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Learn how to avoid sudden layout shifts to improve user-experience

“I was about to click that! Why did it move? 😭”

Layout shifts can be distracting to users. Imagine you’ve started reading an article when all of a sudden elements shift around the page, throwing you off and requiring you to find your place again. This is very common on the web, including when reading the news, or trying to click those ‘Search’ or ‘Add to Cart’ buttons. Such experiences are visually jarring and frustrating. They’re often caused when visible elements are forced to move because another element was suddenly added to the page or resized.

Cumulative Layout Shift (CLS) - a Core Web Vitals metric, measures the instability of content by summing shift scores across layout shifts that don’t occur within 500ms of user input. It looks at how much visible content shifted in the viewport as well as the distance the elements impacted were shifted.

In this guide, we’ll cover optimizing common causes of layout shifts.

The most common causes of a poor CLS are:

- Images without dimensions
- Ads, embeds, and iframes without dimensions
- Dynamically injected content
- Web Fonts causing FOIT/FOUT
- Actions waiting for a network response before updating DOM

#web-development

1625061420

Collection of free hand-picked simple CSS grid examples. Also, it includes a bunch of front-end techniques, tips, and tricks for your future reference. Hope you will like these freebies and find them useful. Happy coding!

- Styling the last row of a grid with CSS selectors
- Grid Animation Effects
- Simple grid mixin
- Simple Grid CSS Grid
- Simple CSS Grid Hover
- Simple css Grid – Responsive
- Simple css grid system using scss
- CSS variables simple CSS grid
- Super Simple CSS Grid
- 3D Grid UI
- Aspect ratio Grid boxes with CSS Variables
- Simple grid system
- Simple Grid template

#layouts #css grid #grid #layouts #css #css grid layout

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In the 1940s, mathematical programming was synonymous with optimization. An optimization problem included an **objective function** that is to be maximized or minimized by choosing **input values** from an **allowed set of values** [1].

Nowadays, optimization is a very familiar term in AI. Specifically, in Deep Learning problems. And one of the most recommended optimization algorithms for Deep Learning problems is ** Adam**.

*Disclaimer: basic understanding of neural network optimization. Such as Gradient Descent and Stochastic Gradient Descent is preferred before reading.*

- Definition of Adam Optimization
- The Road to Adam
- The Adam Algorithm for Stochastic Optimization
- Visual Comparison Between Adam and Other Optimizers
- Implementation
- Advantages and Disadvantages of Adam
- Conclusion and Further Reading
- References

The Adam algorithm was first introduced in the paper **Adam: A Method for Stochastic Optimization** [2] by Diederik P. Kingma and Jimmy Ba. Adam is defined as “a method for efficient **stochastic optimization** that **only requires first-order gradients** with little memory requirement” [2]. Okay, let’s breakdown this definition into two parts.

First, **stochastic optimization** is the process of optimizing an objective function in the presence of *randomness*. To understand this better let’s think of Stochastic Gradient Descent (SGD). SGD is a great optimizer when we have a lot of data and parameters. Because at each step SGD calculates an estimate of the gradient from a *random subset of that data* (mini-batch). Unlike Gradient Descent which considers the entire dataset at each step.

#machine-learning #deep-learning #optimization #adam-optimizer #optimization-algorithms

1624496700

While writing code and algorithms you should consider tail call optimization (TCO).

The tail call optimization is the fact of optimizing the recursive functions in order to avoid building up a tall **call stack**. You should as well know that some programming languages are doing tail call optimizations.

For example, Python and Java decided to don’t use TCO. While JavaScript allows to use TCO since ES2015-ES6.

Even if you know that your favorite language support natively TCO or not, I would definitely recommend you to assume that your compiler/interpreter will not do the work for you.

There are two famous methods to do a tail call optimization and avoid tall call stacks.

As you know recursions are building up the call stack so if we avoid such recursions in our algorithms it will will allow us to save on the **memory usage**. This strategy is called the **bottom-up** (we start from the beginning, while a recursive algorithm starts from the end after building a stack and works backwards.)

Let’s take an example with the following code (top-down — recursive code):

```
function product1ToN(n) {
return (n > 1) ? (n * product1ToN(n-1)) : 1;
}
```

As you can see this code has a problem: it builds up a **call stack** of size O(n), which makes our total memory cost O(n). This code makes us vulnerable to a **stack overflow error**, where the call stack gets too big and runs out of space.

In order to optimize our example we need to go bottom-down and remove the recursion:

```
function product1ToN(n) {
let result = 1;
for (let num = 1; num <= n; num++) {
result *= num;
}
return result;
}
```

This time we are not stacking up our calls in the **call stack**, and we do use a O(1) space complexity(with a O(n) time complexity).

