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This article was published as a part of the Data Science Blogathon


Human vision is lovely and complex. It all commenced billions of ages ago when tiny organisms developed a mutation that made them sensitive to light.

Fast forward to today, and life is abundant on the planet, which all have very similar visual systems. They include eyes for capturing light, receptors in the brain for accessing it, and visual cortex processing.

Genetically engineered and balanced pieces of a system help us do things as simple as appreciating a sunrise. But this is just the beginning.

In the past 30 years, we’ve made even more strides to extending this unique visual ability, not just to us but to computers as a whole.

A little bit of History

The first type of the photographic camera invented around 1816 was a small box that held a piece of paper coated with silver chloride. When the shutter was open, the silver chloride would darken when exposed to light.

Understanding what’s in the photo is much more difficult.

Consider this picture below:

Our human brain can look at it and immediately know that it’s a flower. Our brains are tricking since we’ve got several million years’ worths of evolutionary context to directly understand what is better.


There’s no connection here, just a massive quantity of data. It turns out that context is the crux of getting algorithms to understand image content in the same way that the human brain does.

And to get this work, we practice an algorithm very comparable to how the human brain functions adopting machine learning. Machine learning enables us to adequately train the context for data so that an algorithm can learn what all these numbers in a particular group serve.

And what if we have images that are difficult for a human to classify? Can machine learning achieve better accuracy?

For example, let’s take a look at these images of sheepdogs and mops where it’s pretty hard, even for us, to differentiate between the two.


With the machine learning model, we can get a collection of images of sheepdogs and mops. Then, as deep as we feed it sufficient data, it will ultimately tell the variation among the two accurately.

Computer vision is taking on increasingly complex challenges and is seeing accuracy that rivals humans are performing the same image recognition tasks.

But like humans, these models aren’t perfect. So they do sometimes make mistakes. The specific type of neural network that accomplishes this is called a convolutional neural network or CNN.

Role of Convolutional Neural Networks in Computer Vision


CNN operates by dividing a picture down into more petite groups of

pixels called a filter. Every filter is a matrix of pixel values. Then, the network performs calculations on these pixels, comparing them against pixels in a specific pattern the network is looking at.

In the initial layer of a CNN, it can recognize high-level patterns like uneven edges and sweeps. Then, as the network functions more convolutions, it can classify different things like faces and animals.

How does a CNN know what to look for and if its prediction is accurate?

A large amount of labeled training data helps in the process. When the CNN starts, all of the filter values are randomized. As a decision, its first predictions present slight sense.

Each time the CNN predicts labeled data, it uses an error function to compare how close its forecast was to the image’s actual label. Based on this error or loss function, the CNN updates its filter values and starts the process again. Ideally, each iteration performs with slightly more accuracy.

What if we want to explore a video using machine learning instead of analyzing a single image?

At its essence, a video is just a sequence of picture frames. To analyze footage, we can build on our CNN for image analysis. In noiseless pictures, we can apply CNNs to recognize features.

But when we shift to video, everything gets more complicated as the items we’re recognizing might evolve overhead time. Or, more likely, there’s a context between the video frames that’s highly important to labeling.

For example, if there’s a picture of a half-full cardboard box, we might want to label it packing a box or unpacking a box depending on the frames before and after it.


Now is when CNN’s come up lacking. They can take into spatial report characteristics, the visual data in a picture, but

can’t manipulate temporal or time features like how a frame is

similar to the one before it.

To address this issue, we have to take the output of our CNN and feed it into another model that can handle our videos’ temporal nature described as a recurrent neural network or RNN.

Role Of Recurrent Neural Networks in Computer Vision


While a CNN treats groups of pixels independently, an RNN can retain information about its already processed and use that in its decision-making.

RNNs can manage various sorts of input and output data. An example of classifying videos, we train the RNN by passing it a sequence of frame descriptions

-empty box

-open box

-closing box

And finally, a label- packing.

