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The Artificial Intelligence Infrastructure Workshop

The Artificial Intelligence Infrastructure Workshop

By : Chinmay Arankalle , Gareth Dwyer , Bas Geerdink , Kunal Gera , Kevin Liao , Anand N.S.
4.3 (3)
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The Artificial Intelligence Infrastructure Workshop

The Artificial Intelligence Infrastructure Workshop

4.3 (3)
By: Chinmay Arankalle , Gareth Dwyer , Bas Geerdink , Kunal Gera , Kevin Liao , Anand N.S.

Overview of this book

Social networking sites see an average of 350 million uploads daily - a quantity impossible for humans to scan and analyze. Only AI can do this job at the required speed, and to leverage an AI application at its full potential, you need an efficient and scalable data storage pipeline. The Artificial Intelligence Infrastructure Workshop will teach you how to build and manage one. The Artificial Intelligence Infrastructure Workshop begins taking you through some real-world applications of AI. You’ll explore the layers of a data lake and get to grips with security, scalability, and maintainability. With the help of hands-on exercises, you’ll learn how to define the requirements for AI applications in your organization. This AI book will show you how to select a database for your system and run common queries on databases such as MySQL, MongoDB, and Cassandra. You’ll also design your own AI trading system to get a feel of the pipeline-based architecture. As you learn to implement a deep Q-learning algorithm to play the CartPole game, you’ll gain hands-on experience with PyTorch. Finally, you’ll explore ways to run machine learning models in production as part of an AI application. By the end of the book, you’ll have learned how to build and deploy your own AI software at scale, using various tools, API frameworks, and serialization methods.
Table of Contents (14 chapters)
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Preface
4
4. The Ethics of AI Data Storage

Gradient Descent

Before we dig into what gradient descent is, let's answer the question of why gradient descent is one of the most important techniques in machine learning. For the sake of simplicity, let's use the linear regression model as an example to illustrate. Let's say we are building a model to predict house prices given the area of the house. The mathematical model is as follows: predicted house price = bias + weight*area of the house.

When we initialize the model, we need to initialize the values for the bias and the weight. Usually, we draw random numbers for the initialization of biases and weights. So, now the model becomes the following: predicted house price = 9 + 5*area of the house.

Bias = 9 and weight = 5 are not ideal model parameters to predict the house price. Currently, the model could be extremely wrong about its house price prediction. It is not even close to being accurate. At the same time, there might exist an ideal value for...

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