#memoization #programming #algorithms #optimization #optimize

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So far in our journey through the Machine Learning universe, we covered several big topics. We investigated some **regression** algorithms, **classification** algorithms and algorithms that can be used for both types of problems (**SVM****, ****Decision Trees** and Random Forest). Apart from that, we dipped our toes in unsupervised learning, saw how we can use this type of learning for **clustering** and learned about several clustering techniques.

We also talked about how to quantify machine learning model **performance** and how to improve it with **regularization**. In all these articles, we used Python for “from the scratch” implementations and libraries like **TensorFlow**, **Pytorch** and SciKit Learn. The word optimization popped out more than once in these articles, so in this and next article, we focus on optimization techniques which are an important part of the machine learning process.

In general, every machine learning algorithm is composed of three integral parts:

- A
**loss**function. - Optimization criteria based on the loss function, like a
**cost**function. **Optimization**technique – this process leverages training data to find a solution for optimization criteria (cost function).

As you were able to see in previous articles, some algorithms were created intuitively and didn’t have optimization criteria in mind. In fact, mathematical **explanations** of why and how these algorithms work were done later. Some of these algorithms are **Decision Trees** and **kNN**. Other algorithms, which were developed later had this thing in mind beforehand. **SVM**is one example.

During the training, we change the parameters of our machine learning model to try and **minimize** the loss function. However, the question of how do you change those parameters arises. Also, by how much should we change them during training and when. To answer all these questions we use **optimizers**. They put all different parts of the machine learning algorithm together. So far we mentioned **Gradient Decent** as an optimization technique, but we haven’t explored it in more detail. In this article, we focus on that and we cover the **grandfather** of all optimization techniques and its variation. Note that these techniques are **not** machine learning algorithms. They are solvers of **minimization** problems in which the function to minimize has a gradient in most points of its domain.

Data that we use in this article is the famous *Boston Housing Dataset* . This dataset is composed 14 features and contains information collected by the U.S Census Service concerning housing in the area of Boston Mass. It is a small **dataset** with only 506 samples.

For the purpose of this article, make sure that you have installed the following _Python _libraries:

- **NumPy **– Follow
**this guide**if you need help with installation. - **SciKit Learn **– Follow
**this guide**if you need help with installation. **Pandas**– Follow**this guide**if you need help with installation.

Once installed make sure that you have imported all the necessary modules that are used in this tutorial.

```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import SGDRegressor
```

Apart from that, it would be good to be at least familiar with the basics of **linear algebra**, **calculus** and **probability**.

Note that we also use simple **Linear Regression** in all examples. Due to the fact that we explore **optimization**techniques, we picked the easiest machine learning algorithm. You can see more details about Linear regression **here**. As a quick reminder the formula for linear regression goes like this:

where *w* and *b* are parameters of the machine learning algorithm. The entire point of the training process is to set the correct values to the *w* and *b*, so we get the desired output from the machine learning model. This means that we are trying to make the value of our **error vector** as small as possible, i.e. to find a **global minimum of the cost function**.

One way of solving this problem is to use calculus. We could compute derivatives and then use them to find places where is an extrema of the cost function. However, the cost function is not a function of one or a few variables; it is a function of all parameters of a machine learning algorithm, so these calculations will quickly grow into a monster. That is why we use these optimizers.

#ai #machine learning #python #artificaial inteligance #artificial intelligence #batch gradient descent #data science #datascience #deep learning #from scratch #gradient descent #machine learning #machine learning optimizers #ml optimization #optimizers #scikit learn #software #software craft #software craftsmanship #software development #stochastic gradient descent

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A simple guide to fixing cumulative layout shift (CLS) in WordPress which is currently 15% of core web vital scores.

Cumulative layout shift is when things “shift” on your website and can be seen in Google’s Cumulative Layout Shift Debugger (https://webvitals.dev/cls). CLS issues are often caused by fonts, asynchronous CSS (i.e. Optimize CSS Delivery in WP Rocket), not setting a fallback critical CSS, elements without specified dimensions, and animations that don’t use transform/translate properties.

Written article: https://onlinemediamasters.com/cumulative-layout-shift-wordpress/

0:00 - Intro

1:36 - Ensure text remains visible during webfont load

4:52 - Preload fonts

5:46 - Disable asynchronous CSS

7:28 - Specify dimensions

8:14 - Use CSS Transform property in animations

Like and subscribe if you found this helpful :)

Peace out,

Tom

#wordpress #cumulative #google