Add figures for chapter 17

Author: meherkasam

README.md

@@ -96,7 +96,7 @@ Guest author Sam Sterckval brings deep learning to low-power devices as he showc
 
 Using the photorealistic simulation environment of Microsoft AirSim, guest authors **Aditya Sharma** and **Mitchell Spryn** guide us in training a virtual car by driving it first within the environment and then teaching an AI model to replicate its behavior. Along the way, this chapter covers a number of concepts that are applicable in the autonomous car industry.
 
-[**Chapter 17 - Building an Autonomous Car in Under an Hour: Reinforcement Learning with AWS DeepRacer**](https://github.com/practicaldl/Practical-Deep-Learning-Book/tree/master/code/chapter-17) | [Read online](https://learning.oreilly.com/library/view/practical-deep-learning/9781492034858/ch17.html)
+[**Chapter 17 - Building an Autonomous Car in Under an Hour: Reinforcement Learning with AWS DeepRacer**](https://github.com/practicaldl/Practical-Deep-Learning-Book/tree/master/code/chapter-17) | [Read online](https://learning.oreilly.com/library/view/practical-deep-learning/9781492034858/ch17.html) | [Figures](figures/chapter-17)
 
 Moving from the virtual to the physical world, guest author **Sunil Mallya** showcases how AWS DeepRacer, a miniature car, can be assembled, trained and raced in under an hour. And with the help of reinforcement learning, the car learns to drive on its own, penalizing mistakes and maximizing success. We learn how to apply this knowledge to races from the Olympics of AI Driving to RoboRace (using full-sized autonomous cars). Bonus: Hear from **Anima Anandkumar (NVIDIA)** and **Chris Anderson (founder of DIY Robocars)** on where the self-driving automotive industry is headed.
 

figures/README.md

@@ -24,4 +24,4 @@ This directory contains references to the figures used within the book. All imag
 | Chapter 14 - Building the Purrfect Cat Locator App with TensorFlow Object Detection API |
 | [Chapter 15 - Becoming a Maker: Exploring Embedded AI at the Edge](chapter-15/) |
 | Chapter 16 - Simulating a Self-Driving Car using End-to-End Deep Learning with Keras |
-| Chapter 17 - Building an Autonomous Car in Under an Hour: Reinforcement Learning with AWS DeepRacer |
+| [Chapter 17 - Building an Autonomous Car in Under an Hour: Reinforcement Learning with AWS DeepRacer](chapter-17/) |

figures/chapter-15/README.md

@@ -4,19 +4,19 @@ Note: All images in this directory, unless specified otherwise, are licensed und
 
 ## Figure List
 
-| Figure number | Description | Notes |
-|:---|:---|:---|
-| [15-1](1-family-photo.jpg) | Family photo of Embedded AI devices; starting at the top, going clockwise: PYNQ-Z2, Arduino UNO R3, Intel Movidius NCS2, Raspberry Pi 4, Google Coral USB Accelerator, NVIDIA Jetson Nano, and a €1 coin for reference in the middle | |
-| [15-2](2-pi4.jpg) | Raspberry Pi 4 | |
-| [15-3](3-ncs2.jpg) | Intel Neural Compute Stick 2 | |
-| [15-4](4-coral.jpg) | Google Coral USB Accelerator | |
-| [15-5](5-jetson-nano.jpg) | NVIDIA Jetson Nano | |
-| [15-6](6-pynq-z2.jpg) | Xilinx PYNQ-Z2 | |
-| [15-7](7-matrix-multiplication.png) | Matrix multiplication | |
-| [15-8](8-arduino-uno.jpg) | Arduino UNO R3 | |
-| [15-9](9-perf-complex-graph.png) | Performance versus complexity-to-use (the size of the circle represents price) | |
-| [15-10](10-cat.jpg) | The image of the cat we will be using for our experiments | |
-| [15-11](11-detect-pi4.png) | Google Coral install script changes | |
-| [15-12](https://www.practicaldeeplearning.ai/uploads/1/2/6/2/126206606/12-jetbot-181_orig.jpg) | NVIDIA JetBot | |
-| [15-13](13-ticket-squat.png) | Squatting for tickets | |
-| [15-14](https://cloud.google.com/blog/products/gcp/how-a-japanese-cucumber-farmer-is-using-deep-learning-and-tensorflow) | Makoto Koike's cucumber sorting machine | |
+| Figure number | Description |
+|:---|:---|
+| [15-1](1-family-photo.jpg) | Family photo of Embedded AI devices; starting at the top, going clockwise: PYNQ-Z2, Arduino UNO R3, Intel Movidius NCS2, Raspberry Pi 4, Google Coral USB Accelerator, NVIDIA JetsoNano, and a €1 coin for reference in the middle |
+| [15-2](2-pi4.jpg) | Raspberry Pi 4 |
+| [15-3](3-ncs2.jpg) | Intel Neural Compute Stick 2 |
+| [15-4](4-coral.jpg) | Google Coral USB Accelerator |
+| [15-5](5-jetson-nano.jpg) | NVIDIA Jetson Nano |
+| [15-6](6-pynq-z2.jpg) | Xilinx PYNQ-Z2 |
+| [15-7](7-matrix-multiplication.png) | Matrix multiplication |
+| [15-8](8-arduino-uno.jpg) | Arduino UNO R3 |
+| [15-9](9-perf-complex-graph.png) | Performance versus complexity-to-use (the size of the circle represents price) |
+| [15-10](10-cat.jpg) | The image of the cat we will be using for our experiments |
+| [15-11](11-detect-pi4.png) | Google Coral install script changes |
+| [15-12](https://www.practicaldeeplearning.ai/uploads/1/2/6/2/126206606/12-jetbot-181_orig.jpg) | NVIDIA JetBot |
+| [15-13](13-ticket-squat.png) | Squatting for tickets |
+| [15-14](https://cloud.google.com/blog/products/gcp/how-a-japanese-cucumber-farmer-is-using-deep-learning-and-tensorflow) | Makoto Koike's cucumber sorting machine |