Learn SVG Through 24 Examples

In this SVG tutorial, we go through the source code of 24 SVGs from simple to more complex ones. We cover basic shapes, quadratic and cubic Bézier curves, arcs, how to use groups to transform part of an image, and some fun examples on how to animate SVG with CSS and how to make them interactive with JavaScript.

You can find all the source codes here: https://codepen.io/HunorMarton/pen/PoGbgqj

  • 0:00 Introduction
  • 1:11 The SVG tag
  • 3:31 Simple shapes
  • 6:27 Clip-path
  • 7:08 Transformations
  • 9:26 Gradients
  • 10:07 Curves and arcs
  • 13:31 Animation
  • 14:51 Background patterns
  • 15:33 SVG with JavaScript

The images shown in the video are based or inspired by the work of various artists on Dribbble including:
Snowflake, Gingerbread figure, Start, Bear face by Claire Pinot: https://dribbble.com/shots/8678048-Advent-calendar
House by catalyst https://dribbble.com/shots/8702505-Winter-is-coming
Forest by Haley Harms https://dribbble.com/shots/14611450-Snow-Globe-Holiday-Graphic
Snowman by Elen Winata: https://dribbble.com/shots/14647070-Snow-Globe
Ribbon by Željka Živković https://dribbble.com/shots/12587762-Gifts
Stick by Shelby Warwood https://dribbble.com/shots/14485623-Holiday-Cookies-Illustration
Background patterns by https://www.svgbackgrounds.com/
Lights by Erdem https://dribbble.com/shots/9178510-Free-20-Christmas-Icons

#svg #css #javascript

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Learn SVG Through 24 Examples
Jerad  Bailey

Jerad Bailey

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Google Reveals "What is being Transferred” in Transfer Learning

Recently, researchers from Google proposed the solution of a very fundamental question in the machine learning community — What is being transferred in Transfer Learning? They explained various tools and analyses to address the fundamental question.

The ability to transfer the domain knowledge of one machine in which it is trained on to another where the data is usually scarce is one of the desired capabilities for machines. Researchers around the globe have been using transfer learning in various deep learning applications, including object detection, image classification, medical imaging tasks, among others.

#developers corner #learn transfer learning #machine learning #transfer learning #transfer learning methods #transfer learning resources

Swati patel

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What is Game Based Learning (GBL) - Benefit & Example of Game Base Learning

“Game Based Learning is the future of EdTech and eLearning. Explore its many benefits and examples to unlock its true potential and transform your learning best experience.”

https://www.communicationcrafts.com/benefit-example-of-game-base-learning-gbl/?cc=com&?utm_source=morioh&utm_medium=SBM&utm_campaign=Game-Based-Learning:-The-Future-of-EdTech-in-eLearning

##gbl ##game based learning ##learning ##edtech ##edtech learning ##learning experience

sophia tondon

sophia tondon

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5 Latest Technology Trends of Machine Learning for 2021

Check out the 5 latest technologies of machine learning trends to boost business growth in 2021 by considering the best version of digital development tools. It is the right time to accelerate user experience by bringing advancement in their lifestyle.

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Visit Blog- https://www.xplace.com/article/8743

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Jackson  Crist

Jackson Crist

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Intro to Reinforcement Learning: Temporal Difference Learning, SARSA Vs. Q-learning

Reinforcement learning (RL) is surely a rising field, with the huge influence from the performance of AlphaZero (the best chess engine as of now). RL is a subfield of machine learning that teaches agents to perform in an environment to maximize rewards overtime.

Among RL’s model-free methods is temporal difference (TD) learning, with SARSA and Q-learning (QL) being two of the most used algorithms. I chose to explore SARSA and QL to highlight a subtle difference between on-policy learning and off-learning, which we will discuss later in the post.

This post assumes you have basic knowledge of the agent, environment, action, and rewards within RL’s scope. A brief introduction can be found here.

The outline of this post include:

  • Temporal difference learning (TD learning)
  • Parameters
  • QL & SARSA
  • Comparison
  • Implementation
  • Conclusion

We will compare these two algorithms via the CartPole game implementation. This post’s code can be found here :QL code ,SARSA code , and the fully functioning code . (the fully-functioning code has both algorithms implemented and trained on cart pole game)

The TD learning will be a bit mathematical, but feel free to skim through and jump directly to QL and SARSA.

#reinforcement-learning #artificial-intelligence #machine-learning #deep-learning #learning

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