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At a packed Galaxy Unpacked 2023 event on February 20th, Samsung unveiled the Galaxy S10, the device to mark the tenth anniversary in the Galaxy S series. Thanks to the plenty of rumors and leaks, we already knew the 2023 S10 has at least three variants – the standard S10, the Plus variant, and a smaller S10e model, but as you may have heard, there is also a 5G variant.

On this page, we have everything you need to know about the Galaxy S10e, Galaxy S10, Galaxy S10+, and Galaxy S10 5G, be it their specs, features, software updates, problems and their solutions, tips and tricks of getting the most out of them, the best accessories, deals, firmware download, and so on.


Latest news

June 1, 2023: Samsung has a new software update for the international variants of the Galaxy S10e, S10, and S10+. The three, as part of the May 2023 security patch, are receiving the support for Night mode on the ultra-wide angle lens.

The dedicated Night mode feature was included in the update to April 2023 security patch, but the latest version is here to improve it. The same update also adds Live Focus to the telephoto lens, allowing users to capture closeup bokeh photos without moving physically. Of course, this update is limited to the S10 and S10+ since the S10e doesn’t have a third, telephoto lens.

You can catch more on this story here.

May 21, 2023: Reports coming in suggest Samsung is lining up a new Cardinal Red color variant for the Galaxy S10 and S10+ phones. Yes, this is just a new paint job and nothing else about the phones changes.

May 18, 2023: Samsung will be at the 2023 Summer Olympic Games in Tokyo Japan to shine on with the Galaxy S10+ Olympic Games Edition, more than a year since the original phone launched. It’s strange why Samsung would want to launch a dated phone in conjunction with such a huge sporting event, but hey, we don’t make the rules.

The phone has been launched in partnership with local carrier Docomo and will be sold in Prism White with a Tokyo 2023 Olympic Games logo on the back. Only 10,000 units will be produced and apparently, a pair of special Galaxy Buds featuring Galaxy Buds with the Tokyo 2023 logo on the case will be included.

April 19, 2023: It has been rumored before that Samsung was working on a dedicated Night mode for the camera with an end-of-April release date. Well, it appears that the update that started rolling out yesterday in Europe with April 2023 security patches also tags along the new dedicated Night mode feature.

With the update now rolling out in more markets across Europe and Asia where models SM-G970F, SM-G973F, and SM-G975F of the S10e, S10, and S10+ are sold, respectively, more people are now able to see the Night mode in their camera apps.

See the image below for an idea of how the new addition looks like.

April 18, 2023: Plenty of Galaxy S10e, S10, and S10+ users on Sprint have reported LTE connectivity issues with their units. Sprint has since rolled out two software updates, but the carrier says none of them was meant to fix these issues. Instead, they were meant to protect unaffected units from getting the same LTE issues.

Now, to cool things off, Sprint has confirmed that it will be replacing Galaxy S10e, S10, and S10+ units affected by these LTE issues, but there is a catch or perhaps two. Find out more about these catches and everything else about this program here.

Elsewhere in Europe, the Galaxy S10 series is receiving an update that introduces a dedicated Night mode in the camera app for taking better shots at night. This feature has been in the rumor mills for a while, but it now arrives as part of April 2023 security patches. More to this here.


Samsung Galaxy S smartphones never skimp on matters specs. With the S10, you are getting powerful hardware to match 2023 standards in just about every aspect, as seen below.

6.1-inch 19:9 QHD+ (3040×1440) Curved Dynamic AMOLED display

Qualcomm Snapdragon 855/Exynos 9820 processor


128GB or 512GB expandable storage, up to 512GB

Tri-lens main camera: 12MP (OIS, Dual Pixel AF, f/1.5-f/2.4 aperture) + 12MP (telephoto, f/2.4 aperture, OIS, Dual Pixel AF) + 16MP (super wide-angle, f/2.2 aperture)

10MP (f/1.9, Dual Pixel AF) front camera

3400mAh battery

Android 9 Pie with One UI

Extras: Bluetooth 5.0, USB-C, 3.5mm audio jack, fast wired and wireless charging, reverse wireless charging, Wi-Fi 6, IP68 dust and water resistance, AR Emoji, in-display fingerprint scanner, face recognition, heart rate sensor, etc.

For a quick rundown of the specs of the other three variants of the Galaxy S10, check out their respective pages below.

Also, check out this page: What is the difference between Galaxy S10, Galaxy S10 Plus and Galaxy S10e?

