Learn TensorFlowjs - Deep Learning and Neural Networks with JavaScript
This course provides an introduction to deep learning and neural networks using JavaScript. It covers topics such as converting a Keras model to the Layers API format, serving deep learning models with Node.js and Express, building a UI for a neural network web app, and loading a model into a neural network. Participants will gain the skills to create and deploy their own deep learning models. ▼
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Course Feature
Cost:
Free
Provider:
freeCodeCamp
Certificate:
Paid Certification
Language:
English
Start Date:
On-Demand
Course Overview
❗The content presented here is sourced directly from freeCodeCamp platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.
Updated in [February 21st, 2023]
This course provides an introduction to deep learning and neural networks with JavaScript, allowing users to create and serve deep learning models with Node.js and Express, build UI for neural network web apps, and explore tensor operations with VGG16 preprocessing.
Possible Development Paths include web development, software engineering, data science, and machine learning.
Learning Suggestions for learners include brushing up on JavaScript, HTML, and CSS, as well as exploring other deep learning frameworks such as Keras and TensorFlow. Additionally, learners should consider taking courses in data science, machine learning, and artificial intelligence.
[Applications]
After taking this course, students should be able to apply their knowledge of TensorFlow.js to create deep learning models with JavaScript. They should be able to convert Keras models to the Layers API format, serve deep learning models with Node.js and Express, build UI for neural network web apps, load models into a neural network web app, explore tensor operations with VGG16 preprocessing, examine tensors with the debugger, broadcast with tensors, and run MobileNet in the browser.
[Career Paths]
1. Machine Learning Engineer: Machine Learning Engineers are responsible for developing and deploying machine learning models and algorithms. They use TensorFlowjs to build and deploy deep learning models and neural networks. They also use Node.js and Express to serve these models. As the demand for machine learning increases, the need for Machine Learning Engineers is expected to grow.
2. Data Scientist: Data Scientists use TensorFlowjs to analyze large datasets and uncover patterns and insights. They use the Layers API to convert Keras models and use the debugger to examine tensors. As data becomes increasingly important in decision-making, the demand for Data Scientists is expected to grow.
3. Artificial Intelligence Developer: Artificial Intelligence Developers use TensorFlowjs to create intelligent applications and systems. They use the VGG16 preprocessing to explore tensor operations and use broadcasting with tensors to run MobileNet in the browser. As AI becomes more prevalent in everyday life, the demand for Artificial Intelligence Developers is expected to grow.
Course Provider
Provider freeCodeCamp's Stats at AZClass
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