Bringing a machine learning model into the real world involves a lot more than just modeling. This Specialization will teach you how to navigate various deployment scenarios and use data more effectively to train your model.
In this first course, you’ll train and run machine learning models in any browser using TensorFlow.js. You’ll learn techniques for handling data in the browser, and at the end you’ll build a computer vision project that recognizes and classifies objects from a webcam.
This Specialization builds upon our TensorFlow in Practice Specialization. If you are new to TensorFlow, we recommend that you take the TensorFlow in Practice Specialization first. To develop a deeper, foundational understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.
Welcome to Browser-based Models with TensorFlow.js, the first course of the TensorFlow for Data and Deployment Specialization. In this first course, we’re going to look at how to train machine learning models in the browser and how to use them to perform inference using JavaScript. This will allow…
This week we'll look at Computer Vision problems, including some of the unique considerations when using JavaScript, such as handling thousands of images for training. By the end of this module you will know how to build a site that lets you draw in the browser and recognizes your handwritten digit…
This week we'll see how to take models that have been created with TensorFlow in Python and convert them to JSON format so that they can run in the browser using Javascript. We will start by looking at two models that have already been pre-converted. One of them is going to be a toxicity classifier…
One final work type that you'll need when creating Machine Learned applications in the browser is to understand how transfer learning works. This week you'll build a complete web site that uses TensorFlow.js, capturing data from the web cam, and re-training mobilenet to recognize Rock, Paper and Sc…
Some points apply to every course of this kind; see how we rank.
DeepLearning.AI is in Tier 2: excellent universities and the companies that build the technology of our institution ranking (90/100). Andrew Ng's courses.
Instructor: Dennis Freeman, Kendra Pugh This course provides an integrated introduction to electrical engineering and computer science, including modern software engineering, linear systems analysis, electronic circuits, and decision-making. The lecture videos provide an overview of each topic, whi…
This is CS50W, CS50's Web Programming with Python and JavaScript. Register for free at https://cs50.edx.org/web. Slides and source code at https://cs50.harvard.edu/web. This course picks up where Harvard University’s CS50 leaves off, diving more deeply into the design and implementation of web apps…
Learning objectives This course picks up where Harvard University's CS50 leaves off, diving more deeply into the design and implementation of web apps with Python, JavaScript, and SQL using frameworks like Flask, Django, and Bootstrap. Topics include database design, scalability, security, and user…
Harvard University · edX
An introduction to the intellectual enterprises of computer science and the art of programming.
Learning objectives This course picks up where Harvard University's CS50 leaves off, transitioning from web development to mobile app development with React Native, a popular open-source framework maintained by Facebook that enables cross-platform native apps using JavaScript without Java or Swift.…
Deep dive into latest Google Chrome, Angular, ChromeOS updates to build fast and cutting-edge web experiences. For any web developer, this playlist offers you the latest insights into the newest tools for your web projects.
