Full description not available
M**T
Get up to date in a hurry with deep learning and modern frameworks for development
Chapters:1. Exploring the landscape of artificial intelligence.2. What’s in the picture: image classification with Keras.3. Cats versus dogs: transfer learning in 30 lines with Keras.4. Building a reverse image search engine: understanding embeddings.5. From novice to master predictor: maximising convolutional neural network accuracy.6. Maximising speed and performance of TensoFlow: a handy checklist.7. Practical tools, tips and tricks.8. Cloud APIs for computer vision: up and running in 15 minutes.9. Scalable inference serving on cloud with TensorFlow serving and KubeFlow.10. AI in the browser with TensorFlow.js and ml5.js11. Real-time object classification on iOS with CoreML and CreateML.13. Shazam for food: developing Android apps with TensorFlow lite and MLKit.14. Building the purrfect cat locator app eith TensoFlow object detection API.15. Becoming a maker: exploring embedded AI at the edge.16. Simulating a self-driving car using end-to-end deep learning with Keras.17. Building an autonomous car in under an hour: reinforcement learning with AWS DeepRacer.Appendix:A crash course in convolutional neural networks.This is a large book covering a wide range of both well developed and emergent forms of machine learning approaches to a wide range of applications. Its a big ask tackled well by the three authors and various additional contributors, all of whom are vastly experienced and respected in their fields of interest. The book offers a fascinating and engaging read for those who are seeking rather more than a basic introduction to machine learning and applied data science whether from the point of view of a lay person, or like the reviewer, someone wishes to gain more than an appreciation of the field – an exposure to the actual nuts and bolts. This book succeeds from both approaches. For the first a wideranging account of existing and emerging techniques and tools available for various deep learning frameworks. For the second a closely integrated set of Jupyter Notebooks is made freely available. These notebooks, offer anyone with access to a web browser, the ability to carry out many of the examples written in Python with the libraries of the deep learning frameworks – such as TensorFlow and Keras. In a simple and straightforward way readers are enabled to carry out their own experimentation with the data made accessible by the authors. In other words the reader is able to DO machine learning, not just read about it!The authors have provided a good service to the public. The book is an authoritative introduction to the constituent fields of machine learning and provides an online experience for a number of recognisable groups: the software developer who might seek to develop skills applicable in the field of AI; the data scientist to enrich their skill set and deepen knowledge of the field in order to build real projects; the student to assist in the aspiration to a career in AI through developing a portfolio of interesting projects and to help unleash creativity; the teacher since this book can supplement course work with fun real-world projects. Each of the projects presented in this book can make for great collaborative or individual work in the classroom; the robotics enthusias for here is a survey of actual projects using emerging hardware such as Raspberry Pi, NVIDIA Jetson Nano, Google Coral and others.
B**Y
Must-have for Data scientist and ML engineers !
Where is the 2nd edition of this book?
G**S
super
interesting reading, bridges the gap between implementation and ideas
Trustpilot
4 days ago
1 week ago
