Practical AI on the Google cloud platform : Utilizing Google's state-of-the-art AI cloud services (Record no. 297473)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 05052nam a22001337a 4500 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9789385889455 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.3 |
| Item number | LAN-P |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Lanham, Micheal |
| 245 ## - TITLE STATEMENT | |
| Title | Practical AI on the Google cloud platform : Utilizing Google's state-of-the-art AI cloud services |
| Statement of responsibility, etc. | By Micheal Lanham. |
| 500 ## - GENERAL NOTE | |
| General note | Working with AI is complicated and expensive for many developers. That's why cloud providers have stepped in to make it easier, offering free (or affordable) state-of-the-art models and training tools to get you started. With this book, you'll learn how to use Google's AI-powered cloud services to do everything from creating a chatbot to analyzing text, images, and video. Author Micheal Lanham demonstrates methods for building and training models step-by-step and shows you how to expand your models to accomplish increasingly complex tasks. If you have a good grasp of math and the Python language, you'll quicly get up to speed with Google cloud platform, whether you want to build an AI assistant or a simple business AI application. |
| 505 ## - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Chapter 1. Data Science and Deep Learning --<br/>What is Data Science? --<br/>Classification and Regression --<br/>Regression --<br/>Goodness of Fit --<br/>Classification with Logistic Regression --<br/>Multi-variant Regression and Classification --<br/>Data Discovery and Preparation --<br/>Bad Data Training, Test and Validation Data --<br/>Good Data --<br/>Preparing Data --<br/>Questioning Your Data --<br/>The Basics of Deep Learning --<br/>The Perceptron Game --<br/>Understanding How Networks Learn --<br/>Backpropagation --<br/>Optimization and Gradient Descent --<br/>Vanishing or Exploding Gradients --<br/>SGD and Batching Samples --<br/>Batch Normalization and Regularization --<br/>Activation Functions --<br/>Loss Functions --<br/>Building a Deep Learner --<br/>Overfitting and Underfitting --<br/>Network Capacity --<br/>Conclusion --<br/>Game Answers --<br/><br/>Chapter 2. AI on the Google Cloud Platform --<br/>AI Services on GCP --<br/>The AI Hub --<br/>AI Platform AI Building Blocks --<br/>Google Colab Notebooks --<br/>Building a Regression Model with Colab --<br/>AutoML Tables --<br/>The Cloud Shell --<br/>Managing Cloud Data --<br/>Conclusion --<br/><br/>Chapter 3. Image Analysis and Recognition on the Cloud --<br/>Deep Learning with Images --<br/>Enter Convolution Neural Networks --<br/>Image Classification --<br/>Setup and Load Data --<br/>Inspecting Image Data --<br/>Channels and CNN --<br/>Building the Model --<br/>Training the AI Fashionista to Discern Fashions --<br/>Improving Fashionista AI 2.0 --<br/>Transfer Learning Images --<br/>Identifying Cats or Dogs --<br/>Transfer Learning a Keras Application Model Training Transfer Learning --<br/>ReTraining a Better Base Model --<br/>Object Detection and the Object Detection Hub API --<br/>YOLO for Object Detection --<br/>Generating Images with GANs --<br/>Conclusion --<br/><br/>Chapter 4. Understanding Language on the Cloud --<br/>Natural Language Processing, with Embeddings --<br/>Recurrent Networks for NLP --<br/>Recurrent Networks for Memory --<br/>RNN Variations --<br/>Neural Translation and the Translation API --<br/>Sequence to Sequence Learning --<br/>Translation API --<br/>AutoML Translation --<br/>Natural Language API --<br/>BERT Bidirectional Encoder Representations from Transformers Semantic Analysis with BERT --<br/>Document Matching with BERT --<br/>BERT for General Text Analysis --<br/>Conclusion --<br/><br/>Chapter 5. Chatbots and Conversational AI --<br/>Building Chatbots with Python --<br/>Developing Goal Oriented Chatbots with DialogFlow --<br/>Building Text Transformers --<br/>Loading and Preparing Data --<br/>Understanding Attention --<br/>Masking and the Transformer --<br/>Encoding and Decoding the Sequence --<br/>Training Conversational Chatbots --<br/>Compiling and Training the Model --<br/>Evaluation and Prediction --<br/>Using Transformer for Conversational Chatbots --<br/>Conclusion --<br/><br/>Chapter 6. Video Analysis on the Cloud<br/>chapter 7: Generators in the cloud --<br/>Chater 8: Building AI assistants in the cloud --<br/>Chapter 9: Putting AI assistants to work --<br/>Chapter 10: Commercializing AI. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | AI is complicated, but cloud providers have stepped in to make it easier, offering free (or affordable) state-of-the-art models and training tools to get you started. In this book, AI novices will learn how to use Google's AI-powered cloud services to do everything from analyzing text, images, and video to creating a chatbot. Author Micheal Lanham takes you step-by-step through building models, training them, and then expanding on them to accomplish increasingly complex tasks. If you have a good grasp of math and the Python language, this book will get you up and running with Google Cloud Platform, whether you're looking to build a simple business AI application or an AI assistant. Learn key concepts for data science, machine learning, and deep learning Explore tools like Video AI, AutoML Tables, the Cloud Inference API, the Recommendations AI API, and BigQuery ML Perform image recognition using CNNs, transfer learning, and GANs Build a simple language processor using embeddings, RNNs, and Bidirectional Encoder Representations from Transformers (BERT) Use Dialogflow to build a chatbot Analyze video with automatic video indexing, face detection, and TF Hub. |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Book |
| Withdrawn status | Lost status | Damaged status | Not for loan | Home library | Current library | Shelving location | Date acquired | Total Checkouts | Full call number | Barcode | Date last seen | Date last checked out | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dept. of Computational Biology and Bioinformatics | Dept. of Computational Biology and Bioinformatics | Processing Center | 28/10/2021 | 1 | 006.3 LAN-P | DCB3990 | 31/03/2023 | 28/10/2022 | 28/10/2021 | Book |