Trending December 2023 # Dead By Daylight Developer Update Reveals Removal Of Leatherface Masks # Suggested January 2024 # Top 21 Popular

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Dead by Daylight Developer update reveals removal of Leatherface masks

The changeable masks of DBD’s Bubba Sawyer have been removed permanently following harassment

A Mid-Chapter, Dead by Daylight Developer update has been rolled out to DBD today, removing The Cannibal’s four changeable Leatherface masks for good.

According to the official Developer Update, the DBD Bubba masks have been removed due to “hateful activity” and will not be brought back. Instead, players who have played as The Cannibal will be awarded 6,000 Iridescent Shards to compensate for the removal of the unlockable faces.

It’s not the first time that Behaviour Interactive have rapidly changed the game through a Dead by Daylight Developer Update, as the one in September overhauled the ranked system,

What were the unlockable DBD Leatherface masks?

DBD fans who play Bubba Sawyer, a Dead by Daylight killer based on the Texas Chainsaw Massacre’s Leatherface, were previously able to unlock four changeable masks for the character. The masks were based on the original four survivors; Dwight Fairfield, Meg Thomas, Claudette Morel, and Jake Park, and could be worn by the killer in reference to the original Leatherface’s trait of wearing his victim’s skin over his own face. In order to claim each mask, the player had to sacrifice each of the original survivors 25 times while playing as The Cannibal.

Why were the DBD Bubba masks removed from the game?

As of January 4th, 2023, the DBD Leatherface masks have all been removed, with the reason being that players were using the masks while engaging in “hateful activity”. While the exact reason was not made clear on the official post, rumors circulating the internet suggest that players were using the face based on Claudette, a Black girl, while racially harassing other players.

Behaviour Interactive, the developer behind Dead by Daylight, were quick to react to the claims and put the plans in motion to remove the masks. They have said, “We will not tolerate hateful activity and will continue to take every step necessary to protect the community.”

Free Dead by Daylight Iridescent Shards

In order to compensate those players who have earned the masks, Behaviour Interactive have given 6,000 free Iridescent Shards to everybody who played with The Cannibal before the Mid-Chapter update was rolled out on January 4th, 2023. Iridescent Shards are one of the DBD in-game currencies and can be redeemed in the Shrine of Secrets for Teachable Perks.

Other Dead by Daylight Mid-Chapter changes

Grade rewards increased

According to the Dead by Daylight Developer update notes, Behaviour Interactive have been tracking Grade rewards to ensure that they seem fair. They have apparently determined that the effort outweighed the reward, and, as such, will be increasing the rewards earned in-game from February 13th.

Ten Perks have been changed in this Mid-Chapter update. Here’s just a few of them, with the full list available – along with all the other changes that have taken place – on the official DBD forum post.

Dead Man’s Switch

Originally, Dead Man’s Switch required The Obsession to be hooked. This has been deemed too restrictive, and so Dead Man’s Switch now activates when any player is hooked.

Distortion

More and more things with aura-reading ability have been introduced since Distortion was first brought in, meaning that tokens are now used up quickly. As such, the number of tokens you start with with the Distortion Perk has been increased to four.

Remember Me

Previously, Remember Me was restricted to only activate when The Obsession was damaged with the Killer’s basic attack. As this made the Perk unpopular with some Killers, a new token is earned with Remember Me every time The Obsession loses a health state.

Wake Up

Wake Up has been altered to, hopefully, more appropriately, counter the newly-improved Remember Me. With this Mid-chapter update, the gate opening speed bonus has been increased to 15/20/25%.

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What Neuroscience Reveals About Bullying By Educators

We would never let a teacher or coach physically strike or sexually molest our child. Why then do we allow teachers and coaches to bully our children? There are three major reasons why this occurs:

Adding to years of psychological and psychiatric research, current studies by neuroscientists confirm that emotional abuse harms in serious ways. MRI imaging shows the physical harm done by bullying, not to the body, but to the brain. Bruises heal and broken bones mend, but neuroscientific research shows that emotional abuse can leave permanent scars on the brain.

Most, if not all, of this neuroscientific research examines peer bullying: child-to-child on the playground or adult-to-adult in the workplace. However, bullying hinges on power imbalance, and the greatest imbalance is adult-to-child, especially teacher-to-student or coach-to-student-athlete. Adults in caregiver positions control the child’s present and future. We always instruct our children to tell the teacher if bullying occurs. However, we should also instruct them that sometimes (although not often) teachers and coaches are bullies, and children must report to their parents what’s being said and done.

