Machine Learning Math
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Machine Learning Math All You Need to Know Immediately About Math If You Want Spark In Deep Learning, Artificial Intelligent and Machine Learning
★ 55% OFF for Bookstores! NOW at $36.95 instead of $49.95★ You find out about machine learning form A to Z even if you are a beginner Do you want to spark in the science of XXI century? Do you want to become a recreational scientist in deep learning? If you answer yes to one of these previous questions, then keep reading till the end. Machine learning is an advanced form of data analysis and computation which uses the exceptional processing speed and pattern recognition techniques of computers to find and learn new trends in data. Putting it, it is an artificial-intelligence-inspired technique of programming that allows computers to improve their learning capabilities through the data they are fed, or they can access. The concept behind the technique is consistently to improve and to test, and it will be the key in the bigger technological revolution for the future. It is important for any current or aspiring data scientist to join the growing machine learning community, and contribute a quota to improve technology. This guide will focus on the following items: - Induction and Deduction - Decision Trees - Types of Artificial Intelligence and Machine Learning - Stacked Denoising Autoencoders - Robotics - Reinforcement Learning - Linear Algebra - How Companies Use Big Data to Increase Sales - What Is Supervised Machine Learning - How To Build A Predictive Model - Data Preprocessing with Machine Learning - Machine Learning and Robotics - How AI Is Revolutionizing Industry... AND MORE!!! What are you waiting for? A lot of people think that studying ML and Mathematics is difficult. It's because there are a lot of people that don't know the topic in depth so they can't explain it in easy ways. In this book the items will be described in such an easy way you will be surprised! Buy now if you want to spark in deep learning and know whatever it takes about ML and Math
Mathematics for Machine Learning
Author: Marc Peter Deisenroth
language: en
Publisher: Cambridge University Press
Release Date: 2020-04-23
Distills key concepts from linear algebra, geometry, matrices, calculus, optimization, probability and statistics that are used in machine learning.
Machine Learning Math
Are you looking for a complete guide of machine learning? Then keep reading... In this book, you will learn about the OpenAI Gym, used in reinforcement learning projects with several examples of the training platform provided out of the box. Machine Learning Math is the book most readers will want to have when starting to learn machine learning. This book is a reference, something you can keep coming back to hence suitable for newbies. The book is perfect for all people who have a desire to study data science. Have you heard of machine learning being everywhere, and you intend to understand what it can do? Or are you familiar with applying the tools of machine learning, but you want to make sure you aren't missing any? Having a little knowledge about mathematics, statistics, and probability would be helpful, but this book has been written in such a way that you will get most of this knowledge as you continue reading. You should not shy away from reading the book if you have no background in machine learning. You will learn how to use reinforcement learning algorithms in other tasks, for example, the board game Go, and generating deep image classifiers. This will help you to get a comprehensive understanding of reinforcement learning and help you solve real-world problems. The most interesting part of this book is the asynchronous reinforcement learning framework. You will learn what the shortcomings of DQN are, and why DQN is challenging to apply in complex tasks. Then, you will learn how to apply the asynchronous reinforcement learning framework in the actor-critic method REINFORCE, which led us to the A3C algorithm. You will learn four important things. The first one is how to implement games using gym and how to play games for relaxation and having fun. The second one is that you will learn how to preprocess data in reinforcement learning tasks such as in computer games. For practical machine learning applications, you will spend a great deal of time understanding and refining data, which affects the performance of an AI system a lot. The third one is the deep Q-learning algorithm. You will learn the intuition behind it, for example, why the replay memory is necessary, why the target network is needed, where the update rule comes from, and so on. The final one is that you will learn how to implement DQN using TensorFlow and how to visualize the training process. The following is a glimpse of what you will find inside the book: Introduction to machine learning The best machine learning algorithms Regression (a problem of predicting a real-valued label) and classification( a problem of automatically assigning a label to unlabeled example-for example spam detection) Reinforcement learning Robotics Supervised and Unsupervised learning How to implement a convolutional neural network(usually used for images) in TensorFlow Deep Learning Data preparation and processing TensorFlow machine learning frameworks Neural Networks (a combination of linear and non-linear functions) Clustering(aims to group similar samples together) Even if you have never studied Machine Learning before, you can learn it quickly. So what are you waiting for? Go to the top of the page and click Buy Now!