you will learn

• Use adaptive thinking to solve real-life AI case studies
• Rise beyond being a modern-day factory code worker
• Understand future AI solutions and adapt quickly to them
• Master deep neural network implementation using TensorFlow
• Predict continuous target outcomes using regression analysis
• Dive deep into textual and social media data using sentiment analysis

Who this book is for

This Learning Path is for anyone who wants to understand the fundamentals of Artificial Intelligence and implement it practically by devising smart solutions. You will learn to extend your machine learning and deep learning knowledge by creating practical AI smart solutions. Prior experience with Python and statistical knowledge is essential to make the most out of this Learning Path.

Table of Contents

1. Become an Adaptive Thinker
2. Think Like a Machine
3. Apply Machine Thinking to a Human Problem
4. Become an Unconventional Innovator
5. Manage the Power of Machine Learning and Deep Learning
6. Focus on Optimizing Your Solutions
7. When and How to Use Artificial Intelligence
8. Revolutions Designed for Some Corporations and Disruptive Innovations for Small to Large Companies
9. Getting Your Neurons to Work
10. Applying Biomimicking to Artificial Intelligence
11. Conceptual Representation Learning
12. Optimizing Blockchains with AI
13. Cognitive NLP Chatbots
14. Improve the Emotional Intelligence Deficiencies of Chatbots
15. Building Deep Learning Environments
16. Training NN for Prediction Using Regression
17. Generative Language Model for Content Creation
18. Building Speech Recognition with DeepSpeech2
19. Handwritten Digits Classification Using ConvNets
20. Object Detection Using OpenCV and TensorFlow
21. Building Face Recognition Using FaceNet
22. Generative Adversarial Networks
23. From GPUs to Quantum computing - AI Hardware
24. TensorFlow Serving

About the Authors

Denis Rothman graduated from l'Université Paris-Sorbonne and l'Université Paris-Diderot, writing one of the very first word2matrix embedding solutions. He began his career authoring one of the first AI cognitive NLP chatbots applied as a language teacher for Moët et Chandon and other companies. He authored an AI resource optimizer for IBM and apparel producers. He then authored an Advanced Planning and Scheduling (APS) solution used worldwide.

Matthew Lamons's background is in experimental psychology and deep learning. Founder and CEO of Skejul--the AI platform to help people manage their activities. Named by Gartner, Inc. as a "Cool Vendor" in the "Cool Vendors in Unified Communication, 2017" report. He founded The Intelligence Factory to build AI strategy, solutions, insights, and talent for enterprise clients and incubate AI tech startups based on the success of his Applied AI MasterMinds group. Matthew's global community of more than 85 K are leaders in AI, forecasting, robotics, autonomous vehicles, marketing tech, NLP, computer vision, reinforcement, and deep learning. Matthew invites you to join him on his mission to simplify the future and to build AI for good.

Rahul Kumar is an AI scientist, deep learning practitioner, and independent researcher. His expertise in building multilingual NLU systems and large-scale AI infrastructures has brought him to Copenhagen, where he leads a large team of AI engineers as Chief AI Scientist at Jatana. Often invited to speak at AI conferences, he frequently travels between India, Europe, and the US where, among other research initiatives, he collaborates with The Intelligence Factory as NLP data science fellow. Keen to explore the ramifications of emerging technologies for his next book, he's currently involved in various research projects on Quantum Computing (QC), high-performance computing (HPC), and the brain-computer interaction (BCI).