Galaxy S10 features

As pointed out earlier, Samsung Galaxy S10, S10e, and S10+ are feature-packed smartphones whose features are hardly put to the full test by most people. Usually, this is because some of them are hidden or are simply not common to every smartphone user.

Check out: Best Galaxy S10 features to know

To help you around, we’ve rounded up some of the features of the Galaxy S10 handsets, be it bad or good, and shared them via the links below:


The Galaxy S10 series is the current premium offering from Samsung and obviously commands equally premium price tags. But given their differences, their prices are also different, with the S10e coming in as the budget model whereas the S10 5G is the most premium model.

Without further ado, below are the prices of the Galaxy S10e, S10, S10+, and S10 5G. Note that for the latter, it’s based on Korean pricing, but we should get U.S. pricing pretty soon.

Deals and offers

Looking for the best bang for your buck? Well, several outlets have quite a number of good deals on the Galaxy S10 handsets and while these offers are not permanent, your timing might just be perfect.

That said, here are the latest deals on Samsung Galaxy S10 handsets and accessories:

Tips and tricks

Most people hardly use even half of the features they have at their disposal. It gets even more interesting when talking about a flagship phone like Samsung Galaxy S10, S10e, or S10+. For their prices, these phones not only pack in great hardware but also a ton of features that you might never know about.

To help you get the best out of either Galaxy S10 handset, check out the below tips and tricks:

The Galaxy S10 comes with Android 9 Pie preinstalled. There is Samsung’s One UI skin on top to add customized features over what AOSP offers, but to keep everything in check, regular software updates are inevitable.

Samsung has rolled out an update to improve the performance of fingerprint sensor and camera on its S10 devices. The update comes as ASD3 build, so be sure to check for the update on your S10.

To keep an eye on all the software updates that each of these phones receives alongside the changes they come with, check out their respective software update pages below:

Firmware download

You may run into software issues on your Galaxy S10, S10e or S10+. Whether that be because of a bad app, or any customization you may have tried, fixing the software issues can be done by simply installing an older firmware file that worked fine before the upgrade. To do this, you need the stock ROM in question alongside a tutorial on how to go about it, if you don’t know already.

Below are links to each of the phones stock firmware download pages, where you also find guidelines of how to install the software.

Best Accessories

Check out some of the best accessories that are available for the Galaxy S10 handsets below.

Also, check out our coverage on some of the coolest gadgets you can buy for the Galaxy S10 here → Best accessories for Android.

Best screen protectors

With that expensive cutout display on top of your Galaxy S10 handset, you definitely need to protect while also making sure that the in-display fingerprint sensor works alright to.

Here are some suggestions that will help you buy a solid screen protector that protects your screen well.

Best Cases

Samsung Galaxy S10 devices are some of the most beautiful ones you can find out there. They are built from premium glass that is protected by Gorilla Glass, but this doesn’t make them unbreakable. For this, you need a great case that won’t take away their elegance, but if you need something different, say rugged, we got you covered, too.

To that end, below are all the cases and accessories you’ll need to get the most out of your Galaxy S10e, S10, or S10+.

Galaxy S10e cases

Galaxy S10 cases

Galaxy S10+ cases

Problems and solutions

Like every other phone, the Galaxy S10 family isn’t perfect. From time to time, users face problems here and there – problems that can be fixed via software updates or by applying certain tips and tricks shared in the links below.

Are you buying the Galaxy S10e, Galaxy S10, Galaxy S10+ or holding out for the Galaxy S10 5G?

You're reading Samsung Galaxy S10: All You Need To Know

Samsung Galaxy S4 Benchmarks: All You Need To Know

All the flagship Galaxy S smartphones released in the past three years have always had one thing in common – they’ve managed to beat every other competing flagship phone in almost every benchmark, thanks to Samsung’s efforts in developing better and more powerful chipsets each year.

This hasn’t changed with the Galaxy S4, Samsung’s latest and greatest flagship. The Galaxy S4 comes in two variants – one sports an 1.6GHz 8-core Exynos processor, while the other is powered by a Qualcomm Snapdragon 600 chipset clocked at 1.9GHz. And while previously only the Exynos variants have been able to come out on top in benchmarks, the Galaxy S4 has managed to beat the competition with both the Exynos and Snapdragon variants, in most of the popular benchmark apps and tools it has been subjected to.

