Artificial Intelligence is one field that is growing and increasing its boundaries with astonishing amounts of speed. With the globe always skewed towards more connectivity and the aggregation of petabytes of data, Computer Vision sets itself on a position that attracts a lot of opportunities. Be it in Agriculture, Industries, Education, or in other applicable areas, Computer vision cutting through every fabric that will usher in a new age of technological growth.
Borrowing the definition from IBM, Computer vision is a field of artificial intelligence (AI) that enables computers and systems to derive meaningful information from digital images, videos and other visual inputs — and take actions or make recommendations based on that information. If AI enables computers to think, computer vision enables them to see, observe and understand.
1. Deep Learning for Vision Systems
The author of this book, Mohamed Elgendy, is the head of engineering at Synapse Technology, a leading AI company that builds proprietary computer vision applications to detect threats at security checkpoints worldwide. He was previously an engineering manager at Amazon, where he developed and taught the deep learning for computer vision course at Amazon’s Machine Learning University. You can imagine his level of skills and experience that are shared in this resource he has personally penned.
Covering 480 pages, Deep Learning for Vision Systems answers how a computer learns to understand what it sees. Using only high school algebra, this book illuminates the concepts behind visual intuition. You will understand how to use deep learning architectures to build vision system applications for image generation and facial recognition.
What you will find inside
- Image classification and object detection
- Advanced deep learning architectures
- Transfer learning and generative adversarial networks
- DeepDream and neural style transfer
- Visual embeddings and image search
The author says that if you are an intermediate Python programmer or anyone with wild curiosity, this book will do you good. Shared below is one outstanding review:
“From text and object detection to DeepDream and facial recognition … this book is comprehensive, approachable, and relevant for modern applications of deep learning to computer vision systems!”
–Bojan Djurkovic, DigitalOcean
For ways through which you can find this resource, a link is provided below. Click it and you will land in Amazon where you can find more information including how to get it shipped to you.
2. Machine Learning Design Patterns
Computer Vision has deep roots in machine learning and it heavily depends on it. The three authors, Valliappa Lakshmanan, Sara Robinson, and Michael Munn share their hard-earned experience around the “why” that underlies the tips and tricks that experienced ML practitioners employ when applying machine learning to real-world problems.
In this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation.
You will learn how to
- Identify and mitigate common challenges when training, evaluating, and deploying ML models
- Represent data for different ML model types, including embeddings, feature crosses, and more
- Choose the right model type for specific problems
- Build a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuning
- Deploy scalable ML systems that you can retrain and update to reflect new data
- Interpret model predictions for stakeholders and ensure models are treating users fairly
The authors assume that you have prior knowledge of machine learning and data processing. They advice that the resource is not a fundamental textbook on machine learning. Instead, this book is for you if you are a data scientist, data engineer, or ML engineer who is looking for a second book on practical machine learning.
Here is the magic link that will link you up with Amazon where you can peruse more and order a copy for your warm library where beautiful things come to life. Click below.
3. Concise Computer Vision: An introduction in Theory and Algorithms
Dr. Reinhard Klette, Fellow of the Royal Society of New Zealand, author, is a Professor at the Auckland University of Technology (AUT). He has authored numerous publications including the book “Computer Vision for Driver Assistance” (co-authored by Mahdi Rezaei) among multiple others like this one.
Features you will find theren
- Provides an introduction to the basic notation and mathematical concepts for describing an image and the key concepts for mapping an image into an image.
- Explains the topologic and geometric basics for analysing image regions and distributions of image values and discusses identifying patterns in an image.