A multi-user version of the notebook designed for companies, classrooms and research labs. Deploy the Jupyter Notebook to thousands of users in your organization on centralized infrastructure on- or off-site.
Use Docker and Kubernetes to scale your deployment, isolate user processes, and simplify software installation. Deploy the Notebook next to your data to provide unified software management and data access within your organization. Project Jupyter promotes open standards that third-party developers can leverage to build customized applications.
They contain a complete record of the user's sessions and include code, narrative text, equations, and rich output. Kernels are processes that run interactive code in a particular programming language and return output to the user. Kernels also respond to tab completion and introspection requests. Try it in your browser Install JupyterLab.
Jupyter Notebook: The Classic Notebook Interface The Jupyter Notebook is the original web application for creating and sharing computational documents. Try it in your browser Install the Notebook. Language of choice Jupyter supports over 40 programming languages, including Python, R, Julia, and Scala. A service that translates unicast traffic to multicast traffic. This repository contains an entire class on creating network automation. It utilizes vSRX This python script was built by putting together small portions of code available at the Skip to main content Press Enter.
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Course 1 of 5 in the Deep Learning Specialization. In the first course of the Deep Learning Specialization, you will study the foundational concept of neural networks and deep learning. The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology.
It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI. Extremely helpful review of the basics, rooted in mathematics, but not overly cumbersome.
Very clear, and example coding exercises greatly improved my understanding of the importance of vectorization. I would love some pointers to additional references for each video. Also, the instructor keeps saying that the math behind backprop is hard. What about an optional video with that? Otherwise, awesome!
Set up a machine learning problem with a neural network mindset and use vectorization to speed up your models. Neural Networks and Deep Learning. Enroll for Free. This Course Video Transcript. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose. All rights reserved. Get updates from Juniper.
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