venkat prasad reddy dharmala
Participante desde 2022
Participante desde 2022
This course describes different types of computer vision use cases and then highlights different machine learning strategies for solving these use cases. The strategies vary from experimenting with pre-built ML models through pre-built ML APIs and AutoML Vision to building custom image classifiers using linear models, deep neural network (DNN) models or convolutional neural network (CNN) models. The course shows how to improve a model's accuracy with augmentation, feature extraction, and fine-tuning hyperparameters while trying to avoid overfitting the data. The course also looks at practical issues that arise, for example, when one doesn't have enough data and how to incorporate the latest research findings into different models. Learners will get hands-on practice building and optimizing their own image classification models on a variety of public datasets in the labs they will work on.
This course introduces the products and solutions to solve NLP problems on Google Cloud. Additionally, it explores the processes, techniques, and tools to develop an NLP project with neural networks by using Vertex AI and TensorFlow.
In this course, you apply your knowledge of classification models and embeddings to build a ML pipeline that functions as a recommendation engine. This is the fifth and final course of the Advanced Machine Learning on Google Cloud series.
One of the best ways to review something is to work with the concepts and technologies that you have learned. So, this course is set up as a workshop and in this workshop, you will do End-to-End Machine Learning with TensorFlow on Google Cloud Platform. It involves building an end-to-end model from data exploration all the way to deploying an ML model and getting predictions from it. This is the first course of the Advanced Machine Learning on Google Cloud series. After completing this course, enroll in the Production Machine Learning Systems course.
Quais são as práticas recomendadas para implementar machine learning no Google Cloud? O que é Vertex AI e como é possível usar a plataforma para criar, treinar e implantar modelos de machine learning do AutoML com rapidez e sem escrever nenhuma linha de código? O que é machine learning e que tipos de problema ele pode resolver? O Google pensa em machine learning de uma forma um pouco diferente. Para nós, o processo de ML é sobre fornecer uma plataforma unificada para conjuntos de dados gerenciados, como uma Feature Store, uma forma de criar, treinar e implantar modelos de machine learning sem escrever nenhuma linha de código. Além disso, o ML também é sobre a habilidade de rotular dados, criar notebooks do Workbench usando frameworks (como TensorFlow, SciKit Learn, Pytorch e R) e muito mais. A plataforma Vertex AI também inclui a possibilidade de treinar modelos personalizados, criar pipelines de componente e realizar previsões em lote e on-line. Também falamos sobre as cinco fas…
Este curso apresenta os produtos e serviços de Big Data e machine learning do Google Cloud que auxiliam no ciclo de vida de dados para IA. Ele explica os processos, os desafios e os benefícios de criar um pipeline de Big Data e modelos de machine learning com a Vertex AI no Google Cloud.