As the RNN processes a specific sequence, it practices a loss or error function to match its predicted output amidst the correct label. Then it adjusts the weights and processes the series again until it achieves higher accuracy.

However, the challenge of these approaches to image and video models is that the amount of data we need to mimic human vision is tremendous.

If we train our model to analyze this photo of a duck, as long

as we’re given this one picture with this lighting, color, angle, and shape, we can see that it’s a duck.

Change any of that or even rotate the duck; the algorithm might not understand what it is anymore. So now, this is the signature design problem.

To get an algorithm to truly understand and recognize image content the way the human brain does, you need to feed it substantial amounts of data of millions of objects across thousands of angles, all annotated and adequately defined.

The problem is so big that if you’re a small startup or a

company lean on funding, there are just no resources available for you to do that.

Note: Consequently, technologies like Google Cloud Vision and Video can help. Google understands and filters millions of photographs and videos to train specific APIs. They introduced a network to extract all kinds of data from images and video so that your application doesn’t have to. With just one REST API request, they can access a powerful pre-trained model that gives us all sorts of data.


Billions of years since the evolution of our sense of sight, we found that computers are on their way to matching human vision. Computer vision is an innovative track that practices the latest machine learning technologies to construct software systems that help humans beyond complex areas. From retail to wildlife preservation, intelligent algorithms unlock image classification and pattern recognition problems, sometimes even thoroughly than humans.

About Author

Mrinal Walia is a professional Python Developer with a Bachelors’s degree in computer science specializing in Machine Learning, Artificial Intelligence and Computer Vision. Mrinal is also an interactive blogger, author, and geek with over four years of experience in his work. With a background working through most areas of computer science, Mrinal currently works as a Testing and Automation Engineer at Versa Networks, India. My aim to reach my creative goals one step at a time, and I believe in doing everything with a smile.

The media shown in this article are not owned by Analytics Vidhya and are used at the Author’s discretion.


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Synthetic Data For Computer Vision: Benefits & Examples In 2023

Advancements in deep learning techniques have paved the way for successful computer vision and image recognition applications in fields such as automotive, healthcare, and security. Computers that can derive meaningful information from visual data enable numerous applications such as self-driving cars and highly accurate detection of diseases.

The challenge with deep neural networks and their applications in computer vision is that these algorithms require large, correctly labeled datasets for better accuracy. Collecting and annotating significant amounts of high-quality photos and videos to train a deep learning model is time-consuming and expensive.

Synthetic (i.e., artificially generated) images and videos can solve both the collection and annotation problems of working with visual data.

How can synthetic data help computer vision? Enables creating datasets faster and cheaper

Collecting real-world visual data with desired characteristics and diversity can be prohibitively expensive and time-consuming. After collection, annotating data points with correct labels is crucial because mislabeled data would lead to inaccurate model outcomes. These processes can take months and consume valuable business resources.

Synthetic data is generated programmatically which means it does not require manual data collection efforts and it can contain nearly perfect annotations. The image below by Unity demonstrates the difference between computer vision projects with real data and synthetic data. Unity states that they created a better model while saving about 95% in both time and money.

Enables rare event prediction

Datasets collected from real-world are often imbalanced which means some events are rarer than others. However, this does not mean they are negligible. For example, the computer vision system of a self-driving car that learns from road events may lack enough examples of car accidents because collecting visual data for it is difficult. Rare diseases or counterfeit money are some other examples of rare events that can be encountered in computer vision applications.

Instead, training deep learning algorithms of self-driving cars with synthetic images or videos of car accidents under a diverse set of circumstances (different times of day, number of vehicles, types of vehicles, number of pedestrians, environment, etc.) can enable safer and more reliable autonomous vehicles.

Thus, synthetic data offers a way to generate datasets that represent the diversity of real-world events more accurately.

Prevents data privacy problems

Collecting and storing visual data is also challenging because of data privacy regulations such as GDPR. Non-compliance with such regulations can lead to serious fines and damage business reputation. Working with datasets that contain sensitive information has its risks because data breaches can occur even through model outcomes. For example, researchers managed to extract recognizable face images from the training set with only API access to the facial recognition system and person’s name.