If you’ve wondering about the benchmark scores of the Galaxy S4 and how it fares against the competition, we’ve got them all here in one place. So, let’s take a look.


Geekbench is a cross-platform benchmark that allows you to measure the processor’s power, and though each OS has a lot of factors attributing to its performance attributes, it gives a pretty good idea of where a device stands out. In Geekbench, the Snapdragon 600 variant was able to comfortably beat every other device except the HTC One, with the latter losing due to a lower clock speed. Apple’s iPhone 5, BlackBerry’s Z10, or even Google’s Nexus 4, they all failed to match the performance offered by the Galaxy S4.

For now, the Exynos variant of the S4 hasn’t been tested on Geekbench, so we’ll have to wait to see how that one fares in the test, though we can expect it to leave even the Snapdragon variant behind. Here go the Geekbench scores of the Galaxy S4, HTC One, and other competing devices.

Galaxy S4 3163

HTC One 2687

LG Nexus 4 2040

Galaxy S3 Exynos 1717

Apple iPhone 5 1569

BlackBerry Z10 1480


Quadrant is an Android-only benchmark app that measures performance across every category – processor performance, graphics, input/output speed, and mathematical calculations. Here, the Exynos Galaxy S4 managed a score of 12726, which is the highest and a few more points than the HTC One, which scored 11746 . The next closest one was the Sony Xperia Z, which maxed out at 8075 points.

So how fares the Galaxy S4 against others in Quadrant? Here are the scores.

Galaxy S4 12726

HTC One 11746

Sony Xperia Z 8075

HTC One X+ 7632

LG Optimus G 7439

HTC One X 5952

Galaxy Note 2 5916

LG Nexus 4 4567

AnTuTu Benchmark

AnTuTu is another benchmark tool similar to Quadrant – it tests the device in many areas, including processor, graphics, RAM, and data transfer performance. Well, AnTuTu is where the Exynos variant of the Galaxy S4 really shined, scoring 27,417 points, almost a whopping 10% higher score than its nearest competitor, which was none other than the Snapdragon 600 variant of the S4 itself. AnTuTu is currently the most popular benchmark app available for Android, and such a high score should do nothing but impress everyone, no matter how high their expectations.

Here go the AnTuTu scores for the Galaxy S4 and the competition.

Galaxy S4 Exynos 27417

Galaxy S4 Snapdragon 25900

HTC One 22678

Sony Xperia Z 20794

LG Nexus 4 19318

Galaxy S3 15547

HTC Butterfly 12631

So there you have it. The Galaxy S4 is the fastest smartphone on the planet at least in synthetic benchmarks, and it trumps other devices by quite a fair margin in some cases. This is further impressive when you consider that the Galaxy S4 tested isn’t running the final version of the software, so we can expect the scores to improve when retail units hit the shelves in late April.

Another thing to keep in mind is that real life performance is what really matters, so these benchmark scores are merely what the device is capable of when subjected to particular tests. Also, if you were disappointed that your country isn’t getting the 8-core Exynos variant of the Galaxy S4, don’t be, as the Snapdragon version is not too far behind and should provide performance that’s not discernible in real life usage.

Samsung Galaxy S4 Specifications

5-inch 1080p Super AMOLED display, 1920 x 1080 pixels

1.6GHz 8-core Exynos/1.9GHz quad-core Snapdragon 600 processor


13-megapixel rear camera, HDR, 1080p video recording

1.9-megapixel front camera

16/32/64GB storage, microSD slot

Wi-Fi, HSPA+, LTE, NFC, GPS, IR Blaster

2,600 mAh battery, wireless charging via optional back cover

Android 4.2.2 Jelly Bean, TouchWiz UI

136.6 x 69.8 x 7.9mm

All You Need To Know About Recommendation Systems

This article will support data scientists in furthering their studies on recommendation systems so that they can develop applications for professional use. We introduce the content-based filtering, for the recommendation system, using this filtering, we learn here how to use this system and how to predict items, we use an amazon dataset.

In recommendation systems, we have two techniques, In this bog we major focus on content-based filtering.

Collaborative Filtering.

Content-based Filtering.

Today in real-world recommendation systems are an integral part of our lives. In amazon Roughly 35% of revenue is made by a Recommendation system, hence we can say the Recommendation system contributes to the major chunk of revenue in amazon. Working on recommendation algorithms is one of my favourite things to do. When I come across a recommendation engine on a website, I immediately want to dissect it and, how it works. It’s one of the many perks of a data scientist!

