Titlebook: Building Machine Learning and Deep Learning Models on Google Cloud Platform; A Comprehensive Guid Ekaba‘Bisong Book 2019 Ekaba Bisong 2019

Petechiae

meritorious

Google Cloud Storage (GCS) has guarantees of scalability (can store increasingly large data objects), consistency (the most updated version is served on request), durability (data is redundantly placed in separate geographic locations to eliminate loss), and high availability (data is always available and accessible).

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分散

JupyterLab Notebooksrelevant software packages for carrying out analytics and modeling tasks. It also makes available high-performance computing TPU and GPU processing capabilities at a single click. These VMs expose a JupyterLab notebook environment for analyzing data and designing machine learning models.

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期滿(mǎn)

Principles of Learningies of learning are the supervised, unsupervised, and reinforcement learning schemes. In this chapter, we will go over supervised learning schemes in detail and also touch upon unsupervised and reinforcement learning schemes to a lesser extent.

OMIT

Batch vs. Online Learningild your learning model with data at rest (batch learning), and the other is when the data is flowing in streams into the learning algorithm (online learning). This flow can be as individual sample points in your dataset, or it can be in small batch sizes. Let’s briefly discuss these concepts.

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Optimization for Machine Learning: Gradient Descentn iterative optimization algorithm because, in a stepwise looping fashion, it tries to find an approximate solution by basing the next step off its present step until a terminating condition is reached that ends the loop.

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Building Machine Learning and Deep Learning Models on Google Cloud PlatformA Comprehensive Guid

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