Synthetic data eliminates the risks of privacy violations because a synthetic dataset would not contain information about real persons while preserving the important characteristics of a real dataset.

What are some case studies?

Caper is a startup making intelligent shopping carts that enable customers to shop without waiting in checkout line. Image recognition model deployed in their shopping carts requires 100 to 1000 images for each item and there can be thousands of different items in a store. Caper used synthetic images of store items that capture different angles and trained the deep learning algorithm with it. The company states that their shopping carts have 99% recognition accuracy.

NVIDIA created a robotics simulation application and synthetic data generation tool called Isaac Sim for developing, testing, and managing AI-based robots working in real world.

Training an object detector with synthetic images containing random objects and non-realistic scenes is showed to improve deep neural network model performance. The technique is called domain randomization and researchers conclude that the real world may appear to the model as just another variation. The object detector could locate physical objects in a cluttered environment with 1.5 cm accuracy.

If you want to learn more about synthetic data and its applications, check our other articles on the topic:

If you are looking for synthetic data generation software, check our data-driven, sortable/filterable list of vendors.

If you still have questions about synthetic data, do not hesitate to contact us:

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.





Getting Started With The Basic Tasks Of Computer Vision

This article was published as a part of the Data Science Blogathon

If you are interested or planning to do anything which is related to images or videos, you should definitely consider using Computer Vision. Computer Vision (CV) is a branch of artificial intelligence (AI) that enables computers to extract meaningful information from images, videos, and other visual inputs and also take necessary actions. Examples can be self-driving cars, automatic traffic management, surveillance, image-based quality inspections, and the list goes on. 

What is OpenCV?

OpenCV is a library primarily aimed at computer vision. It has all the tools that you will need while working with Computer Vision (CV). The ‘Open’ stands for Open Source and ‘CV’ stands for Computer Vision.

What will I learn?

The article contains all you need to get started with computer vision using the OpenCV library. You will feel more confident and more efficient in Computer Vision. All the code and data are present here.

Reading and displaying the images

First let’s understand how to read the image and display it, which is the basics of CV.

Reading the Image:

import numpy as np import cv2 as cv import matplotlib.pyplot as plt img=cv2.imread('../input/images-for-computer-vision/tiger1.jpg')

The ‘img’ contains the image in the form of a numpy array. Let’s print its type and shape,

print(type(img)) print(img.shape)

The numpy array has a shape of (667, 1200, 3), where,

667 – Image height, 1200 – Image width, 3 – Number of channels,

Displaying the Image:

# Converting image from BGR to RGB for displaying img_convert=cv.cvtColor(img, cv.COLOR_BGR2RGB) plt.imshow(img_convert) Drawing over Image

We can draw lines, shapes, and text an image.

# Rectangle color=(240,150,240) # Color of the rectangle cv.rectangle(img, (100,100),(300,300),color,thickness=10, lineType=8) ## For filled rectangle, use thickness = -1 ## (100,100) are (x,y) coordinates for the top left point of the rectangle and (300, 300) are (x,y) coordinates for the bottom right point # Circle color=(150,260,50), (650,350),100, color,thickness=10) ## For filled circle, use thickness = -1 ## (250, 250) are (x,y) coordinates for the center of the circle and 100 is the radius # Text color=(50,200,100) font=cv.FONT_HERSHEY_SCRIPT_COMPLEX cv.putText(img, 'Save Tigers',(200,150), font, 5, color,thickness=5, lineType=20) # Converting BGR to RGB img_convert=cv.cvtColor(img, cv.COLOR_BGR2RGB) plt.imshow(img_convert)

Blending Images

We can also blend two or more images with OpenCV. An image is nothing but numbers, and you can add, subtract, multiply and divide numbers and thus images. One thing to note is that the size of the images should be the same.

