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This is an excellent machine learning primer where you scroll the page and the author(s) walk you through the process of creating a model to distinguish homes in New York from homes in San Francisco. Get the latest news and podcasts for developers in your inbox, every week. We make it super easy to keep up with developer news that matters.

"This is an ideal introduction for people who have limited time but still want to go beyond trivial, hand-waving explanations about the core concepts in deep learning. The book's focus is illustrations with a minimal amount of text. The illustrations are clear, crisp, and accurate. Moreover, they perfectly balance the text. Many books are too verbose. Some are too terse. Here, Meor strikes the perfect balance -- enough text to explain the little the illustrations don't. The book is like a CEO summary of deep learning and serves as a good starting point for people who want an overview before diving in or who simply want an overview to see what the fuss is all about."— Ronald T. Kneusel, Ph.D. (author of Practical Deep Learning: A Python-Based Introduction and Math for Deep Learning)"I am always on the lookout for effective ways to summarize concepts visually. This book takes an impressive no frills approach for people who want to learn about the underpinnings of neural networks in the most time-effective way possible."— Sebastian Raschka, Ph.D. (author of Python Machine Learning)Deep learning is the algorithm powering the current renaissance of Artificial Intelligence (AI). And the progress is not showing signs of slowing down. A McKinsey report estimates that by 2030, AI will potentially deliver $13 trillion to the global economy, or 16% of the world's current GDP. This opens up exciting career opportunities in the coming decade.But deep learning can be quite daunting to learn. With the abundance of learning resources in recent years has emerged another problem—information overload.This book aims to compress this knowledge and make the subject approachable.By the end of this book, you will be able to build a visual intuition about deep learning and neural networks. Who is this for:If you are just beginning your journey in deep learning, or machine learning in general.If you have already got started with deep learning but want to gain further intuition.If you are a leader looking to understand deep learning and AI from first principles. The book's contents are designed to help you navigate the various concepts with as little friction as possible:Each of the 235 pages is visual-led and supported by concise text.The math is kept to a minimum.The same dataset is used in all chapters so you have the same, consistent reference.The dataset is small and simple so you can 'touch and feel' it and grasp the dynamics more easily. What the Book CoversThe motivation behind deep learning and machine learning in general.Deep dive into a feedforward neural network via four tasks - linear regression, nonlinear regression, binary classification, multiclass classification. These will be demonstrated using tabular data.A quick tour of the different variants of a neural network - convolutional, recurrent and generative - and the different types of data - images, text, etc.Content Overview Table of Contents What the Book Doesn't CoverMathematical derivationsCode examplesFurther topics such as optimizers, regularization, embeddings, etc. DetailsLength: 235 pagesAuthor: Meor Amer About the AuthorMy journey into AI began in 2010 after my son was born with a limb difference. I became interested in machine learning in prosthetics and did an MSc at Imperial College London majoring in neurotechnology.I have also worked in the telecoms data analytics space, where I did solution engineering for clients in over 15 countries.Above all, I am passionate about education and how we learn. I am currently working on projects that explore ways to create alternative learning experiences using visuals, storytelling, and games.Connect with me on LinkedIn Refund PolicyThere is a 30-day refund policy. And to compensate for your time, you get to keep the book even after the refund. For any queries, send your email to contact@kdimensions.com.Reader ReviewsOne of our most advanced senses is sight. Our eyes alert us to danger, lead us to sustenance, and allow us to enjoy stories. Meor Amer is a master storyteller. In A Visual Introduction to Deep Learning, Meor is our tour guide for a journey of discovery in this amazing field of Artificial Intelligence. His hand-crafted minimalist graphics are accompanied by succinct descriptions where he illuminates the subtle hints in each picture. I enthusiastically recommend this learning resource for AI enthusiasts. — Jack CrawfordThis is an amazing visual illustration book on deep learning. It bridges the gap between textual reading and contextual thinking. You can see what you learn. It's like "things coming to life!".— Raj ArunYou have made it really simple.— Sanjay MahanaYou really did a great job in explaining the concepts and reflecting them visually.— Alia HamwiVery clear non-technical explanations of deep learning. As AI becomes more prevalent in many businesses, it’s important that leaders understand the first principles.— Emily Ryder MartinsYou can’t miss anymore the basics of this. Love this book. The visuals help a lot. Meor Amer has produced, for me, the unique foundation overview. — Francisco TosteAre you a visual learner and want to build an intuition about deep learning? Here is a good, very easy-to-read book.— Andrew YaroshevskyI have been looking for this type of formatted approach. A no-risk investment with huge rewards!— Louis Girardin

Using NLP and sentiment analysis dictionaries, different features are computed. NLP and sentiment analysis is a must for the visual introduction of machine learning. A brief feature engineering is performed to get realistic results. Out of all computed features, the most outperformed features are selected for the Machine Learning model. The outperformed features are computed using various techniques that include information gain, gain ratio, and correlation score.

This is not an isolated incident. For example, we could grow additional trees from random subsets of the training data. The resulting trees all look reasonably different and also have single-home leaf nodes. These seemingly-esoteric homes that may result in single-data-point leaf nodes are actually a normal part of any data set. They are an outcome of the method for fitting the model.

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In machine learning, computers apply statistical learning techniques to automatically identify patterns in data. Numerical Optimization is one of the central techniques in... "Everything Should Be Made as Simple as Possible, But Not Simpler" - Albert Einstein Sign up for our newsletter to stay updated. "Everything Should Be Made as Simple as Possible, But Not Simpler" - Albert Einstein

In machine learning, these statements are called forks, and they split the data into two branches based on some value. That value between the branches is called a split point. Homes to the left of that point get categorized in one way, while those to the right are categorized in another. A split point is the decision tree's version of a boundary. Picking a split point has tradeoffs. Our initial split ( 73 m) incorrectly classifies some San Francisco homes as New York ones.

You can visualize your elevation ( 242 ft) and price per square foot ( $1776) observations as the boundaries of regions in your scatterplot. Homes plotted in the green and blue regions would be in San Francisco and New York, respectively. Identifying boundaries in data using math is the essence of statistical learning. Of course, you'll need additional information to distinguish homes with lower elevations and lower per-square-foot prices. The dataset we are using to create the model has 7 different dimensions.

From Google search results to Netflix recommendations and investment strategies, Bayes Theorem (also often called Bayes Rule or Bayes Formula) is used across countless industries to help calculate and assess probability. Bayesian statistics is taught in most first-year statistics classes across the nation, but there is one major problem that many students (and others who are interested in the theorem) face. The theorem is not intuitive for most people, and understanding how it works can be a challenge, especially because it is often taught without visual aids. In this guide, we unpack the various components of the theorem and provide a basic overview of how it works – and with illustrations to help. Three scenarios – the flu, breathalyzer tests, and peacekeeping – are used throughout the booklet to teach how problems involving Bayes Theorem can be approached and solved.