Advanced applied deep learning : convolutional neural networks and object detection /: convolutional neural networks and object detection. ([2019])

• Record Type: Book
• Further Information: Note: Umberto Michelucci.
• Authors: Michelucci, Umberto
• Contents: Intro; Table of Contents; About the Author; About the Technical Reviewer; Acknowledgments; Introduction; Chapter 1: Introduction and Development Environment Setup; GitHub Repository and Companion Website; Mathematical Level Required; Python Development Environment; Google Colab; Benefits and Drawbacks to Google Colab; Anaconda; Installing TensorFlow the Anaconda Way; Local Jupyter Notebooks; Benefits and Drawbacks to Anaconda; Docker Image; Benefits and Drawbacks to a Docker Image; Which Option Should You Choose?; Chapter 2: TensorFlow: Advanced Topics; Tensorflow Eager Execution Enabling Eager ExecutionPolynomial Fitting with Eager Execution; MNIST Classification with Eager Execution; TensorFlow and Numpy Compatibility; Hardware Acceleration; Checking the Availability of the GPU; Device Names; Explicit Device Placement; GPU Acceleration Demonstration: Matrix Multiplication; Effect of GPU Acceleration on the MNIST Example; Training Only Specific Layers; Training Only Specific Layers: An Example; Removing Layers; Keras Callback Functions; Custom Callback Class; Example of a Custom Callback Class; Save and Load Models; Save Your Weights Manually; Saving the Entire Model Dataset AbstractionIterating Over a Dataset; Simple Batching; Simple Batching with the MNIST Dataset; Using tf.data.Dataset in Eager Execution Mode; Conclusions; Chapter 3: Fundamentals of Convolutional Neural Networks; Kernels and Filters; Convolution; Examples of Convolution; Pooling; Padding; Building Blocks of a CNN; Convolutional Layers; Pooling Layers; Stacking Layers Together; Number of Weights in a CNN; Convolutional Layer; Pooling Layer; Dense Layer; Example of a CNN: MNIST Dataset; Visualization of CNN Learning; Brief Digression: keras.backend.function(); Effect of Kernels Effect of Max-PoolingChapter 4: Advanced CNNs and Transfer Learning; Convolution with Multiple Channels; History and Basics of Inception Networks; Inception Module: Naïve Version; Number of Parameters in the Naïve Inception Module; Inception Module with Dimension Reduction; Multiple Cost Functions: GoogLeNet; Example of Inception Modules in Keras; Digression: Custom Losses in Keras; How To Use Pre-Trained Networks; Transfer Learning: An Introduction; A Dog and Cat Problem; Classical Approach to Transfer Learning; Experimentation with Transfer Learning Chapter 5: Cost Functions and Style TransferComponents of a Neural Network Model; Training Seen as an Optimization Problem; A Concrete Example: Linear Regression; The Cost Function; Mathematical Notation; Typical Cost Functions; Mean Square Error; Intuitive Explanation; MSE as the Second Moment of a Moment-Generating Function; Cross-Entropy; Self-Information or Suprisal of an Event; Suprisal Associated with an Event X; Cross-Entropy; Cross-Entropy for Binary Classification; Cost Functions: A Final Word; Neural Style Transfer; The Mathematics Behind NST; An Example of Style Transfer in Keras
• Publisher Details: New York : Apress
• Publication Date: 2019
• Copyright Date: 2019
• Extent: 1 online resource, illustrations (some color)
• Subjects: 006.3/1; Machine learning; Neural networks (Computer science); Python (Computer program language); Electronic books; Electronic books
• Languages: English
• ISBNs: 9781484249765; 1484249763
• Related ISBNs: 9781484249758
• Notes: Note: Online resource; title from PDF title page (SpringerLink, viewed October 3, 2019).
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.