# For plotting multiple images at once def myplot(images,titles): fig, axs=plt.subplots(1,len(images),sharey=True) fig.set_figwidth(15) for img,ax,title in zip(images,axs,titles): if img.shape[-1]==3: else: img=cv.cvtColor(img, cv.COLOR_GRAY2BGR) ax.imshow(img) ax.set_title(title) img1 = cv.imread('../input/images-for-computer-vision/tiger1.jpg') img2 = cv.imread('../input/images-for-computer-vision/horse.jpg') # Resizing the img1 img1_resize = cv.resize(img1, (img2.shape[1], img2.shape[0])) # Adding, Subtracting, Multiplying and Dividing Images img_add = cv.add(img1_resize, img2) img_subtract = cv.subtract(img1_resize, img2) img_multiply = cv.multiply(img1_resize, img2) img_divide = cv.divide(img1_resize, img2) # Blending Images img_blend = cv.addWeighted(img1_resize, 0.3, img2, 0.7, 0) ## 30% tiger and 70% horse myplot([img1_resize, img2], ['Tiger','Horse']) myplot([img_add, img_subtract, img_multiply, img_divide, img_blend], ['Addition', 'Subtraction', 'Multiplication', 'Division', 'Blending'])

The multiply image is almost white and the division image is black, this is because white means 255 and black means 0. When we multiply two-pixel values of the images, we get a higher number, so its color becomes white or close to white and opposite for the division image.

Image Transformation

Image transformation includes translating, rotating, scaling, shearing, and flipping an image.

img=cv.imread('../input/images-for-computer-vision/tiger1.jpg') width, height, _=img.shape # Translating img_translate=cv.warpAffine(img,M_translate,(height,width)) # Rotating center=(width/2,height/2) M_rotate=cv.getRotationMatrix2D(center, angle=90, scale=1) img_rotate=cv.warpAffine(img,M_rotate,(width,height)) # Scaling scale_percent = 50 width = int(img.shape[1] * scale_percent / 100) height = int(img.shape[0] * scale_percent / 100) dim = (width, height) img_scale = cv.resize(img, dim, interpolation = cv.INTER_AREA) # Flipping img_flip=cv.flip(img,1) # 0:Along horizontal axis, 1:Along verticle axis, -1: first along verticle then horizontal # Shearing srcTri = np.array( [[0, 0], [img.shape[1] - 1, 0], [0, img.shape[0] - 1]] ).astype(np.float32) dstTri = np.array( [[0, img.shape[1]*0.33], [img.shape[1]*0.85, img.shape[0]*0.25], [img.shape[1]*0.15, img.shape[0]*0.7]] ).astype(np.float32) warp_mat = cv.getAffineTransform(srcTri, dstTri) img_warp = cv.warpAffine(img, warp_mat, (height, width)) myplot([img, img_translate, img_rotate, img_scale, img_flip, img_warp], ['Original Image', 'Translated Image', 'Rotated Image', 'Scaled Image', 'Flipped Image', 'Sheared Image']) Image Preprocessing

Thresholding: In thresholding, the pixel values less than the threshold value become 0 (black), and pixel values greater than the threshold value become 255 (white).

I am taking the threshold to be 150, but you can choose any other number as well.

# For visualising the filters import plotly.graph_objects as go from plotly.subplots import make_subplots def plot_3d(img1, img2, titles): fig = make_subplots(rows=1, cols=2, specs=[[{'is_3d': True}, {'is_3d': True}]], subplot_titles=[titles[0], titles[1]], ) x, y=np.mgrid[0:img1.shape[0], 0:img1.shape[1]] fig.add_trace(go.Surface(x=x, y=y, z=img1[:,:,0]), row=1, col=1) fig.add_trace(go.Surface(x=x, y=y, z=img2[:,:,0]), row=1, col=2) fig.update_traces(contours_z=dict(show=True, usecolormap=True, highlightcolor="limegreen", project_z=True)) img=cv.imread('../input/images-for-computer-vision/simple_shapes.png') # Pixel value less than threshold becomes 0 and more than threshold becomes 255 _,img_threshold=cv.threshold(img,150,255,cv.THRESH_BINARY) plot_3d(img, img_threshold, ['Original Image', 'Threshold Image=150'])