But it’s not that simple. When students do report on teacher or coach bullying, parents are instantly in a double bind, because the teacher and coach may still have power over their child. Reporting the harm might make the child more of a target. This is why teachers and coaches need greater oversight if we want children and parents to be confident speaking up about bullying.

When parents and educators understand just how permanent and damaging bullying is to the brain, it won’t seem extreme to argue that teachers and coaches should be held legally accountable for emotional abuse. As with physical and sexual abuse, this would be a powerful deterrent. It would also create the possibility that bullies in caregiver positions would have the choice and support to seek help prior to working with children.

5 Bullying Myths

However, this won’t happen while we continue believing that bullying plays a useful role in education. In this context, with a focus on teenagers, let’s look at the myths still being disseminated about bullying within the school system:

1. Teens are almost adults and need to develop thick skins.

While this is true physically, it’s just the opposite in terms of brain development. Teenagers’ brains are at a developmental stage that makes them as fragile as a 0- to 3-year-old child. If you wouldn’t allow a teacher or coach to yell or swear in the face of your baby or toddler, you shouldn’t let them do this to your teenager. Both toddlers and teens are at significant risk of developing PTSD due to their stage of brain development.

2. Bullying is actually tough love meant to make kids stronger.

In fact, bullying causes a stress response that releases cortisol to the brain. That hormone has been directly linked to depression, a mental illness reaching epidemic proportions in our teen populations. Bullying can leave an indelible imprint because it affects hormones, reduces connectivity in the brain, and sabotages new neurons’ growth. None of this makes any child stronger, smarter, more artistic, or more athletic. It just harms his or her brain permanently.

3. Emotional abuse isn’t as serious as physical or sexual abuse.

Bullying leaves neurological scars on the brain that can be seen on MRI scanners. What has surprised researchers is how closely these changes to the brain resemble those borne by children who are physically and sexually abused in early childhood. So parents who approve of teachers or coaches yelling, swearing, insulting, ignoring, and ostracizing students, all in the name of winning and achieving, should be aware it’s comparable to condoning sexual or physical abuse. For instance, MRIs show that the brain’s pain response to exclusion and taunting is identical to its reaction when the body is physically hit or burned.

4. Bullying is just part of growing up.

Neuroscientists are clear that a positive, supportive environment will allow teens to flourish, but a toxic environment will cause them to suffer in powerful and enduring ways. Bullying does not stop when students leave school. The brain changes are long-term, and the emotional scars may last a lifetime. Therefore, neuroscientists say it is urgent that we confront the “scourge of bullying.” As bullying is learned behavior, we must ask ourselves tough questions about where children learn that bullying is a way to get ahead, achieve, and excel. Is it being taught by teachers and coaches, and condoned in educational settings?

5. Students and athletes reach their potential under bullying regimes.

Brain cells grown in childhood are still used in adolescence and form new connections, while those that go unused wither away. Hence, the adolescent period can make or break a child’s intelligence. This is exactly why cortisol is so devastating when released into the brain by bullying: it damages brain structures affecting learning, memory, concentration, and decision making. Therefore, a teacher or coach’s bullying regime will never lead children to fulfill their potential. Instead, it will stunt them in serious and lasting ways.

Beyond the Myths

Parents, teachers, school administrators, and lawmakers should join together to ensure that our most vulnerable population is fully protected from all kinds of abuse — including emotional abuse or bullying by adults. It makes no sense to protect our children from two kinds of abuse only to allow a third, equally damaging kind. In a world full of passionate teachers and coaches who want the best for their students, I’m hopeful that informed discussions, grounded in psychological and neuroscientific research, will result in finally laying to rest the myths that surround bullying approaches in education.

References

Anthes, Emily, “Inside the bullied brain: The alarming neuroscience of taunting,” The Boston Globe, 2010.

Gruenewald, T. et al., “Acute threat to the social self: Shame, social self-esteem, and cortisol activity,” Psychomatic Medicine 66, 2004: 915-924.

Jensen, Frances E. with Amy Ellis Nutt, The teenage brain: A neuroscientist’s survival guide to raising adolescents and young adults, Toronto: Harper Collins, 2023.

McMahon, Tamsin, “Inside your teenager’s scary brain,” Maclean’s Magazine, January 2023: 48-53.

