Python Beginners Guide to Artificial Intelligence

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'll find a new balance of classical ideas and modern insights into machine learning. 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 using Feedforward Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, and Autoencoders. 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. 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