Learning data science may seem intimidating but it doesn’t have to be that way. Let’s make learning data science fun and easy. So the challenge is how do we exactly make learning data science both…
Learning data science may seem intimidating but it doesn’t have to be that way. Let’s make learning data science fun and easy. So the challenge is how do we exactly make learning data science both…
Learning data science may seem intimidating but it doesn’t have to be that way. Let’s make learning data science fun and easy. So the challenge is how do we exactly make learning data science both…
Learning data science may seem intimidating but it doesn’t have to be that way. Let’s make learning data science fun and easy. So the challenge is how do we exactly make learning data science both…
Learning data science may seem intimidating but it doesn’t have to be that way. Let’s make learning data science fun and easy. So the challenge is how do we exactly make learning data science both…
Learning data science may seem intimidating but it doesn’t have to be that way. Let’s make learning data science fun and easy. So the challenge is how do we exactly make learning data science both…
Learning data science may seem intimidating but it doesn’t have to be that way. Let’s make learning data science fun and easy. So the challenge is how do we exactly make learning data science both…
Learn the skills necessary to design, build, and deploy applications powered by machine learning. Through the course of this hands-on book, you'll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managers with little or no ML experience will learn the tools, best practices, and challenges involved in building a real-world ML application step-by-step. Author Emmanuel Ameisen, who worked as a data scientist at Zipcar and led Insight Data Science's AI program, demonstrates key ML concepts with code snippets, illustrations, and screenshots from the book's example application. The first part of this guide shows you how to plan and measure success for an ML application. Part II shows you how to build a working ML model, and Part III explains how to improve the model until it fulfills your original vision. Part IV covers deployment and monitoring strategies. This book will help you: Determine your product goal and set up a machine learning problem Build your first end-to-end pipeline quickly and acquire an initial dataset Train and evaluate your ML model and address performance bottlenecks Deploy and monitor models in a production environment | Author: Emmanuel Ameisen | Publisher: O'Reilly Media | Publication Date: February 04, 2020 | Number of Pages: 260 pages | Language: English | Binding: Paperback | ISBN-10: 149204511X | ISBN-13: 9781492045113
Understand deep learning, the nuances of its different models, and where these models can be applied. The abundance of data and demand for superior products/services have driven the development of advanced computer science techniques, among them image and speech recognition. Introduction to Deep Learning Using R provides a theoretical and practical…
Machine Learning systems are complex. At their core, they ingest data in a certain format, to build models that are able to predict the future. A famous example in the industry is identifying fragile…
Learn when to apply TensorFlow neural networks, solve neural network problems, understand image data, and compare TensorFlow with other libraries.