Raffensperger, Lisa, “Why words are as painful as sticks and stones: Rejection and heartbreak can have effects every bit as physical as cuts and bruises, and understanding why could change your life,” New Scientist, November 2012.

Schinnerer, John, Ph.D, “‘Help, my coach is a bully!’: The consequences of verbally abusive coaching.”

Steinberg, Laurence, Age of opportunity: Lessons from the new science of adolescence, Boston and New York: Houghton, Mifflin, Harcourt, 2014.

See research of Dr. Tracy Vaillancourt, adjunct professor in the Department of Psychology, Neuroscience & Behavior at McMaster University and a core member of the Offord Centre for Child Studies.

Walsh, David with Erin Walsh, Why do they act that way?: A survival guide to the adolescent brain for you and your teen, 2nd edition, New York: Simon & Schuster, 2014 (254).

Justin Bieber Reveals That He Has Facial Paralysis Caused By Ramsay Hunt Syndrome

Pop sensation Justin Bieber announced his face is partially paralyzed by a viral illness called Ramsay Hunt syndrome, caused due to the same varicella-zoster virus which causes diseases like chicken pox and even shingles.

He told viewers in a YouTube video that the infection had damaged the nerve in his ear and facial nerves and had caused his face to have paralysis. He also highlighted one of his eyes was not blinking, and he couldn’t smile on one side of his face. Even his nostrils were not moving. For this, he canceled many of his pre-planned tours and events, which made his fans worldwide tremendously anxious.

What is Ramsay Hunt Syndrome?

Chickenpox in children and shingles in adults share the varicella-zoster virus, which is responsible for the neurological condition known as Ramsay Hunt syndrome. The virus can persist in your body for your whole life, even long after recovering from chickenpox and reawaken to irritate and inflame the nerves in your face. The nerves that travel through our face go through a narrow, bony canal; when swollen, they enlarge and lose the ability to function.

The condition affects men and women equally and can result in paralysis on one side of the face and severe, blistering rashes. It is more frequent in older adults. Some patients experience alterations in their hearing, maybe perceiving noises stronger in one ear than another or acquiring tinnitus, a chronic ringing in the ears, or even deafness in one ear. Ear and facial pain are usually a part of the illness, and some individuals may suffer from vertigo.

Properly intervention of Ramsay Hunt syndrome can lessen the risk of sequelae, including irreversible facial muscle paralysis and hearing.

Affected Population

One estimate states that every 5 in 100,000 people in the U.S. experience Ramsay Hunt syndrome each year. Equal numbers of boys and females are afflicted by the Ramsay Hunt syndrome. Anybody who has had chickenpox is at risk for developing Ramsay Hunt syndrome. The vast majority of patients, however, are above the age of 60. Ramsay Hunt syndrome is very rare in kids.

It might be challenging to estimate the actual prevalence of Ramsay Hunt syndrome in the general community because the disorder may go untreated or be incorrectly diagnosed.

Diagnosis of Ramsay Hunt Syndrome

While facial paralysis and rashes often occur together, there aren’t many clinical entities that may cause both. Nevertheless, herpes simplex virus infections, impetigo due to Staphylococcus aureus, and contact dermatitis brought on by contact with irritants like poison ivy or topical treatments like neomycin are all common causes of localized face rashes. Bell’s palsy, which manifests with symptoms like those of zoster sine herpete, is the most common cause of abrupt facial paralysis. Neurosyphilis, benign skull base tumors, Lyme disease, autoimmune illnesses, extratemporal malignancies, otologic diseases, and different viral infections are some more non-traumatic causes. Another example of a non-traumatic cause is a stroke.

Although brainstem strokes typically paralyze the full hemiface, cortical strokes spare the forehead. Nonetheless, a developing stroke should present with neurological symptoms in addition to facial paralyses, such as vital sign instability. Infections with viruses, such as SARS-CoV-2, and central nervous system pathologies can lead to multiple cranial neuropathies.

Risk Factors and Complications

Any person who has had chickenpox is susceptible to Ramsay Hunt syndrome. Older folks are more likely to experience it; those over 60 are typically affected. Young children rarely develop Ramsay Hunt syndrome.

There is no spread of Ramsay Hunt syndrome. However, those who have never had chickenpox or the varicella-zoster vaccine are susceptible to the illness when the virus reactivates. For those with immune system issues, the infection may be severe.

