Informações sobre o curso: We introduce low-level TensorFlow and work our way through the necessary concepts and APIs so as to be able to write distributed machine learning models. Given a TensorFlow model, we explain how to scale out the training of that model and offer high-performance predictions using Cloud Machine Learning Engine. Course Objectives: Create machine learning models in TensorFlow Use the TensorFlow libraries to solve numerical problems Troubleshoot and debug common TensorFlow code pitfalls Use tf.estimator to create, train, and evaluate an ML model Train, deploy, and productionalize ML models at scale with Cloud ML Engine
Para quem é direcionado este curso: This course is aimed at data scientists, data engineers and programmers interested in learning how to apply machine learning in practice.
Desenvolvido por: Google Cloud
| Informações básicas | |
| Nível | Intermediário |
| Compromisso | 1 week of study, 8-10 hours/week |
| Idioma | English |
| Requisitos de hardware | You'll need to use a desktop web browser to access labs. |
| Como ser aprovado | Seja aprovado em todas as tarefas para concluir o curso. |
| Classificação do usuário |
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Programa
SEMANA 1
Introduction
The tool we will use to write machine learning programs is TensorFlow and so in this course, we will introduce you to TensorFlow. In the first course, you learned how to formulate business problems as machine learning problems and in the second course, you learned how machine works in practice and how to create datasets that you can use for machine learning. Now that you have the data in place, you are ready to get started writing machine learning programs.
2 videos
- Vídeo: Intro to Qwiklabs
Core TensorFlow
We will introduce you to the core components of TensorFlow and you will get hands-on practice building machine learning programs. You will compare and write lazy evaluation and imperative programs, work with graphs, sessions, variables, as finally debug TensorFlow programs.
19 videos
- Vídeo: What is TensorFlow
- Vídeo: Benefits of a Directed Graph
- Vídeo: TensorFlow API Hierarchy
- Vídeo: Lazy Evaluation
- Discussion Prompt: Importance of an API hierarchy
- Vídeo: Graph and Session
- Vídeo: Evaluating a Tensor
- Vídeo: Visualizing a graph
- Vídeo: Tensors
- Vídeo: Variables
- Discussion Prompt: Variables and Placeholder
- Vídeo: Lab Intro: Writing low-level TensorFlow programs
- Vídeo: Lab Solution
- Vídeo: Introduction
- Vídeo: Shape problems
- Vídeo: Fixing shape problems
- Vídeo: Data type problems
- Vídeo: Debugging full programs
- Vídeo: Intro: Debugging full programs
- Vídeo: Demo: Debugging Full Programs
Nota atribuída: What is TensorFlow?
Nota atribuída: Graphs and Sessions
Nota atribuída: Qwiklabs – Writing low-level TensorFlow programs
Nota atribuída: Core TensorFlow
SEMANA 2
Estimator API
In this module we will walk you through the Estimator API.
18 videos
- Vídeo: Estimator API
- Vídeo: Pre-made Estimators
- Vídeo: Demo: Housing Price Model
- Vídeo: Checkpointing
- Vídeo: Training on in-memory datasets
- Vídeo: Lab Intro: Estimator API
- Vídeo: Lab Solution: Estimator API
- Vídeo: Train on large datasets with Dataset API
- Vídeo: Lab Intro: Scaling up TensorFlow ingest using batching
- Vídeo: Lab Solution: Scaling up TensorFlow ingest using batching
- Vídeo: Big jobs, Distributed training
- Vídeo: Monitoring with TensorBoard
- Vídeo: Demo: TensorBoard UI
- Vídeo: Serving Input Function
- Vídeo: Recap: Estimator API
- Vídeo: Lab Intro: Creating a distributed training TensorFlow model with Estimator API
- Vídeo: Lab Solution: Creating a distributed training TensorFlow model with Estimator API
Nota atribuída: Qwiklabs – Lab Estimator API
Nota atribuída: Qwiklabs – Scaling up TensorFlow ingest using batching
Nota atribuída: Qwiklabs – Creating a distributed training TensorFlow model with Estimator API
Nota atribuída: Estimator API
SEMANA 3
Scaling TensorFlow models with CMLE
I’m here to talk about how you would go about taking your TensorFlow model and training it on GCP’s managed infrastructure for machine learning model training and deployed.
6 videos
- Vídeo: Why Cloud Machine Learning Engine?
- Vídeo: Train a Model
- Vídeo: Monitoring and Deploying Training Jobs
- Vídeo: Lab Intro: Scaling TensorFlow with Cloud Machine Learning Engine
- Vídeo: Lab Solution: Scaling TensorFlow with Cloud Machine Learning Engine
Nota atribuída: Qwiklabs – Scaling TensorFlow with Cloud Machine Learning Engine
Nota atribuída: Cloud MLE
Summary
Here we summarize the TensorFlow topics we covered so far in this course. We'll revisit core TensorFlow code, the Estimator API, and end with scaling your machine learning models with Cloud Machine Learning Engine.
1 vídeos
Perguntas frequentes
Como funciona
Coursework
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Desenvolvedores
Google Cloud
We help millions of organizations empower their employees, serve their customers, and build what’s next for their businesses with innovative technology created in—and for—the cloud. Our products are engineered for security, reliability, and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping customers apply our technologies to create success.
Custo
| Comprar curso | |
|---|---|
| Acesso aos materiais do curso | Disponível |
| Acesso a materiais valendo nota | Disponível |
| Receba uma nota final | Disponível |
| Obtenha um Certificado de Curso compartilhável | Disponível |
Classificações e avaliações
Good general TensorFlow introduction. The course focuses on the highest level tf.Estimator whereas there are maybe something interesting low level they don't present.
Great overview, well paced.
This course shows show how to build models in tensorflow what is necessary to train and validate them, then you can check your work using tensorboard in a graphical way. Lastly you can expose your ML model using gcloud.
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An excellent introduction to TensorFlow, Including debugging tips, and how to scale up TensorFlow models and deploy them. So why only 4 stars ? because there is no audit option for this course and the videos can't be downloaded. Presumable the notebooks with sample code can be cloned from Github - but it seems the explanations will not be available unless you re-enroll. This policy is even more inexplicable considering that the course serves as a "presale" for the Google cloud platform.