Collaborative Filtering 

In this filtering, we use user and item reviews and then using this review we find a common user who has the same interest-as other users.

Content-based Filtering 

Content-based filtering we recommend to what the user likes, based on their interest.

Here we will focus on a content-based Recommendation System and we understand using real-life data,amazon-apparel dataset.

Source: Wikipedia

Table of Contents

Table of Contents:-

1. What is the Recommtations system?

2. Overview of the data.

3. Data preprocessing.

5. Text Preprocessing.

6. Apply the different techniques to convert text to vector.

7. Measuring the effectiveness of the solution.

What is the Recommendation System?

Let’s take one real-life example. all of the done shopping on Amazon. So when you search for one product and then amazon shows a similar item. In nutshell, we can say this similar product is nothing but it is a recommendation system for you, so it’s all about a recommendation. But, how a recommendation system works? We will learn about it in this blog. To understand better we take an amazon woman apparel dataset.

Overview of Data

Here we have an item title, brand name, the colour of the item, price of the item, etc. Using an amazon API we take data from amazon, we have a total of 183k datapoint(product) and 19 features available here.

The Feature List data.columns # prints column-names or feature-names.

Index([‘sku’, ‘asin’, ‘product_type_name’, ‘formatted_price’, ‘author’,

‘color’, ‘brand’, ‘publisher’, ‘availability’, ‘reviews’,

‘large_image_url’, ‘availability_type’, ‘small_image_url’,

‘editorial_review’, ‘title’, ‘model’, ‘medium_image_url’,

‘manufacturer’, ‘editorial_reivew’],


Of these 19 features, we will be using only 6 features in this blog

1. asin ( identification number)

2. brand ( brand to which the product belongs)

3. color ( Color information of apparel)

4. product_type_name (type of the apparel, ex: SHIRT/T-SHIRT )

5. medium_image_url ( URL of the image )

6. title (title of the product.)

7. formatted_price (the price of the product)

data = data[['asin', 'brand', 'color', 'medium_image_url', 'product_type_name', 'title', 'formatted_price']] print ('Number of data points : ', data.shape[0], 'Number of features:', data.shape[1]) data.head() # prints the top rows in the table.

Source: Author’s GitHub Profile

Data Preprocessing

For the data preprocessing we remove all the datapoint where feature value is not present.

After the remove datapoint where colour and price value is null and after this we have 28k datapoint available.

Remove some text from the title

Eg of duplicates data points:

Titles 1:

16. woman’s place is in the house and the senate shirts for Womens XXL White

17. woman’s place is in the house and the senate shirts for Womens M Grey

Title 2:

25. tokidoki The Queen of Diamonds Women’s Shirt X-Large

26. tokidoki The Queen of Diamonds Women’s Shirt Small

27. tokidoki The Queen of Diamonds Women’s Shirt Large

Here we have some title that looks like this where the meaning of the title is the same, except the few words. from the eg titles1 where we can show

both titles is the same they talk about the same shirts, the only difference is the size of shirt.

so here we remove this type of data title.

Remove the same Image.

There is some image is available where the product is the same but different only that is product colour. So, we remove that product where the product is the same but the colour is different.


Source: Author’s GitHub Profile

Text Preprocessing

Here we have the product title and to convert this title into vector first we have to do text processing.

Remove the stop word  # we use the list of stop words that are downloaded from nltk lib. import nltk'stopwords') stop_words = set(stopwords.words('english')) print ('list of stop words:', stop_words)

