Learn Flexbox in 20 Minutes

This video will walk through you all the concepts that you must know about CSS Flexbox Module.

Flexbox is a one-dimensional layout method for arranging items in rows or columns. Items flex (expand) to fill additional space or shrink to fit into smaller spaces.

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🔥🔥🔥 Happy Learning 🔥🔥🔥
Timeline :
-------------------------------------------------------------------
00:00 Introduction to Flexbox
00:48 4 Main Concepts you must know
01:57 Creating Flex Container

Flex Container Properties
-------------------------------
03:31 Flex-Direction-Row
05:11 Justify-Content
06:31 Align-Items
08:55 Flex-Direction-Column
10:45 Flex-Wrap
19:25 Align Content

Flex Item Properties
-------------------------------
11:39 Flex Item Properties Starts
11:44 Align-Self
12:38 Order Property
14:23 Flex-Grow
16:10 Flex-Basis
16:48 Flex-Shrink
18:25 Flex Shorthand Property

 

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CSS FlexBox Cheat Sheets for Web Developers in 2021

It’s 2021! Let’s refresh Our CSS Flexbox  Memory. Here’s a Cheat Sheet of everything you can do with CSS flexbox to get started in 2021

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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.

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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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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.

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