After applying thresholding, the values which are 150 becomes equal to 255

Filtering: Image filtering is changing the appearance of an image by changing the values of the pixels. Each type of filter changes the pixel value based on the corresponding mathematical formula. I am not going into detail math here, but I will show how each filter work by visualizing them in 3D. If you are interested in the math behind the filters, you can check this.

img=cv.imread('../input/images-for-computer-vision/simple_shapes.png') # Gaussian Filter ksize=(11,11) # Both should be odd numbers img_guassian=cv.GaussianBlur(img, ksize,0) plot_3d(img, img_guassian, ['Original Image','Guassian Image']) # Median Filter ksize=11 img_medianblur=cv.medianBlur(img,ksize) plot_3d(img, img_medianblur, ['Original Image','Median blur']) # Bilateral Filter img_bilateralblur=cv.bilateralFilter(img,d=5, sigmaColor=50, sigmaSpace=5) myplot([img, img_bilateralblur],['Original Image', 'Bilateral blur Image']) plot_3d(img, img_bilateralblur, ['Original Image','Bilateral blur'])

Gaussian Filter: Blurring an image by removing the details and the noise. For more details, you can read this.

Bilateral Filter: Edge-preserving, and noise-reducing smoothing.

In simple words, the filters help to reduce or remove the noise which is a random variation of brightness or color, and this is called smoothing.

Feature Detection

Feature detection is a method for making local decisions at every image point by computing abstractions of image information. For example, for an image of a face, the features are eyes, nose, lips, ears, etc. and we try to identify these features.

Let’s first try to identify the edges of an image.

Edge Detection img=cv.imread('../input/images-for-computer-vision/simple_shapes.png') img_canny1=cv.Canny(img,50, 200) # Smoothing the img before feeding it to canny filter_img=cv.GaussianBlur(img, (7,7), 0) img_canny2=cv.Canny(filter_img,50, 200) myplot([img, img_canny1, img_canny2], ['Original Image', 'Canny Edge Detector(Without Smoothing)', 'Canny Edge Detector(With Smoothing)'])

Here we are using the Canny edge detector which is an edge detection operator that uses a multi-stage algorithm to detect a wide range of edges in images. It was developed by John F. Canny in 1986. I am not going in much details of how Canny works, but the key point here is that it is used to extract the edges. To know more about its working, you can check this.

Before detecting an edge using the Canny edge detection method, we smooth the image to remove the noise. As you can see from the image, that after smoothing we get clear edges.

Contours img=cv.imread('../input/images-for-computer-vision/simple_shapes.png') img_copy=img.copy() img_gray=cv.cvtColor(img,cv.COLOR_BGR2GRAY) _,img_binary=cv.threshold(img_gray,50,200,cv.THRESH_BINARY) #Edroing and Dilating for smooth contours img_binary_erode=cv.erode(img_binary,(10,10), iterations=5) img_binary_dilate=cv.dilate(img_binary,(10,10), iterations=5) contours,hierarchy=cv.findContours(img_binary,cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE) cv.drawContours(img, contours,-1,(0,0,255),3) # Draws the contours on the original image just like draw function myplot([img_copy, img], ['Original Image', 'Contours in the Image'])

Erode The erosion operation that uses a structuring element for probing and reducing the shapes contained in the image.

Dilation: Adds pixels to the boundaries of objects in an image, simply opposite of erosion

Hulls img=cv.imread('../input/images-for-computer-vision/simple_shapes.png',0) _,threshold=cv.threshold(img,50,255,cv.THRESH_BINARY) contours,hierarchy=cv.findContours(threshold,cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE) hulls=[cv.convexHull(c) for c in contours] img_hull=cv.drawContours(img, hulls,-1,(0,0,255),2) #Draws the contours on the original image just like draw function plt.imshow(img) Summary

We saw how to read and display the image, drawing shapes, text over an image, blending two images, transforming the image like rotating, scaling, translating, etc., filtering the images using Gaussian blur, Median blur, Bilateral blur, and detecting the features using Canny edge detection and finding contours in an image.

