Beth  Nabimanya

Beth Nabimanya

1619554920

The Ultimate Guide to Learning Sass/SCSS

Sass is one of the most widely used preprocessors out there. A preprocessor is a language or a program that generates CSS from the code written using the unique syntax, features provided by the preprocessor itself. Since the browser does not understand the Sass/SCSS code, all of the code compiles back to standard CSS.

Sass vs. SCSS

Sass and SCSS refer basically to the same Sass language. The only major difference between them is the way we write them. The file written with the .scss extension follows the traditional block-like structure with { } to write the rules. Whereas the file with the .sass extension follows the indentation-based structure without semicolons (as used in python) to write the rules.

#web-development #ux #front-end-development #programming #css #sass

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The Ultimate Guide to Learning Sass/SCSS
Biju Augustian

Biju Augustian

1575032127

Learn SASS and SCSS | Sass & SCSS Tutorial for Beginners

Description
As a developer, you know the importance of CSS and you also know how CSS can become cumbersome, disorganized and repetitive. Maybe your CSS performs poorly, or, maybe your just spending too much time digging through CSS code to solve minor display issues.

Stop wasting time tearing your hair out over confusing or poor performing CSS.

With Learn SASS and SCSS with Dave Moran, you’ll be introduced to to the technologies of SASS (Syntactically Awesome Style Sheets) and SCSS (Sassy CSS). If you’re not familiar with these important advances in the CSS world, SASS is a CSS pre-processor with syntax advancements. Style sheets in the advanced syntax are processed, and turned into regular CSS style sheets. SCSS is a super set of CSS, expanded to accommodate the features of SASS. SASS can be used with any version of CSS and all CSS libraries are compatible.

In this course you’ll do more than watch demonstrations from an expert instructor. Dave will invite you to code along as you go from SASS newbie to expert. You’ll complete this course ready to integrate SASS workflow and SCSS syntax in to your own development projects. To insure that you retain the information presented, Dave has prepared several code exercises to help you get the hang of things.

With over 1,000,000 enrollments world wide LearnToProgram brings you instructors who are teachers first, our instructors are able to take complex technical information and make it understandable for just about anyone. Dave Moran is no exception and you will find his teaching style to be both comfortable and immediately understandable as you learn SASS and SCSS.

Welcome to Learn SASS and SCSS with Dave Moran. See you in class!

Who is the target audience?

Web Developers and Designers
Front End Developers
Software Engineers
Basic knowledge
You should have fundamental knowledge of HTML and CSS
You should be able to use your PC or Mac operating system
What will you learn
Set up a SAAS project
Understand and apply SAAS nesting
Use and apply the concept of Segmentation
Integrate SAAS Variables in to your Work
Understand SCSS Mixins and SCSS Functions
Create your Own Mixins
Know and apply Best Practices
To continue:

#sass #scss #php

Jerad  Bailey

Jerad Bailey

1598891580

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

Tia  Gottlieb

Tia Gottlieb

1596336480

Beginners Guide to Machine Learning on GCP

Introduction to Machine Learning

  • Machine Learning is a way to use some set of algorithms to derive predictive analytics from data. It is different than Business Intelligence and Data Analytics in a sense that In BI and Data analytics Businesses make decision based on historical data, but In case of Machine Learning , Businesses predict the future based on the historical data. Example, It’s a difference between what happened to the business vs what will happen to the business.Its like making BI much smarter and scalable so that it can predict future rather than just showing the state of the business.
  • **ML is based on Standard algorithms which are used to create use case specific model based on the data **. For example we can build the model to predict delivery time of the food, or we can build the model to predict the Delinquency rate in Finance business , but to build these model algorithm might be similar but the training would be different.Model training requires tones of examples (data).
  • Basically you train your standard algorithm with your Input data. So algorithms are always same but trained models are different based on use cases. Your trained model will be as good as your data.

ML, AI , Deep learning ? What is the difference?

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ML is type of AI

AI is a discipline , Machine Learning is tool set to achieve AI. DL is type of ML when data is unstructured like image, speech , video etc.

Barrier to Entry Has Fallen

AI & ML was daunting and with high barrier to entry until cloud become more robust and natural AI platform. Entry barrier to AI & ML has fallen significantly due to

  • Increasing availability in data (big data).
  • Increase in sophistication in algorithm.
  • And availability of hardware and software due to cloud computing.

GCP Machine Learning Spectrum

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  • For Data scientist and ML experts , TensorFlow on AI platform is more natural choice since they will build their own custom ML models.
  • But for the users who are not experts will potentially use Cloud AutoML or Pre-trained ready to go model.
  • In case of AutoML we can trained our custom model with Google taking care of much of the operational tasks.
  • Pre-trained models are the one which are already trained with tones of data and ready to be used by users to predict on their test data.

Prebuilt ML Models (No ML Expertise Needed)

  • As discuss earlier , GCP has lot of Prebuilt models that are ready to use to solve common ML task . Such as image classification, Sentiment analysis.
  • Most of the businesses are having many unstructured data sources such as e-mail, logs, web pages, ppt, documents, chat, comments etc.( 90% or more as per various studies)
  • Now to process these unstructured data in the form of text, we should use Cloud Natural Language API.
  • Similarly For common ML problems in the form of speech, video, vision we should use respective Prebuilt models.

#ml-guide-on-gcp #ml-for-beginners-on-gcp #beginner-ml-guide-on-gcp #machine-learning #machine-learning-gcp #deep learning

sophia tondon

sophia tondon

1620898103

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.

#machinelearningapps #machinelearningdevelopers #machinelearningexpert #machinelearningexperts #expertmachinelearningservices #topmachinelearningcompanies #machinelearningdevelopmentcompany

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

#machine learning companies #top machine learning companies #machine learning development company #expert machine learning services #machine learning experts #machine learning expert

Jackson  Crist

Jackson Crist

1617331066

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