Process, Build, Deploy, and Productionize Your Models Using AWS
260 pages
10 hours of reading
The book provides a comprehensive guide on effectively building, tuning, deploying, and scaling machine learning models. It emphasizes the automation of the entire process, covering essential aspects from data processing to deployment. Readers will gain insights into best practices and techniques for ensuring models are production-ready, making it a valuable resource for anyone looking to enhance their machine learning capabilities.
Chapter 1: Installation and Environment Setup Chapter Goal: Making System
Ready for Image Processing and Analysis No of pages 20 Sub -Topics (Top 2) 1.
Installing Jupyter Notebook 2. Installing OpenCV and other Image Analysis
dependencies 3. Installing Neural Network Dependencies Chapter 2: Introduction
to Image Processing Chapter Goal: Introduction to different concepts of Image
Processing No of pages: 50 Sub - Topics (Top 2) 1. Terminologies related to
Image Analysis 2. Flowchart of Image Processing Chapter 3: Python Operations
Chapter Goal: Introduction to Python as a Programming Language No of pages: 50
Sub - Topics (Top 2): 1. Essentials of Python 2. Advanced Python Chapter 4:
Image Processing and Analysis Algorithms Chapter Goal: Understanding
Algorithms and their applications in Python No of pages: 100 Sub - Topics (Top
2): 1. Operations on Images 2. Image Segmentation Chapter 5: OpenCV - An
Introduction Chapter Goal: Exploring the famous computer vision Library
OpenCV, and understanding its essential elements No of pages: 100 Sub - Topics
(Top 2): 1. Exploring OpenCV Algorithms 2. Applications on Images using Python
Chapter 6: Machine Learning & Image Processing Chapter Goal: Applying Machine
Learning Models on Processed Images. No of pages: 100 Sub - Topics (Top 2): 1.
Applying Supervised and Unsupervised Learning approaches on Images using
Python 2. Applying Advanced Artificial Neural Networks on Images Chapter 7:
Real Time Use Cases Chapter Goal: Working on 5 projects using Python, applying
all the concepts learned in this book No of pages: 100 Sub - Topics (Top 2):
Facial Recognition and Detection Self-Driving Cars Conceptualization Hand
Gesture Movement Recognition Attendance Management Retail Stacks Management
Gain insight into fuzzy logic and neural networks, and how the integration between the two models makes intelligent systems in the current world. This book simplifies the implementation of fuzzy logic and neural network concepts using Python. You’ll start by walking through the basics of fuzzy sets and relations, and how each member of the set has its own membership function values. You’ll also look at different architectures and models that have been developed, and how rules and reasoning have been defined to make the architectures possible. The book then provides a closer look at neural networks and related architectures, focusing on the various issues neural networks may encounter during training, and how different optimization methods can help you resolve them. In the last section of the book you’ll examine the integrations of fuzzy logics and neural networks, the adaptive neuro fuzzy Inference systems, and various approximations related to the same. You’ll review different types of deep neuro fuzzy classifiers, fuzzy neurons, and the adaptive learning capability of the neural networks. The book concludes by reviewing advanced neuro fuzzy models and applications. What You’ll Learn Who This book Is For Data scientists and software engineers with a basic understanding of Machine Learning who want to expand into the hybrid applications of deep learning and fuzzy logic.