I tried to scratch the surface of the computer vision world. This field is evolving each day but the basics will remain the same, so if you try to understand the basic concepts, you will definitely excel in this field.

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Cooler Master Mk850 Review: Don’t Throw Away Your Controller Just Yet

The reality? Let’s take a look.

Note: This review is part of our 

best gaming keyboards

roundup. Go there for details about competing products and how we tested them.

Before we get into Aimpad, it’s worth noting that Cooler Master’s designed a relatively unique-looking keyboard in the MK850. No mean feat, in my opinion.

Two elements help the MK850 stand out. First off, it’s octagonal. Only slightly, mind you, but lopping the corners off certainly gives the MK850 a look. I can’t say it’s my favorite styling, especially where the keyboard and wrist rest meet. There’s a triangle of negative space at those intersections that just looks…weird. But it’s recognizable, at least.

IDG / Hayden Dingman

The other unique element: Not one but two volume rollers, and they’re arrayed in the middle of the keyboard instead of relegated to the right edge. I called them both volume rollers because that’s what I associate that style of wheel with, but only the right-hand roller deserves that label. The other controls the brightness of the MK850’s backlight by default.

It looks neat, though unfortunately panache comes at the expense of usability. Both the rollers and a set of standalone media keys are tucked down behind the keyboard’s Function row. It makes accessing them way more difficult than if they were in the standard top-right spot, which has more clearance—and is easier to find without looking, as well.

IDG / Hayden Dingman

Still, the MK850 looks pretty decent sitting on a desk, even if somewhat oversized and overdesigned. I’m a big fan of the slim typeface Cooler Master uses on its keycaps, and the backlighting is bright and uniform. The wrist rest is fantastic as well. We’ve officially entered an era where every flagship keyboard ships with a plush leatherette-bound wrist rest, and I love it. (Kudos to Razer for starting this trend.)

Take it easy

Case in point: The reason the media keys are in the center is because the Aimpad controls take the top-right spot. There you’ll find three buttons. One toggles Aimpad on and off. The others increase and decrease the sensitivity of Aimpad, just as you would adjust the dead zones on analog sticks.

IDG / Hayden Dingman

As I said, plenty of games can handle both without a hitch. Most change the controls that display on-screen depending on your input device. Thus the “A” button on an Xbox controller might correspond to the “E” key on your keyboard. You don’t really notice or even need to know this usually, because you choose an input and stick to it.

IDG / Hayden Dingman

It’s a cool proof of concept, and if properly supported by developers I could see analog keyboard tech taking off. A proper controller and analog stick is still preferable for some games I think, but racing games and the like are way more playable on the MK850 than on standard keyboards.

IDG / Hayden Dingman

The MK850 ships with Aimpad disabled. Thus your first step out of the box is to hit the button in the top-right to turn it on. You then need to calibrate all eight keys—Q, W, E, R, A, S, D, and F—by holding down each key until a corresponding LED turns from green to red. Once all eight are calibrated, you’ll be in Aimpad mode.

Or…well, sort-of. As I said, games aren’t built to support Aimpad. Whenever it’s enabled? The corresponding keyboard inputs are disabled.

Put simply, if you’re using one of the Aimpad profiles you won’t be able to type using the aforementioned WASD block. The rest of your keyboard works as normal, but those keys don’t. Thus Cooler Master includes a profile without Aimpad functionality enabled so you can type.

IDG / Hayden Dingman

Regardless, it’s not hard to enable or disable Aimpad. You don’t need to go through that whole calibration routine again each time, so long as you just use the profiles to swap instead of turning off Aimpad entirely.