Ramsay Hunt syndrome complications could consist of −

Permanent facial paralysis and loss of hearing − Ramsay Hunt syndrome-related hearing loss and facial paralysis are often transient for most persons. But it might stick around for good.

Eye harm − You could find it challenging to close your eyelid due to the facial weakness of Ramsay Hunt syndrome. When this occurs, the cornea, which guards your eye, may sustain damage. Eye pain and vision haze may result from this damage.

Post-herpetic neuralgia, an infection from shingles, destroys the nerve fibers, resulting in this painful illness − Ramsay Hunt syndrome causes these nerve fibers to send mixed-up and distorted messages, resulting in pain that might linger long after other Ramsay Hunt syndrome symptoms have subsided.

Treatments

Further treatment is focused on particular symptoms that each person may clearly see. This includes painkillers, carbamazepine, an anti-seizure drug that may help lessen neuralgic pain, and vertigo-suppressing drugs, such as antihistamines and anticholinergics. Diazepam (Valium), an anti-anxiety medication, can alleviate pain and vertigo.

The U.S. Food and Drug Administration (FDA) has approved capsaicin to treat post-herpetic neuralgia-related neuropathic pain. For maximum benefit, medication should be started within three days of the onset of symptoms.

Both the chickenpox vaccine for children and the shingles vaccine for those over 50 can protect against the varicella-zoster virus. These immunizations can significantly lower the risk of catching the varicella-zoster virus, which reduces the risk of developing Ramsay Hunt syndrome.

Prevention

Google Reveals Its Top Searches Of The Year For 2023

Google has released its annual list of top searches around the world, including overall searches and searches in various categories.

Top queries reflect everyday questions, as well as the people and events that made headlines in 2023.

Certain events led to people searching for how to improve their everyday lives, Google notes.

For example, the passing of iconic celebrities resulted in an influx of searches for “how to be a good role model.”

Similarly, when first responders rescued a team of soccer players from a cave in Thailand, searches for “scuba diving lessons near me” increased by 110%.

Here’s are some highlights of top worldwide searches, and the top US searches in 2023.

Everything We Know About Zack Snyder’S Army Of The Dead Franchise

Netflix

Netflix is still the leading premium streaming service, with over 200 million worldwide subscribers. It offers thousands of movies and TV shows to binge watch, including its always growing list of original films and series, including Stranger Things, The Witcher, Bridgerton, and many more.

See price at Netflix

Warning: The following post includes some major plot details from Army of the Dead.

What is Army of the Dead?

Netflix

Army of the Dead is the first standalone project in the franchise. It’s a feature-length film directed by Zack Snyder and written by Snyder, Shay Hatten, and Joby Harold.

The film follows a group of mercenaries led by Scott (Dave Bautista) as they get together to try to pull off a heist in Las Vegas.

The catch? Vegas is now a quarantine zone following a zombie outbreak. The government keeps the zombie threat at bay in Vegas behind massive walls. And to make things even trickier, the government plans to bomb the whole city to end the zombie threat.

This means Scott has to move fast to assemble his team, get in, steal a whole bunch of cash from a safe, and get out. The safe isn’t guarded, and insurance has already covered the loss, so no one’s watching the money. But those pesky zombies aren’t just going to let them at it without a fight.

What’s next in the Army of the Dead franchise?

Are there zombies in Army of Thieves?

Netflix

A big question that was left open when the Army of the Dead prequel was announced was: will there be zombies?

Dieter wasn’t used to fighting zombies in Army of the Dead, so his central role here is a red flag for anyone hoping for more zombie-slaying fun. A poster revealed last week included the tagline “More safes. Less zombies,” adding a sense that Army of Thieves would jump from the zombie genre more firmly into heist film territory. Or maybe it would ditch the undead altogether.

Now, with a new teaser released at San Diego Comic-Con over the weekend, we have more clarity.

With a reference to the “outbreak in America” and news footage of the zombie plague, the film clearly takes place after people have started turning. But as the film takes place in Europe, it looks as though the zombie semi-apocalypse won’t be too much of a factor. It’s part of the broader world the film exists in, but probably not a huge part of the narrative.

It does look as though the fallout of the zombie outbreak is global though. It can serve as cover for a heist, for instance. Presumably while authorities are focused on preventing the spread of zombie-ism, and financial institutions fret over effects on the economy, a team of thieves can get to work more easily.