{“couldn’t”, ‘such’, ‘where’, ‘too’, ‘are’, ‘ve’, ‘your’, ‘him’, ‘this’, “wouldn’t”, “didn’t”, ‘has’, ‘than’, ‘ll’, ‘very’, ‘who’, ‘having’, ‘for’, “should’ve”, ‘mightn’, ‘of’, ‘until’, ‘we’, ‘haven’, “you’d”, ‘while’, “shouldn’t”, ‘doing’, “mightn’t”, ‘just’, ‘through’, ‘own’, ‘o’, ‘what’, ‘any’, ‘will’, “weren’t”, ‘have’, “hadn’t”, ‘my’, ‘weren’, ‘most’, “aren’t”, ‘it’, ‘had’, ‘further’, ‘more’, ‘those’, ‘on’, ‘against’, “doesn’t”, ‘himself’, ‘their’, ‘few’, ‘being’, ‘you’, ‘below’, ‘in’, ‘here’, ‘be’, “mustn’t”, “wasn’t”, ‘nor’, ‘then’, ‘how’, “that’ll”, ‘a’, ‘hasn’, ‘mustn’, “needn’t”, ‘shouldn’, ‘by’, ‘doesn’, ‘hadn’, ‘y’, ‘herself’, “she’s”, ‘shan’, ‘do’, ‘d’, ‘an’, ‘ourselves’, ‘the’, ‘that’, ‘after’, ‘there’, “you’re”, ‘them’, ‘was’, ‘itself’, ‘hers’, ‘yours’, ‘needn’, ‘down’, ‘its’, “you’ll”, ‘didn’, “won’t”, ‘both’, ‘these’, ‘up’, ‘again’, ‘his’, ‘did’, ‘our’, ‘when’, ‘only’, ‘s’, ‘over’, ‘because’, ‘wasn’, ‘should’, ‘so’, ‘re’, ‘couldn’, ‘under’, ‘ain’, ‘at’, “it’s”, ‘as’, ‘he’, ‘all’, ‘does’, “don’t”, ‘won’, ‘whom’, ‘to’, ‘i’, “haven’t”, ‘ma’, ‘were’, “hasn’t”, ‘m’, ‘above’, ‘each’, ‘she’, “isn’t”, ‘between’, ‘they’, ‘am’, ‘no’, ‘myself’, ‘yourself’, ‘during’, ‘t’, ‘out’, ‘off’, ‘wouldn’, “you’ve”, ‘or’, ‘with’, ‘ours’, ‘before’, ‘same’, ‘which’, ‘into’, ‘now’, “shan’t”, ‘if’, ‘themselves’, ‘isn’, ‘about’, ‘yourselves’, ‘theirs’, ‘and’, ‘don’, ‘not’, ‘from’, ‘can’, ‘me’, ‘but’, ‘is’, ‘once’, ‘why’, ‘some’, ‘her’, ‘aren’, ‘been’, ‘other’}

Apply Stemming from nltk.stem.porter import * stemmer = PorterStemmer() print(stemmer.stem('arguing')) print(stemmer.stem('fishing'))


argu fish Apply the Different Techniques to Convert Text to Vector TF-IDF Base Word to Vector

Here we use a TF-IDF to convert a text to a vector and after this, we got a vector for each title.

Source: Towards Data Science

Now we have a vector and for this find, similarity we use a Euclidean distance, which product dist is very small to the query product we can defined-as a similar product.

Source: Tutorial Example

Similar Product Output

Source: Author’s GitHub Profile

Brand and Color similarity

Here we have two categorical feature which is colour and brand, so we think we use only a brand and a feature and make a similarity or product. So for the categorical data, we use one-hot encoding to convert it into a vector.

After this, we use euclidean distance and find a similarity.

Source: GitHub Profile

Here we can see this is more focused on colour and brand.

Image similarity

We have a product image so we use it to find a similar product and for converting images, to vector data we use deep learning.

we use a CNN (VGG16) to convert images to vectors. Now after this, we find a distance and predict a similar product.

The output of the VGG16 model.

                                                                 Source: Author’s GitHub Profile

Combine all features for similarity

Till the time we take each feature and find a similar product, now we use all the features and find a similar product and using all features they give much more efficient result.

Source: Author’s Github Profile

Measuring the Effectiveness of the Solution

So here we provide 5 solutions for finding a similar product, we can perform A/B testing.


Recommendation systems are a powerful new tool for adding value to a company and hese systems assist users in locating things they wish to purchase from a business. Recommendation systems are quickly becoming a critical element in online E-commerce.

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Samsung Galaxy S10, S10+ And S10E With Dynamic Amoled Infinity

The entire S10 lineup is a product of Samsungs quest for perfection in providing smartphones with immersive user experiences, and for the Korean giant, perfection at this stage means a device with:

An edge-to-edge screen with pint-sized bezels, one that can wirelessly charge another device, one with an Infinity-O display with pint-sized cut for front-facing cameras, and what is more, one with ultrasonic in-screen fingerprint reader that uses sound waves and of course a device with multiple rear cameras, up to 1TB of built-in storage and many more.

