▶Book Description
Google Cloud Machine Learning Engine combines the services of Google Cloud Platform with the power and flexibility of TensorFlow. With this book, you will not only learn to build and train different complexities of machine learning models at scale but also host them in the cloud to make predictions.
This book is focused on making the most of the Google Machine Learning Platform for large datasets and complex problems. You will learn from scratch how to create powerful machine learning based applications for a wide variety of problems by leveraging different data services from the Google Cloud Platform. Applications include NLP, Speech to text, Reinforcement learning, Time series, recommender systems, image classification, video content inference and many other. We will implement a wide variety of deep learning use cases and also make extensive use of data related services comprising the Google Cloud Platform ecosystem such as Firebase, Storage APIs, Datalab and so forth. This will enable you to integrate Machine Learning and data processing features into your web and mobile applications.
By the end of this book, you will know the main difficulties that you may encounter and get appropriate strategies to overcome these difficulties and build efficient systems
▶What You Will Learn
⦁ Use Google Cloud Platform to build data-based applications for dashboards, web, and mobile
⦁ Create, train and optimize deep learning models for various data science problems on big data
⦁ Learn how to leverage BigQuery to explore big datasets
⦁ Use Google's pre-trained TensorFlow models for NLP, image, video and much more
⦁ Create models and architectures for Time series, Reinforcement Learning, and generative models
⦁ Create, evaluate, and optimize TensorFlow and Keras models for a wide range of applications
▶Key Features
⦁ Get well versed in GCP pre-existing services to build your own smart models
⦁ A comprehensive guide covering aspects from data processing, analyzing to building and training ML models
⦁ A practical approach to produce your trained ML models and port them to your mobile for easy access
▶Who This Book Is For
This book is for data scientists, machine learning developers and AI developers who want to learn Google Cloud Platform services to build machine learning applications. Since the interaction with the Google ML platform is mostly done via the command line, the reader is supposed to have some familiarity with the bash shell and Python scripting. Some understanding of machine learning and data science concepts will be handy
▶What this book covers
⦁ Chapter 1, Introducing the Google Cloud Platform, explores different services that may be useful to build a machine learning pipeline based on GCP.
⦁ Chapter 2, Google Compute Engine, helps you to create and fully manage your VM via both the online console and command-line tools, as well as how to implement a data science workflow and a Jupyter Notebook workspace.
⦁ Chapter 3, Google Cloud Storage, shows how to upload data and manage it using the services provided by the Google Cloud Platform.
⦁ Chapter 4, Querying Your Data with BigQuery, shows you how to query data from Google Storage and visualize it with Google Data Studio.
⦁ Chapter 5, Transforming Your Data, presents Dataprep, a service useful for preprocessing data, extracting features, and cleaning up records. We also look at Dataflow, a service used to implement streaming and batch processing.
⦁ Chapter 6, Essential Machine Learning, starts our journey into machine learning and deep learning; we learn when to apply each one.
⦁ Chapter 7, Google Machine Learning APIs, teaches us how to use Google Cloud machine learning APIs for image analysis, text and speech processing, translation, and video inference.
⦁ Chapter 8, Creating ML Applications with Firebase, shows how to integrate different GCP services to build a seamless machine-learning-based application, mobile or web-based.
⦁ Chapter 9, Neural Networks with TensorFlow and Keras, gives a good understanding of the structure and key elements of a feedforward network, how to architecture one, and how to tinker and experiment with different parameters.
⦁ Chapter 10, Evaluating Results with TensorBoard, shows how the choice of different parameters and functions impacts the performance of the model.
⦁ Chapter 11, Optimizing the Model through Hyperparameter Tuning, teaches us how to use hypertuning in TensorFlow application code and interpret the results to select the best performing model.
⦁ Chapter 12, Preventing Overfitting with Regularization, shows how to identify overfitting and make our models more robust to previously unseen data by setting the right parameters and defining the proper architectures.
⦁ Chapter 13, Beyond Feedforward Networks –. CNN and RNNs, teaches which type of neural network to apply to different problems, and how to define and implement them on GCP.
⦁ Chapter 14, Time Series with LSTMs, shows how to create LSTMs and apply them to time series predictions. We will also understand when LSTMs outperform more standard approaches.
⦁ Chapter 15, Reinforcement Learning, introduces the power of reinforcement learning and shows how to implement a simple use case on GCP.
⦁ Chapter 16, Generative Neural Networks, teaches us how to extract the content generated within the neural net with different types of content—.text, images, and sounds.
⦁ Chapter 17, Chatbots, shows how to train a contextual chatbot while implementing it in a real mobile application.