More Zack Snyder at Netflix

Netflix

Outside Army of the Dead, director/producer Zack Snyder is busy at Netflix.

Snyder recently finished a stint as one of the architects of the DC superhero cinematic universe for Warner Bros. At Warner, Snyder directed Man of Steel, Batman V. Superman: Dawn of Justice, and Justice League.

The latter was plagued by reshoots and eventually finished (and dramatically altered) by director Joss Whedon following the death of Snyder’s daughter. After fan outcry — sometimes devolving into online abuse — Warner green-lit Zack Snyder’s Justice League, which bowed on HBO Max earlier this year, known among fans as “The Snyder Cut.”

Now with DC superheroes (likely) behind him, Snyder and his production company Stone Quarry appears to be pushing full-steam ahead with the streamer. In addition Army of the Dead and its spinoffs, prequels, and potential sequels, he has signed onto an entirely separate project at Netflix: Rebel Moon.

Rebel Moon will see Snyder reteam with Army of the Dead co-screenwriter Shay Hatten. It will tell the story of a young woman tasked with recruiting warriors to help defend her home planet when an evil tyrant invades the peaceful colony at the edge of the galaxy.

Netflix

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Background Removal In The Image Using The Mediapipe Library

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

In this article, we will be making an application that will remove or replace the background of the image with another image. For that, we will be using two libraries. First, is the media pipe library for segmenting the person from the background. Second, cv2 for performing image processing techniques.

Now, we will be loading all the required libraries to build this application.

So, our very first step will be to initialize our model which will be like a pre-step for the selfie segmentation model.

In this model, we will have two types of model:

General Model: If we pass 0 as the parameter for the selfie segmentation model then the general model will be selected.

Landscape Model: If we will pass 1 as the parameter for the above model then the landscape model will be selected.

Note: If we will not specify any of the models then 0 will be selected by default i.e. General model.

But wait a minute! What is the difference between both models? Let’s discuss that:

When it comes to the general model, it specifically works on 256X256x1, i.e., 256-Height, 256-Width, 1 channel as the input, and 256x256x3 as the output. When we talk about the Landscape model, it specifically works on the 144X256X1 as the input and results in 144x256x3 output tensors other than that, both the general and landscape model is the same.

change_background_mp = mp.solutions.selfie_segmentation change_bg_segment = change_background_mp.SelfieSegmentation()

Code breakdown:

As discussed here, we will initialize the segmentation model using

mp. solutions.selfie_segmentation,

if we break it down, then we can see that from the Mediapipe library, we are calling solutions class, and from that class,

we are calling selfie_segmentation model

After model initialization, we will be setting our segmentation function, i.e., SelfieSegmentation().

Read an Image

So previously, we have initialized our segmentation model and created a selfie segmentation function as well. Now, let’s read our sample image and see what it looks like:

cv2.read: To read the sample image from the local system.

plt.imshow: This is the matplotlib function that will help us to see/plot the image.

sample_img = cv2.imread('media/sample.jpg') plt.figure(figsize = [10, 10]) plt.title("Sample Image");plt.axis('off');plt.imshow(sample_img[:,:,::-1]);plt.show()

Sample image source: Unsplash

Code breakdown:

So firstly, we are reading the image from the read() function

Then before plotting/displaying the image, we will set the size of the display using the figure function.

Finally, before displaying the image, it will be a good practice to convert the image format from RGB to BGR as cv2 will read the image in that format only when it comes to colored image, and then with the help of the show function, we will display the image.

Remove/Replace Background using Mediapipe Library

RGB_sample_img = cv2.cvtColor(sample_img, cv2.COLOR_BGR2RGB) result = change_bg_segment.process(RGB_sample_img) plt.figure(figsize=[22,22]) plt.subplot(121);plt.imshow(sample_img[:,:,::-1]);plt.title("Original Image");plt.axis('off'); plt.subplot(122);plt.imshow(result.segmentation_mask, cmap='gray');plt.title("Probability Map");plt.axis('off');

Output:

Code breakdown:

As discussed, we will first convert the BGR format image to an RGB format image.

Now with the help of process function, we will process our selfie segmentation model on the sample image.

Then as we did in the Read image section, here also we will set the figure size with the help of figure function.

Finally, we will be displaying the original image as well as segmented image side by side (by using subplot function of matplotlib) and imshow function.