Samsung Galaxy S10+

At the rear, you have a triple shooter with dual OIS comprising of a 12MP  primary sensor with Dual Pixel autofocus, optical stabilization, an aperture that can switch between f/1.5 and f/2.4 as well as a 12MP telephoto shooter (45°) laced with OIS and Phase Detection AF, and a third 16MP 123° Ultra Wide sensor with f/2.2 aperture with 0.5X/2X optical zoom, up to 10X digital zoom.

Samsung Galaxy S10

Samsung also announced a 5G variant of the Galaxy S10 that has a 4,500-mAh battery with 8GB RAM and 256GB storage with no option for expansion making it the only member of the lineup without a microSD card slot.

Samsung Galaxy S10e

The Samsung Galaxy S10e is the most basic of the trio. Its a watered-down variant of the Galaxy S10, and is clearly aimed at those operating on a tighter budget. The device still, keeps some of the juicy features of its bigger siblings, albeit some compromises. First, the display is a lot more now compact – at 5.8 inches, the Galaxy S10e eschews the continuum curved sides display for a flat Dynamic AMOLED panel with HDR+ support, and drops the in-screen fingerprint reader for a side-mounted one as rumored.

Samsung Galaxy S10e specifications

5.8-inch Full HD+ (2280 × 1080 pixels) Dynamic AMOLED display with 438ppi, HDR10+, Corning Gorilla Glass 5 protection

Octa-Core Qualcomm Snapdragon 855 7nm Mobile Platform with Adreno 640 GPU / Octa-Core Samsung Exynos 9 Series 9820 8nm processor with Mali-G76 MP12 GPU

6GB / 8GB LPDDR4x RAM with 128GB/256GB storage (UFS 2.1), expandable memory up to 512GB with microSD

Android 9.0 (Pie)

Single / Hybrid Dual SIM (nano + nano / microSD)

12MP Dual Pixel rear camera with LED Flash, f/2.4-f/1.5 variable  aperture, OIS, 16MP 123° Ultra Wide sensor with f/2.2 aperture with 0.5X/2X optical zoom, up to 8X digital zoom

10MP Dual Pixel front-facing camera with 80° wide-angle lens, f/1.9 aperture

Water and dust resistant (IP68)

Stereo speakers tuned by AKG, Dolby Atmos

Side-mounted Fingerprint Sensor

Sensors – Accelerometer, Barometer, Capacitive Fingerprint Sensor, Gyro Sensor, Geomagnetic Sensor, Hall Sensor, Proximity Sensor, RGB Light Sensor

Dimensions: 69.9 x 142.2 x 7.9mm; Weight: 150g

Dual 4G VoLTE, Wi-Fi 802.11ax (2.4/5GHz), VHT80 4×4 MIMO, Bluetooth 5, GPS with GLONASS, USB 3.1, NFC, MST

3,100mAh battery with fast Charging both on wired and wireless (WPC and PMA) charging, Wireless PowerShare

Samsung has also dropped the telephoto module on the flip side, leaving the S10e with just the 12MP main cam and 16MP ultra-wide angle shooter, while retaining the 10MP/4K module used on the S10. Battery sized also has been cut down to a 3,100mAh size, but still enjoys the fast wired and wireless charging, plus Wireless PowerShare goodies. Inside the device is the Snapdragon 855 or  Samsung Exynos 9 Series 9820 SoCs coupled with 6GB of RAM and 128GB storage, and  8GB of RAM and 256GB storage.

The Galaxy S10, S10+, S10e are immediately available for pre-order in the US and several countries and will go on sale from March 8, 2023, in select markets.  Early adopters in a few select markets will get the Galaxy Buds worth $129.99 for free. Pricing will vary depending on the region, but below are the general price ideas.

Galaxy S10 Pricing

Samsung Galaxy S10 8GB + 128GB – $899.99

Samsung Galaxy S10 8GB + 512GB – $1149.99

Samsung Galaxy S10+ 8GB + 128GB – $999.99

Samsung Galaxy S10+ 8GB + 512GB Ceramic – $1249.99

Samsung Galaxy S10+ 12GB + 1TB Ceramic – $1599.99

Samsung Galaxy S10e 6GB + 128GB  – $749.99

Samsung Galaxy 10e+ 8GB + 256GB – $849.99

Xiaomi Black Shark 2: All You Need To Know

Xiaomi’s first gaming phone, Black Shark, was announced in April 2023 and a year down the line, the Xiaomi Black Shark 2 is among us. The phone has been released in China and is already available for purchase in the Asian country. In this post, let’s talk about what the Black Shark 2 adds to the OG model and the mobile gaming industry in general, including pricing and international availability.

