Building machine learning and deep learning models on Google Cloud platform : a comprehensive guide for beginners
Ekaba Bisong (Author)
Take a systematic approach to understanding the fundamentals of machine learning and deep learning from the ground up and how they are applied in practice. You will use this comprehensive guide for building and deploying learning models to address complex use cases while leveraging the computational resources of Google Cloud Platform. Author Ekaba Bisong shows you how machine learning tools and techniques are used to predict or classify events based on a set of interactions between variables known as features or attributes in a particular dataset. He teaches you how deep learning extends the machine learning algorithm of neural networks to learn complex tasks that are difficult for computers to perform, such as recognizing faces and understanding languages. And you will know how to leverage cloud computing to accelerate data science and machine learning deployments. Building Machine Learning and Deep Learning Models on Google Cloud Platform is divided into eight parts that cover the fundamentals of machine learning and deep learning, the concept of data science and cloud services, programming for data science using the Python stack, Google Cloud Platform (GCP) infrastructure and products, advanced analytics on GCP, and deploying end-to-end machine learning solution pipelines on GCP. You will: Understand the principles and fundamentals of machine learning and deep learning, the algorithms, how to use them, when to use them, and how to interpret your results Know the programming concepts relevant to machine and deep learning design and development using the Python stack Build and interpret machine and deep learning models Use Google Cloud Platform tools and services to develop and deploy large-scale machine learning and deep learning products Be aware of the different facets and design choices to consider when modeling a learning problem Productionalize machine learning models into software products
xxix, 709 pages : color illustrations ; 26 cm
9781484244692, 1484244699
1084326489
Part 1. Getting started with Google Cloud platform. What is Cloud computing?
An overview of Google Cloud platform services
The Google Cloud SDK and Web CLI
Google Cloud Storage (GCS)
Google compute engine (GCE)
JupyterLab Notebooks
Google colaboratory
Part 2. Programming foundations for data science. What is data science?
Python
NumPy
Pandas
Matplotlib and Seaborn
Part 3. Introducing machine learning. What is machine learning?
Principles of learning
Batch vs. online learning
Optimization for machine learning: gradient descent
Learning algorithms
Part 4. Machine learning in practice. Introduction to Scikit-learn
Linear regression
Logistic regression
Regularization for linear models
Support vector machines
Ensemble methods
More supervised machine learning techniques with Scikit-learn
Clustering
Principal component analysis (PCA)
Part 5. Introducing deep learning. What is deep learning?
Neural network foundations
Training a neural network
Part 6. Deep learning in practice. TensorFlow 2.0 and Keras
The multilayer perceptron (MLP)
Other considerations for training the network
More on optimization techniques
Regularization for deep learning
Convolutional neural networks (CNN)
Recurrent neural networks (RNNs)
Autoencoders
Part 7. Advanced analytics/machine learning on Google platform. Google BigQuery
Google Cloud dataprep
Google Cloud dataflow
Google cloud machine learning engine (Cloud MLE)
Google AutoML: cloud vision
Google AutoML: cloud natural language processing
Model to predict the critical temperature of superconductors
Part 8. Productionalizing machine learning solutions on GCP. Containers and Google Kubernetes engine
Kubeflow and Kuberflow pipelines
Deploying an end-to-end machine learning solution on Kubeflow pipelines
Includes index
"For professionals by professionals."