Inference: So, if we will closely look at the output (segmented subplot, i.e., our main processed output) then there, we can see that some areas are neither purely black nor purely white they are a bit gray which indicates that those places our model was not able to predict that it was the background or the person so for that reason we will be using the threshold techniques to have the more accurate segmented area in the image.

So in our next step, we will be using thresholding of the mask so that we would only get two types of pixel values, i.e., Binary black and white mask, which has a pixel value of 1 for the person and 0 for the background.

plt.figure(figsize=[22,22]) plt.subplot(121);plt.imshow(sample_img[:,:,::-1]);plt.title(“Original Image”);plt.axis(‘off’); plt.subplot(122);plt.imshow(binary_mask, cmap=’gray’);plt.title(“Binary Mask”);plt.axis(‘off’);

Output:

Code breakdown:

Binary masking with thresholding: Here, we are using the concept of binary masking, which will have a pixel value of 1 for the person and a pixel value of 0 for the background. Also, we will be setting up the threshold value of 0.9, i.e., the confidence of 90% that pixel values when will be greater it will be 1 otherwise 0.

Now, again we will plot both the original and preprocessed image (one with the binary mask) using subplots and Matplotlib’s show function.

So by far, we have segmented our image accurately by performing some image preprocessing techniques. Now it’s time to visually see how the image’s background will be removed, so for that, we will be using the numpy.where() function. This function will use the binary mask values and returns white are for every 1-pixel value and then replace every area with 0 pixels, i.e., a black region with 255, which means the background will have white color only.

But, before having the required output, we have to first convert the one-channel image into the three-channel image using numpy.dstack function.

binary_mask_3 = np.dstack((binary_mask,binary_mask,binary_mask)) output_image = np.where(binary_mask_3, sample_img, 255) plt.figure(figsize=[22,22]) plt.subplot(121);plt.imshow(sample_img[:,:,::-1]);plt.title("Original Image");plt.axis('off'); plt.subplot(122);plt.imshow(output_image[:,:,::-1]);plt.title("Output Image");plt.axis('off');

Output:

Code breakdown:

As discussed, we will be using Numpy’s d-stack function to convert our image from one channel to three-channel.

Now, we will be using the Numpy’s function that will convert every black region to a white region. That is, it removes the black segmented area with the white so that it appears to be like the white background.

Finally, we will set the image size using the figure function. And then display both the original and output image using the show function.

Note: By far, for having the white background, we have used 255 as the value, but we can also have another background image as the output, for that, we just need to change the parameter in np.where function.

bg_img = cv2.imread('media/background.jpg') output_image = np.where(binary_mask_3, sample_img, bg_img) plt.figure(figsize=[22,22]) plt.subplot(131);plt.imshow(sample_img[:,:,::-1]);plt.title("Original Image");plt.axis('off'); plt.subplot(132);plt.imshow(binary_mask, cmap='gray');plt.title("Binary Mask");plt.axis('off'); plt.subplot(133);plt.imshow(output_image[:,:,::-1]);plt.title("Output Image");plt.axis('off');

Output:

Code breakdown:

So here comes the last part where we will replace the background of the image. For that, we will first read that background image using imread the function.

Now we will create one final output image. We’ll use the np. where function to replace the black region (binary asking) with the other background image.

Finally, we will display the original image, sample image, and the final segmentation result.

So, finally, we have developed our application which can remove the background of any particular image that has the person in it, though, we can also create functionality, where it can be done in real-time just like the zoom application. Still, the logic will be the same only, instead of image processing, there, we will be handling the video processing.

Key takeaways from the article

The very first takeaway from this article is that we have learned how image segmentation works and its real-world implementation.

There are ample techniques available for image segmentation. But this is one of the simplest to use as you can see it’s in the modular form.

We have also covered some image preprocessing techniques like thresholding, erosion, stack. These basic techniques are also involved in building a complete computer vision pipeline for an application.

Endnotes

Read on AV Blog about various predictions using Machine Learning.

About Me

Greeting to everyone, I’m currently working in TCS and previously, I worked as a Data Science Analyst in Zorba Consulting India. Along with full-time work, I’ve got an immense interest in the same field, i.e. Data Science, along with its other subsets of Artificial Intelligence such as Computer Vision, Machine Learning, and Deep learning; feel free to collaborate with me on any project on the domains mentioned above (LinkedIn).

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