To start off, here are the official Xiaomi Black Shark 2 specifications.

Related article:

Xiaomi Black Shark 2 specs

6.39-inch 19.5:9 FHD+ (1080×2340) AMOLED display

Qualcomm Snapdragon 855 processor

6GB, 8GB or 12GB RAM

128GB or 256GB storage

Dual 48MP + 13MP main camera

20MP front camera

4000mAh battery

Android 9 Pie

Extras: Bluetooth 5.0, USB-C, Quick Charge 4.0 (27W) fast charging, in-display fingerprint scanner, pressure-sensitive display, etc.

Xiaomi is still quite new in the gaming phone scene, but this hasn’t stopped the Chinese company from showing the rest of the world what’s up its sleeves. The original Black Shark was without a doubt an impressive debuting device and having learned a few things here and there, the company now has an upgraded Xiaomi Black Shark 2.

Looking at the specs sheet above, the Black Shark 2 is a powerful device, which is expected for any gaming phone. It ships with the latest and most powerful Qualcomm Snapdragon 855 processor allied to the biggest RAM module available at the moment – 12GB. This is to ensure that gamers enjoy the best of the best experience despite how resource-intensive a given game gets.

But given that these hardware specs are nothing unusual to any enthusiastic smartphone fun, some must be asking what’s so special about the Black Shark 2. Well, it got to be the 6.39-inch Samsung AMOLED display screen.

Besides shipping with an in-display fingerprint scanner, the panel is also home a pressure-sensitive system that adds more functionality to the screen for superior gaming. According to the company, this feature lets Black Shark 2 users map the buttons on the flanks of the screen and trigger them by pressing that part of the screen harder.

It seems Xiaomi has been taking notes from one of the best in the gaming industry, Razer. The Black Shark 2 includes a vapor chamber cooling system akin to the Razer Phone 2, ensuring the phone remains cool during extensive gaming. Similar to the Black Shark, the second-generation model also supports optional accessories like a handheld grip for attaching to the device.

Being a gaming device doesn’t mean the Black Shark 2 skimps on other specs. There is a dual-lens 48MP + 13MP main camera on the back and a 20MP selfie shooter that have lots of AI optimizations. Keeping it alive is a 4000mAh battery that is aided by 27W Quick Charge 4.0 fast charging technology, which takes place via a USB-C port.

Xiaomi Black Shark 2 pricing and availability

Xiaomi Black Shark 2 is already selling in China and it comes in four memory configurations. The base model has 6/128GB and goes for CNY 3,199 followed by 8/128GB priced at CNY 3,499. There’s also a variant with 8GB RAM and 256GB storage priced at CNY 3,799 while the premium-most model with 12GB RAM and 256GB storage goes for CNY 4,199.

When converted, it means the gaming phone costs between $476 and $625, but of course, these prices are likely to change in markets outside China. The phone is available in two color variants of frozen silver and shadow black.

Speaking of, we still don’t know when the Black Shark 2 will begin selling globally and the markets that will get it, but its expected in Europe and Asia at some point in the near future.


All You Need To Know About Autoencoders In 2023

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


Autoencoders are unsupervised learning techniques based on neural network frameworks, trained to copy inputs to outputs. Neural networks are designed to create bottlenecks in the network. Internally, the hidden layer h describes the code used to represent the input. An autoencoder network consists of three parts. First, the encoder compresses the image and generates code using the encoder function h = f(x). Then comes a bottleneck where we have a compressed knowledge representation of the original input, followed by a decoder that forms the reconstruction r = g(h). The autoencoder scheme is shown in Figure 1. Data is compressed and restructured as it moves through the architecture. This compression and reconstruction process is complicated when the input features are independent. However, ere is some correlation within the input data, the existing dependencies can be learned and used when the input is forced through the network bottleneck.

Figure 1 – Diagram showing the schematic of a typical Autoencoder 

In the following subsection, we will take a detailed look into the network architecture and the corresponding hyperparameters of an Autoencoder.

The Architecture of an AutoEncoders

You must already have a faded idea of what an autoencoder would look like. In this section, we will add more depth to your understanding. We would be particularly interested in the hyperparameters you need to take care of while designing an autoencoder.

