Practical AI on the Google cloud platform : Utilizing Google's state-of-the-art AI cloud services By Micheal Lanham.

By:

Lanham, Micheal

Material type: Text

TextISBN:

9789385889455

DDC classification:

006.3 LAN-P

Contents:

Chapter 1. Data Science and Deep Learning -- What is Data Science? -- Classification and Regression -- Regression -- Goodness of Fit -- Classification with Logistic Regression -- Multi-variant Regression and Classification -- Data Discovery and Preparation -- Bad Data Training, Test and Validation Data -- Good Data -- Preparing Data -- Questioning Your Data -- The Basics of Deep Learning -- The Perceptron Game -- Understanding How Networks Learn -- Backpropagation -- Optimization and Gradient Descent -- Vanishing or Exploding Gradients -- SGD and Batching Samples -- Batch Normalization and Regularization -- Activation Functions -- Loss Functions -- Building a Deep Learner -- Overfitting and Underfitting -- Network Capacity -- Conclusion -- Game Answers -- Chapter 2. AI on the Google Cloud Platform -- AI Services on GCP -- The AI Hub -- AI Platform AI Building Blocks -- Google Colab Notebooks -- Building a Regression Model with Colab -- AutoML Tables -- The Cloud Shell -- Managing Cloud Data -- Conclusion -- Chapter 3. Image Analysis and Recognition on the Cloud -- Deep Learning with Images -- Enter Convolution Neural Networks -- Image Classification -- Setup and Load Data -- Inspecting Image Data -- Channels and CNN -- Building the Model -- Training the AI Fashionista to Discern Fashions -- Improving Fashionista AI 2.0 -- Transfer Learning Images -- Identifying Cats or Dogs -- Transfer Learning a Keras Application Model Training Transfer Learning -- ReTraining a Better Base Model -- Object Detection and the Object Detection Hub API -- YOLO for Object Detection -- Generating Images with GANs -- Conclusion -- Chapter 4. Understanding Language on the Cloud -- Natural Language Processing, with Embeddings -- Recurrent Networks for NLP -- Recurrent Networks for Memory -- RNN Variations -- Neural Translation and the Translation API -- Sequence to Sequence Learning -- Translation API -- AutoML Translation -- Natural Language API -- BERT Bidirectional Encoder Representations from Transformers Semantic Analysis with BERT -- Document Matching with BERT -- BERT for General Text Analysis -- Conclusion -- Chapter 5. Chatbots and Conversational AI -- Building Chatbots with Python -- Developing Goal Oriented Chatbots with DialogFlow -- Building Text Transformers -- Loading and Preparing Data -- Understanding Attention -- Masking and the Transformer -- Encoding and Decoding the Sequence -- Training Conversational Chatbots -- Compiling and Training the Model -- Evaluation and Prediction -- Using Transformer for Conversational Chatbots -- Conclusion -- Chapter 6. Video Analysis on the Cloud chapter 7: Generators in the cloud -- Chater 8: Building AI assistants in the cloud -- Chapter 9: Putting AI assistants to work -- Chapter 10: Commercializing AI.

Summary: 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.

Tags from this library: No tags from this library for this title. Log in to add tags.

Holdings
Item type	Current library	Home library	Call number	Status	Date due	Barcode
Book	Dept. of Computational Biology and Bioinformatics Processing Center	Dept. of Computational Biology and Bioinformatics	006.3 LAN-P (Browse shelf(Opens below))	Available		DCB3990

Browsing Dept. of Computational Biology and Bioinformatics shelves, Shelving location: Processing Center Close shelf browser (Hides shelf browser)

Previous								Next
Previous	006.3 KON-A Artificial Intelligence and Soft Computing: Behavioral and Cognitive Modeling of the Human Brain	006.3 KUB-I An introduction to machine learning	006.3 KUL-A .CM Artificial Intelligence: Building Intelligent Systems	006.3 LAN-P Practical AI on the Google cloud platform : Utilizing Google's state-of-the-art AI cloud services	006.3 LEL-A Artificial intelligence meets augmented reality: Redefining regular reality	006.3 MIT-D Data Mining: Multimedia, Soft Computing, and Bioinformatics	006.3 MIT-D Data Mining: Multimedia, Soft Computing, and Bioinformatics	Next

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.

Chapter 1. Data Science and Deep Learning --
What is Data Science? --
Classification and Regression --
Regression --
Goodness of Fit --
Classification with Logistic Regression --
Multi-variant Regression and Classification --
Data Discovery and Preparation --
Bad Data Training, Test and Validation Data --
Good Data --
Preparing Data --
Questioning Your Data --
The Basics of Deep Learning --
The Perceptron Game --
Understanding How Networks Learn --
Backpropagation --
Optimization and Gradient Descent --
Vanishing or Exploding Gradients --
SGD and Batching Samples --
Batch Normalization and Regularization --
Activation Functions --
Loss Functions --
Building a Deep Learner --
Overfitting and Underfitting --
Network Capacity --
Conclusion --
Game Answers --

Chapter 2. AI on the Google Cloud Platform --
AI Services on GCP --
The AI Hub --
AI Platform AI Building Blocks --
Google Colab Notebooks --
Building a Regression Model with Colab --
AutoML Tables --
The Cloud Shell --
Managing Cloud Data --
Conclusion --

Chapter 3. Image Analysis and Recognition on the Cloud --
Deep Learning with Images --
Enter Convolution Neural Networks --
Image Classification --
Setup and Load Data --
Inspecting Image Data --
Channels and CNN --
Building the Model --
Training the AI Fashionista to Discern Fashions --
Improving Fashionista AI 2.0 --
Transfer Learning Images --
Identifying Cats or Dogs --
Transfer Learning a Keras Application Model Training Transfer Learning --
ReTraining a Better Base Model --
Object Detection and the Object Detection Hub API --
YOLO for Object Detection --
Generating Images with GANs --
Conclusion --

Chapter 4. Understanding Language on the Cloud --
Natural Language Processing, with Embeddings --
Recurrent Networks for NLP --
Recurrent Networks for Memory --
RNN Variations --
Neural Translation and the Translation API --
Sequence to Sequence Learning --
Translation API --
AutoML Translation --
Natural Language API --
BERT Bidirectional Encoder Representations from Transformers Semantic Analysis with BERT --
Document Matching with BERT --
BERT for General Text Analysis --
Conclusion --

Chapter 5. Chatbots and Conversational AI --
Building Chatbots with Python --
Developing Goal Oriented Chatbots with DialogFlow --
Building Text Transformers --
Loading and Preparing Data --
Understanding Attention --
Masking and the Transformer --
Encoding and Decoding the Sequence --
Training Conversational Chatbots --
Compiling and Training the Model --
Evaluation and Prediction --
Using Transformer for Conversational Chatbots --
Conclusion --

Chapter 6. Video Analysis on the Cloud
chapter 7: Generators in the cloud --
Chater 8: Building AI assistants in the cloud --
Chapter 9: Putting AI assistants to work --
Chapter 10: Commercializing AI.

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.

There are no comments on this title.

to post a comment.