Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners
Authors: Ekaba Bisong
ISBN-10: 1484244699
ISBN-13: 9781484244692
Edition 版次: 1st ed.
Publication Date 出版日期: 2019-09-28
Print Length 页数: 709 pages
Book Description
By finelybook
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.
What You’ll Learn
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
Part 1. Getting Started with Google Cloud Platform
1. What Is Cloud Computing?
2. An Overview of Google Cloud Platform Services
3. The Google Cloud SDK and Web CLl
4. Google Cloud Storage(GCS)
5. Google Compute Engine(GCE)
6. JupyterLab Notebooks
7. Google Colaboratory
Part . Programming Foundations for Data Science
8. What Is Data Science?
9. Python
10. NumPy
11. Pandas
12. Matplotlib and Seaborn
Part ll. Introducing Machine Learning
13. What Is Machine Learning?
14. Principles of Learning
15. Batch vs. Online Learning
16. Optimization for Machine Learning: Gradient Descent
17. Learning Algorithms
Part IV. Machine Learning in Practice
18. Introduction to Scikit-learn
19. Linear Regression
20. Logistic Regression
21. Regularization for Linear Models
22. Support Vector Machines
23. Ensemble Methods
24. More Supervised Machine Learning Techniques with Scikit-learn
25. Clustering
26. Principal Component Analysis (PCA)
Part V. Introducing Deep Learning
27. What Is Deep Learning?
28. Neural Network Foundations
29. Training a Neural Network
Part I. Deep Learning in Practice
30. TensorFlow 2.0 and Keras
31. The Multilayer Perceptron(MLP)
32. Other Considerations for Training the Network
33. More on Optimization Techniques
34. Regularization for Deep Learning
35. Convolutional Neural Networks (CNN)
36. Recurrent Neural Networks (RNNS)
37. Autoencoders
Part Vll. Advanced Analytics/Machine Learning on Google Cloud Platform
38. Google BigQuery
39. Google Cloud Dataprep
40. Google Cloud Dataflow
41. Google Cloud Machine Learning Engine(Cloud MLE)
42. Google AutoML: Cloud Vision
43. Google AutoML: Cloud Natural Language Processing
44. Model to Predict the Critical Temperature of Superconductors
Part VllProductionalizing Machine Learning Solutions on GCP
45. Containers and Google Kubernetes Engine
46. Kubeflow and Kubeftlow Pipelines
47. Deploying an End-to-End Machine Learning Solution on Kubeflow Pipelines
