Chapter 1: Where to Start Your Deep LearningChapter Goal: Learn about what tools are available for deep learning and computer vision tasks. Learn about what consideration the reader needs to make about the tools, OS, and hardware.
No of pages: 20
Sub - Topics
1. Can We Build Deep Learning Models on Windows?
2. Programming Language - Python
3. Package and Environment Management - Anaconda
4. Python Utility Libraries for Deep Learning and Computer Vision
5. Deep Learning Frameworks
6. Computer Vision Libraries
7. Optimizers and Accelerators
8. What About Hardware?
9. Recommended PC Hardware Configurations
Chapter 2: Setting Up Your Tools Chapter Goal: Step-by-step instructions on how to install, configure and troubleshoot the required tools.
No of pages: 35
Sub - Topics: 1. Installing Visual Studio with C++ Support
2. Installing CMake 3. Installing Anaconda Python
4. Setting up the Conda Environment and the Python Libraries 5. Installing TensorFlow
6. Installing Keras multi-backend version 7. Installing OpenCV
8. Installing Dlib 9. Verify Installations
10. Optional Steps 11. Troubleshooting
12. Summary
Chapter 3: Building Your First Deep Learning Model In Windows Chapter Goal: A step-by-step coding guide to building the first 'hello world' convolutional neural network image classification model.
No of pages: 20
Sub - Topics:
1. What is the MNIST Dataset? 2. The LeNet Model
3. Let us Build Our First Model 4. Running Our Model
5. What Can You Do Next?
Chapter 4: Understanding What We Built Chapter Goal: Learn the internal workings of a convolutional neural network.
No of pages: 20
Sub - Topics:
1. Digital Images 2. Convolutions
3. Non-Linearity Function 4. Pooling
5. Classifier (Fully Connected Layer) 6. How Does This All Come Together?
Chapter 5: Visualizing Models Chapter Goal: Understand ways to visualize the internal workings of deep learning models, allowing the reader to use that knowledge to build complex models.
No of pages: 20
Sub - Topics:
1. Why Visualizing Models is Useful
2. Using the plot_model Function of Keras
3. Using Netron to Visualize Model Structures
4. Visualizing Convolutional Filters
Chapter 6: Transfer Learning
Chapter Goal: Building deep learning systems that solves a practical problem is usually made hard due to the difficulty of collecting and managing training data. It is usually al
About the Author:
Thimira Amaratunga is an Inventor, a Senior Software Architect at Pearson PLC Sri Lanka with over 12 years of industry experience, and a researcher in AI, Machine Learning, and Deep Learning in Education and Computer Vision domains.
Thimira holds a Master of Science in Computer Science with a Bachelor's degree in Information Technology from the University of Colombo, Sri Lanka. He has filed three patents to date, in the fields of dynamic neural networks and semantics for online learning platforms. Before this, Thimira has published two books on deep learning - 'Build Deeper: The Deep Learning Beginners' Guide' and 'Build Deeper: The Path to Deep Learning'.
Thimira is also the author of Codes of Interest (www.codesofinterest.com), a portal for deep learning and computer vision knowledge, covering everything from concepts to step-by-step tutorials.
LinkedIn: www.linkedin.com/in/thimira-amaratunga