But again, it feels like a hack. I’ve found it frustrating, Alt-Tabbing out of a game to chat with friends only to realize midway through a message that eight keys are still disabled. Or rather: “I’v foun it utting.” You can’t even read that final word as “frustrating,” it’s so butchered. I’ve found it just as annoying going the other direction as well, trying to remember to turn my analog controls back on every time I launch a game. That’s not a great user experience.

There’s an aesthetic concern as well. In order for Aimpad to work, those eight keys need to have full LED brightness enabled. Or at least that’s what I assume is happening, because no matter what you set the rest of your keyboard to, that one block will always display bright white. It’s an ugly compromise.

Bottom line

Refresh Your Browser On Windows And Mac In A Few Steps

Refresh Your Browser on Windows and Mac in a Few Steps Different methods to refresh your favorite browser on various devices




Refreshing pages are a great way to fix all sorts of problems in your current web browser.

There are several ways to refresh your browser and pages on various devices.

Your cache can cause various problems, so we will show you how to refresh them too.

Struggling with various browser issues? Try a better option: Opera One

You deserve a better browser! Over 300 million people use Opera One daily, a fully-fledged navigation experience coming with various built-in packages, enhanced resource consumption, and great design.

Here’s what Opera One can do:

Optimize resource usage: Opera One uses your Ram more efficiently than Brave

AI and User Friendly: New feature directly accessible from the sidebar

Gaming friendly: Opera GX is the first and best browser for gamers

⇒ Get Opera One

The best browsers sometimes encounter problems, as there is no perfect app. One of the quick ways to fix most of these issues is to refresh your browser.

While refreshing a browser is incredibly simple, you still need the right information to do it across different browsers.

This guide will show you the simple methods to apply on all major browsers across different devices. We all also have extra bits added to help spice things up.

Quick Tip:

Opera One is built on the Chromium engine, so it’s quite similar in terms of functionality, and it can also work with Chrome extensions, so you won’t miss the features you love.

Opera One

Install Opera One, and rest assured that refreshing it is incredibly simple!

Free Visit Website

What does a browser refresh mean?

Refreshing pages on your browsers mean that you want to load the most recent version of the page. This is a standard function in most modern browsers and is pretty easy to do.

You can also refresh your browser’s content by clearing the cache and cookies. With this, you can remove corrupt browser data and allow it to load new information.

How do I refresh my browser and clear my cache? 1. Refresh the Chrome browser

This is a bit different from how to refresh the Chrome browser on mobile, as you will need to tap the menu button before you can find the refresh icon.

1.1 Hard browser refresh

By performing a hard refresh, the cache for that page will be removed and created again.

1.2 Refresh browser keyboard shortcut

In Chrome, there are two shortcuts for refreshing:

Ctrl + R for normal refresh

Ctrl + Shift + R for hard refresh

1.4 Refresh browser cache on Chrome

Remember to restart Chrome after clearing the cache to sync the changes made.

2. Refresh the Firefox browser

With the steps above, you can refresh Firefox and get the updated version of the page you are loading.

Expert tip:

2.2 Refresh browser shortcut

Firefox browser shortcuts are the following:

F5 to refresh the page

Ctrl + F5 or Ctrl + Shift + R for hard refresh

2.3 Auto refresh in Firefox

To auto-refresh Firefox, you need to rely on extensions. You can download Tab Auto Refresh, Auto Reload Tab, and Tab Reloader from the Mozilla Addons Store.

2.4 Refresh cache on Firefox

Make sure you restart your browser before loading any page to sync the changes.

3. How to refresh the browser on Mac

The refresh process is almost identical if you use the Firefox or Chrome browser on your Mac laptop. On Safari, the refresh process is a bit different. So, we’ll focus on it in this section.

3.1. Refresh Safari 3.2. Hard browser refresh on Safari

3.2 Refresh shortcuts in Safari

Command + R for the normal refresh

Command + Option + R for hard refresh

3.3 Auto-refresh in Safari

Just like Chrome and Firefox, to auto-refresh pages in Safari, you need to use extensions such as Auto Refresh.

3.4 Refresh browser cache in Safari

Refreshing cache in Safari is simple. You need to use Command + Option + E shortcut.

