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According to McKinsey, AI will create an estimated $13 trillion of GDP growth between now and 2030. As a comparison, the GDP of the entire United States of America was around 19 trillion in 2017. Leading AI scientists, like Andrew Ng, describe AI as the fourth industrial revolution or „the new electricity“. AI is undoubtedly a centerpiece of digital transformation and its application throughout the industry will dramatically change our world and how we do business. The problem is that many people want to participate in this AI-revolution but they are overwhelmed by its technological sophistication. They don’t know what…


This moment has been a long time coming. The technology behind speech recognition has been in development for over half a century, going through several periods of intense promise — and disappointment. So what changed to make ASR viable in commercial applications? And what exactly could these systems accomplish, long before any of us had heard of Siri?

The story of speech recognition is as much about the application of different approaches as the development of raw technology, though the two are inextricably linked. …


“Technical Strategy for AI Engineers, In the Era of Deep Learning”

Machine Learning Yearning is about structuring the development of machine learning projects. The book contains practical insights that are difficult to find somewhere else, in a format that is easy to share with teammates and collaborators. Most technical AI courses will explain to you how the different ML algorithms work under the hood, but this book teaches you how to actually use them. If you aspire to be a technical leader in AI, this book will help you on your way. Historically, the only way to learn how to make strategic decisions about AI projects was to participate in a…


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In this blog-post, I will go through the whole process of creating a machine learning model on the famous Titanic dataset, which is used by many people all over the world. It provides information on the fate of passengers on the Titanic, summarized according to economic status (class), sex, age and survival.

I initially wrote this post on kaggle.com, as part of the “Titanic: Machine Learning from Disaster” Competition. In this challenge, we are asked to predict whether a passenger on the titanic would have been survived or not.

RMS Titanic

The RMS Titanic was a British passenger liner that sank in…


The concepts of Linear Algebra are crucial for understanding the theory behind Machine Learning, especially for Deep Learning. They give you better intuition for how algorithms really work under the hood, which enables you to make better decisions. So if you really want to be a professional in this field, you cannot escape mastering some of its concepts. This post will give you an introduction to the most important concepts of Linear Algebra that are used in Machine Learning.

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Table of Contents:

  • Introduction
  • Mathematical Objects
  • Computational Rules
  • Matrix Multiplication Properties
  • Inverse and Transpose
  • Summary
  • Resources

Introduction

Linear Algebra is a continuous form of mathematics…


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Data Types are an important concept of statistics, which needs to be understood, to correctly apply statistical measurements to your data and therefore to correctly conclude certain assumptions about it. This blog post will introduce you to the different data types you need to know, to do proper exploratory data analysis (EDA), which is one of the most underestimated parts of a machine learning project.


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Descriptive Statistical Analysis helps you to understand your data and is a very important part of Machine Learning. This is due to Machine Learning being all about making predictions. On the other hand, statistics is all about drawing conclusions from data, which is a necessary initial step. In this post you will learn about the most important descriptive statistical concepts. They will help you understand better what your data is trying to tell you, which will result in an overall better machine learning model and understanding.

Table of Contents:

  • Introduction
  • Normal Distribution
  • Central Tendency (mean, mode, median)
  • Measures of Variability…

Niklas Donges

Co-Founder: markov-solutions.com → AI-powered Software Solutions | linkedin.com/in/niklas-donges | machinelearning-blog.com

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