Introduction to Deep Learning for Engineers: Using Python and Google Cloud Platform
- Length: 110 pages
- Edition: 1
- Language: English
- Publisher: Morgan & Claypool Publishers
- Publication Date: 2020-07-22
- ISBN-10: 1681739135
- ISBN-13: 9781681739137
- Sales Rank: #4268897 (See Top 100 Books)
This book provides a short introduction and easy-to-follow implementation steps of deep learning using Google Cloud Platform. It also includes a practical case study that highlights the utilization of Python and related libraries for running a pre-trained deep learning model.
In recent years, deep learning-based modeling approaches have been used in a wide variety of engineering domains, such as autonomous cars, intelligent robotics, computer vision, natural language processing, and bioinformatics. Also, numerous real-world engineering applications utilize an existing pre-trained deep learning model that has already been developed and optimized for a related task. However, incorporating a deep learning model in a research project is quite challenging, especially for someone who doesn’t have related machine learning and cloud computing knowledge. Keeping that in mind, this book is intended to be a short introduction of deep learning basics through the example of a practical implementation case.
The audience of this short book is undergraduate engineering students who wish to explore deep learning models in their class project or senior design project without having a full journey through the machine learning theories. The case study part at the end also provides a cost-effective and step-by-step approach that can be replicated by others easily.
Preface Acknowledgments Introduction: Python and Array Operations Introduction Anaconda Installation Using Jupyter Notebook Array Computing Using NumPy Importing NumPy and Basic Commands Changing Array Elements Creating Array with Specific Values Broadcasting in NumPy Introduction to PyTorch Introduction Setting up PyTorch Basic PyTorch Operations Basic Artificial Neural Network and Architectures Introduction Applications Neurons and Activation Functions Sigmoid Activation Hyperbolic Tangent Activation Rectified Linear Units (ReLU) Activation Leaky Rectified Linear Units (Leaky ReLU) Activation Exponential Linear Units (ELU) Activation SoftPlus Activation Minimizing the Loss Function Gradient Descent Algorithm Introduction to Deep Learning Introduction Convolutional Neural Network Pooling Layer Fully Connected Layer Recurrent Neural Network (RNN) Basic Structures Other Deep Learning Models Deep Transfer Learning Introduction Types of Transfer Learning Using Pre-Trained Networks Feature Extraction, Fine Tuning, and Data Augmentation Model Evaluation Setting Up PyTorch and Google Cloud Platform Console Introduction Setting Up a GCP Account Create a New Project Set Up a VM instance GPU Quota Request A Cost-Effective Approach VPC Network Set Up External IP Address Create Firewall Rules Setting Up VM Instance to Run Models Anaconda Installation on VM Jupyter Notebook Set Up Case Study: Practical Implementation Through Transfer Learning Problem Statement Data Processing Upload Data into Storage Bucket Transferring File to VM Instance Transfer Learning Steps Define Loss Function and Optimizer Install Dependencies Import Libraries Checkpoint and Adaptive Learning Rate Set Seed Dataset Class and Augmentation Data Preprocessing Transfer Learning Model (EfficientNet-B7) Fine-Tuning and Training Model Testing Conclusion Bibliography Author's Biography Blank Page
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