As mentioned earlier, an autoencoder consists of three parts: encoder, code, and decoder. Both the encoder and decoder are simple feedforward neural networks. The code is a single layer of ANN with selected dimensions. For input and output layers, the number of nodes is determined by the input data X. Therefore, the input and output layers have the same number of nodes, and both correspond to high-dimensional representations. The middle hidden layer with the fewest nodes corresponds to the low-dimensional representation. The goal of the training process is to minimize the squared reconstruction error between the network’s inputs and outputs. For learning algorithms, the most commonly used strategy is backpropagation. The initial weights of the network are important for the encoder to find a good solution. Backpropagation works more effectively when the initial weights are closer to the optimal solution. Many algorithms have been developed to find good initial weights.

Before training the autoencoder, we need to set four hyperparameters.

The number of nodes in the middle layer, i.e., the code layer. A smaller size of the code layer would result in more compression.

The number of nodes per layer is the third hyperparameter we need to tune. Typically the encoder and decoder are symmetric in terms of the layer structure, and the number of nodes in each subsequent layer of the encoder keeps decreasing till the code layer is reached and then keeps increasing similarly in the decoder architecture.

The choice of the loss function is the fourth hyperparameter. The most frequently used loss functions include the mean squared error or binary cross entropy.

The most important tradeoff in autoencoders is the bias-variance tradeoff. At the same time, the autoencoder architecture should reconstruct the input well (reducing the reconstruction error) while generalizing the low representation to something meaningful. Therefore, to achieve this property, let’s look at the various architectures developed to address this trade-off.

Autoencoders types to tackle the tradeoff 1. Sparse Autoencoders

These networks offer an alternative method of introducing bottlenecks without requiring node count reduction. It handles the trade-off by forcing sparsity on hidden activations. They can be added over or in place of bottlenecks. There are two ways to apply sparse regularization. The first is by using L1 regularization, and the second is by implementing KL divergence. I won’t go into the mathematical details of the regularization technique, but a brief overview is sufficient for this blog.

Figure 2 – Schematic representation of a Sparse Autoencoder

2. Denoising Autoencoders

Autoencoders have been considered neural networks with identical inputs and outputs. The main goal is reproducing the input as accurately as possible while avoiding information bottlenecks. However, another way to design an autoencoder is to slightly perturb the input data but keep the pure data as the target output. With this approach, the model cannot simply create a mapping from input data to output data because they are no longer similar. So using this regularization option introduces some noise into the input while the autoencoder is expected to reconstruct a clean version of the input.

Figure 3 – Schematic representation of a Denoising Autoencoder 

While in the previous case, the emphasis was on making the encoder more resilient to some input perturbations, in these types of architectures, the emphasis is on making the feature extraction less sensitive to small perturbations. It is written. This is achieved by having the encoder ignore changes in the input that are not significant for reconstruction by the decoder. The main idea behind this regularization technique is that potential representations that are not important for reconstruction are reduced by the regularization factor. In contrast, important variations remain because they have a large impact on the reconstruction error is.

Figure 4 – Schematic representation of a Contractive Autoencoder

Applications of Autoencoders

If you’ve read this far, you should have the theoretical background you need to know about autoencoders. You must be wondering where the application of these structures lies in machine learning. This section sheds light on the applications of these structures.

Dimensionality reduction was one of the first applications of representation learning. Reducing dimensions can help improve the model performance in several cases.

Another task that bene more than dimensionality reduction is information retrieval.

Other applications of autoencoders include anomaly detection, image processing, data denoising, drug discovery, popularity prediction, and machine translation.


That’s why I talked about autoencoders in today’s blog. Autoencoders are architectures originally designed to help with dimensionality reduction. However, its applications have multiplied many times over time. First, we briefly introduced the structure of an autoencoder and how data compression is achieved at the code layer. We then discussed different types of autoencoders and how each one helps to deal with bias-variance tradeoffs. Finally, we have finished discussing the scenarios in which autoencoders are applied in today’s world. So the key takeaways from this article are:

The general architectural approach towards autoencoders

The bias-variance tradeoff faced by the autoencoders

How applying different regularization techniques can enable us to handle the tradeoff. This would enable you to think of more such algorithms and develop newer architectures.

The areas where this type of architecture finds applicability.

I believe I could leave you with a deep theoretical understanding of the architecture and use cases of Autoencoders from this discussion in the blog. If this article excites you, I urge you to go ahead and develop one such architecture for yourself. It’s a good project to have with you.

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