Refreshing your browser and pages is quite simple. We have shown the different ways you can complete the task on other browsers in this guide. You now have everything you need to take control of your favorite browser refresh process.

If you need the list of best cookie cleaner software on PC to make the data clearing process faster, check our detailed guide for the top options to install today.

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Take A Few Blogging Hints From Keynote Speakers

What if your writing could have the captivating influence of a keynote speaker? Writing is little more than presenting with words, after all. It could be that writers aren’t learning from the speakers who captivate so well.

Earlier this month, I attended presentations by Chris Brogan, Mitch Joel, and Julien Smith when they came to town. If you aren’t familiar with them, they frequently headline for blogging/media conferences so naturally I was just as interested in how they presented as in what they presented.

All I can say is that these guys are like the Johnny Appleseeds of ideas. They’re like Bob Ross painting “happy little trees” (replace ‘trees’ with ‘epiphanies’ and give Bob an energy drink). They make it look easy and people can’t criticize them because they’re too busy being motivated and inspired. Is that what you’re doing with your writing?

I want to examine how they presented. I’m not going to tell you how you should work with these details to change your writing style because you’ll get exactly what you need with some thoughtful analysis and introspection. (And you’re more likely to change if you believe you thought of it yourself) Let the brains start storming.


Each of the speakers used PowerPoint. They didn’t try for any groundbreaking presentation format because they were comfortable with PowerPoint. It was, however, obvious that the PowerPoint deck was a tool and not a centerpiece for the presentations because (1) the slides were extremely light on text, (2) used no bullet points, and (3) didn’t use any sort of branded template, even though that would have been easy.

They rarely looked at their slides and never pointed to them. They kept the focus on themselves, as if to said, “I’m prepared. That PowerPoint is my slave. I am not slave to PowerPoint.” Big difference.

If you looked at their slides, you’d find no coherently predictable path from one slide to the next. They weren’t teaching a cooking show – they were teaching the most important, powerful points they had to make in front of our audience. Another big difference in format.


As previously mentioned, their slides were text-light and picture-heavy.

At least 80% of the slides in each deck was nothing but a full-sized picture; a representation –   something like whispering sheep, a burning ship, or a lemonade stand.

The images helped both represent and guide the narrative. Not all images were a perfect fit for the topic or talking point, but each image was captivating. It felt like the pictures were picked before the script was written.

Personal Storytelling

I’d formulize their storytelling frequency as a ratio:

3:2:1 (3X Past Experience Stories : 2X Current Experience : 1X Future Plans)

They purposely only told the best stories.

Again, it felt like they picked their best stories up-front, then created the content around them, rather than making a point, then offering a supporting story.

Business Examples

They used plenty of case studies, but used them in a unique way. Their business examples were great examples and I hadn’t heard most of them. They all flowed more like stories than case studies, making them all the more compelling. It’s hard to treat the story of a homeless man who does his own A/B testing when panhandling as just another case study.


These speakers, like all keynoters, are quoted all the time. Part of the reason they’re so “quotable” is that they quote a lot of other people.

To be more specific, they each quoted plenty of well-known media figures from memory (or at least we couldn’t tell the difference). Their incessant name-dropping did well to lend to their credibility and more importantly, their connectedness.

Occasionally, they turned a quote into a slide, but this was rare, and reserved for something monumental or too long to recount.


I was most impressed with the speakers’ use of statistics. Numbers are critical for almost any presentation because there are usually “number people” in the audience who need something to drool over.

When Chris, Joel, and Julien used stats, they hand-picked the very best stats – ones that started a buzz of instant chatter, regardless of the context. For example:

80 percent of first brand interactions occur in search results.

They gave most stats their own big, bold slide for emphasis. They didn’t pack multiple figures on the same slide.


If you’re at the end of the post and still wondering how it all applies to your writing and blogging, take another look. The way writers connect with readers is the same way that speakers connect with audiences. If you want to improve your writing, look to the speakers for some answers.

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