Strengthening Deep Neural Networks: Making AI Less Susceptible to Adversarial Trickery

Strengthening Deep Neural Networks: Making AI Less Susceptible to Adversarial Trickery

E- BookAIDeep LearningDeep Neural networks

English | July 3rd, 2019 | ISBN: 1492044954 | 246 pages | EPUB (True/Retail Copy) | 40.76 MB As deep neural networks (DNNs) become increasingly common in real-world applications, the potential to deliberately “fool” them with data that wouldn’t trick a human presents a new attack vector. This practical book examines real-world scenarios where DNNs—the

AI for People and Business: A Framework for Better Human Experiences and Business Success (True EPUB)

AI for People and Business: A Framework for Better Human Experiences and Business Success

E- BookAIAI forPeople and BusinessBusiness SucessHuman Frame work Experince

English | July 5th, 2019 | ISBN: 1492036579 | 416 pages | EPUB (True/Retail Copy) | 9.53 MB If you’re an executive, manager, or anyone interested in leveraging AI within your organization, this is your guide. You’ll understand exactly what AI is, learn how to identify AI opportunities, and develop and execute a successful AI

AI Self-Driving Cars Evolvement: Practical Advances in Artificial Intelligence and Machine Learning

AI Self-Driving Cars Evolvement: Practical Advances in Artificial Intelligence and Machine Learning

E- BookAIartificial intelligencemachine learningSelf-Driving Cars

English | March 25, 2019 | ISBN: 1732976082 | 256 pages | AZW3 | 1.59 Mb A vital book by industry thought leader and global AI expert, Dr. Lance Eliot, and based on his popular AI Insider series and podcasts, this fascinating book provides evolving advances for the advent of AI self-driving driverless cars. Included

Machine Learning and AI: Support Vector Machines in Python

Machine Learning and AI: Support Vector Machines in Python

BusinessTutosUdemyAImachine learningMachinespythonVector Support

MP4 | Video: AVC 1280×720 | Audio: AAC 44KHz 2ch | Duration: 9 Hours Genre: eLearning | 3 GB | Language: English What you’ll learn Apply SVMs to practical applications: image recognition, spam detection, medical diagnosis, and regression analysis Understand the theory behind SVMs from scratch (basic geometry) Use Lagrangian Duality to derive the Kernel

[Udacity] – AI Programming with Python (Nanodegree Program)

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Course preview What You Will Learn? Learn AI Fundamentals Learn the essential foundations of AI: the programming tools (Python, NumPy, PyTorch), the math (calculus and linear algebra), and the key techniques of neural networks (gradient descent and backpropagation). Why Take This Nanodegree Program? Learning to program with Python, one of the most widely used languages

[Udemy] – Building Recommender Systems with Machine Learning and AI

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Course Preview What you’ll learn Understand and apply user-based and item-based collaborative filtering to recommend items to users Create recommendations using deep learning at massive scale Build recommender systems with neural networks and Restricted Boltzmann Machines (RBM’s) Make session-based recommendations with recurrent neural networks and Gated Recurrent Units (GRU) Build a framework for testing and

[Udemy] – Artificial Intelligence 2018: Build the Most Powerful AI

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Learn, build and implement the most powerful AI model at home. Compete with multi-billion dollars companies using ARS. Created by Hadelin de Ponteves, Kirill Eremenko, SuperDataScience Team Last updated 9/2018 English English [Auto-generated]  What Will I Learn? Build an AI Understand the theory behind augmented random search algorithm Learn how to build most powerful AI algorithm

[Packtpub.Com] – Building Recommender Systems with Machine Learning and AI

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By: Frank KaneReleased: Friday, September 21, 2018  Help people discover new products and content with deep learning, neural networks, and machine learning recommendations. Video Details ISBN 9781789803273Course Length 9 hours 14 minutes Table of Contents GETTING STARTED INTRODUCTION TO PYTHON EVALUATING RECOMMENDER SYSTEMS A RECOMMENDER ENGINE FRAMEWORK CONTENT-BASED FILTERING NEIGHBORHOOD-BASED COLLABORATIVE FILTERING MATRIX FACTORIZATION METHODS