Genetic Algorithms and Machine Learning for Programmers: Create AI Models and Evolve Solutions

Genetic Algorithms and Machine Learning for Programmers: Create AI Models and Evolve Solutions

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ISBN: 168050620X | 2019 | True PDF | 236 pages | 14 MB Self-driving cars, natural language recognition, and online recommendation engines are all possible thanks to Machine Learning. Now you can create your own genetic algorithms, nature-inspired swarms, Monte Carlo simulations, cellular automata, and clusters. Learn how to test your ML code and dive

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

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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

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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

Data Science Algorithms in a Week: Top 7 algorithms for computing, data analysis, and machine learning

Data Science Algorithms in a Week: Top 7 algorithms for computing, data analysis, and machine learning

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English | September 20th, 2017 | ISBN: 1787284581 | 210 pages | EPUB |2.09 MB Machine learning applications are highly automated and self-modifying, and they continue to improve over time with minimal human intervention as they learn with more data. To address the complex nature of various real-world data problems, specialized machine learning algorithms have

[Udemy] – Machine Learning with Javascript

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Course Preview What you’ll learn Assemble machine learning algorithms from scratch! Build interesting applications using Javascript and ML techniques Understand how ML works without relying on mysterious libraries Optimize your algorithms with advanced performance and memory usage profiling Use the low-level features of Tensorflow JS to supercharge your algorithms Grow a strong intuition of ML

[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] – Projects in Machine Learning : Beginner To Professional

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Course Preview What you’ll learn Learn core concepts of Machine Learning Learn about differnt types of machine learning algorithms Build real world projects using Supervised and Unsupervised learning algorithms Requirements Basic knolwedge of Python is required to compile and run the examples Basic knolwedge of mathematics is assumed  Description Update: This course has been updated

[Udemy] – Machine Learning – TensorFlow JavaScript

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Learn Machine Learning from theory to the required hands-on experience by using latest TensorFlow.js framework Created by Vinay Phadnis Last updated 10/2018 English What you’ll learn Understand Machine Learning from basics Understand the basics of a neural network Understand basics of Tensor operations for Tensorflow Create project using Polynomial Regression Create basic HTML, Javascript projects Use

[Ebook] – Machine Learning with Python Cookbook By Chris Albon

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Practical Solutions from Preprocessing to Deep Learning By Chris Albon Publisher: O’Reilly Media Release Date: March 2018 Pages: 366 This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If you’re comfortable with Python and its libraries, including pandas and scikit-learn Machine Learning