High Quality HDR 8K VIDEO ULTRA HD 120FPS, 60FPS, 30FPS For Your HDR 8K resolution devices. Amazing combination of 12k sensor and one of the sharpest lenses in the world Zeiss Otus set in addition of HDR brings image to life! You can use this collection of Hight Resolution clips in your Tv For The Living Room, Office, Lounge, Waiting Room, Spa, Showroom, Restaurant and more. Play It On Your LG Qled TV, Samsung Oled TV, Smart TV, Sony Device, Samsung Technology, Roku, Apple TV, IPad Pro, Apple XDR, Chromecast, Xbox, Playstation and more.

⭐ Note: To view at 8K 60P you will need to use Chrome & opera.

⭐ All Videos was shot, edited & color graded by me.
THESE 8K VIDEOS ARE FOR TV'S FOR DEMO AND TEST THE QUALITY OF ALL 8K TV'S.

⭐ I have done High color correction, Color changing, Raw Videos editing, HDR color Setting, merge files & 8K Export file etc.

Editing Video by
PREETI NAAGAR

This video created for entertainment informative, educational purposes and Film & Animation.


Editing Software
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ALL CREDIT GOES TO YOUTUBE

Copyright
⭐ All The footage Was Edited And Color Corrected By Me.
⭐ Video Footage Copyright Under License.

All other rights reserved.

#12khdr #dolbyvision #60fps

How I increased my testosterone levels from 380 ng/dl (first test), 480 ng/dl (second test) to 836 ng/dl in less than a year, naturally, so about a 2x increase. After reading a few studies on how testosterone is a hormone linked to greater resilience, higher drive, physical recovery, stress tolerance, and energy levels I was intrigued to do an experiment if it would help me. Note: I did not only introduce supplements, but also improved sleep etc at the same time. Causality is hard, but I do believe the supplements have helped increase my testosterone, my estimate is at least ~200 total increase from supplements. I encourage you to do blood test before and after to confirm that it works though as I am only n=1. Evaluating your own resilience, drive, energy level etc is a lot harder and what is even harder is determining causality. I am careful to say it has made any difference for me personally: I don't know, but at least I can say I am stronger in the gym :)

Meta review mentioned in video: https://www.mdpi.com/2673-396X/2/2/11/htm
Good video on testosterone: https://youtu.be/BtHg1AJwOzs

The exact supplements I take (I ordered from amazon DE):
Ashwaganda (500mg per capsule, 2x daily, once earlier in the day and once taken before bed): https://amzn.to/3UAWdJF
D Vitamin (5000 IU daily, taken with food): https://amzn.to/3E7T39b
Zinc Bisglycinate (25mg daily): https://amzn.to/3g4UGwl
Magnesium Bisglycinate (300mg, 2 capsules before bed): https://amzn.to/3TC344n
Creatine Monohydrate (5g a day): https://amzn.to/3UzPkrP
Fenugreek (650 mg a day, 2 capsules): https://amzn.to/3AeLNHj
Boron (6 to 9mg a day, 2-3 capsules): https://amzn.to/3hA3SJr
Fadogia Agrestis (300 to 600mg daily, 1-2 capsules): https://amzn.to/3UVqrqx
Tongkat Ali (500mg daily, 1 capsule): https://amzn.to/3EFJmR3

I also take these for improved sleep:
L-theanine (200-400mg, ~30 minutes before bed): https://amzn.to/3trKwsM
Melatonin (0.5mg, ~30 minutes before bed, I use maybe 3x a week): https://amzn.to/3Ah7HK9
Magnesium bisglycinate(~ 30 minutes before bed): mentioned above already
Apigenin (50mg, 1 capsule, ~ 30 minutes before bed): https://amzn.to/3gbSTFI

Others:
Omega 3 (4-5 capsules, 2-2.5gram EPA a day, note this is not triglyceride formula so take with foods and store in freezer/fridge): https://amzn.to/3hHZMPs
Icing testicles (free): 15 min/day before bed while reading. Lol :)

*Disclaimer: This video or video description contains affiliate links. That means that I am awarded a small commission for purchases made through them, at no added cost for you.

Timestamps:
0:00 - Introduction
1:09 - Bloodwork result
3:07 - Why do you want to increase it?
6:18 - General advice (Notes) from Huberman to increase it
7:45 - Thoughts on diet
9:30 - Supplements meta review
11:00 - Magnesium Bisglycinate
11:51 - Tonkat Ali (huberman talks a lot about this)
13:30 - Boron for lowering SHBG
15:23 - Ashwaganda
16:30 - Fenugreek
17:00 - Additional supplements that was not included in meta review
17:54 - Other interesting avenue, that's free :) (icing)
21:47 - Summary and why you (most likely) do not need lifelong TRT

In this video we take a look at how to solve the super common problem of having an imbalanced or skewed dataset, specifically we look at two methods namely oversampling and class weighting and how to do them both in PyTorch.

Toy dataset used in video:
https://www.kaggle.com/dataset..../77934f795ab14e30431

People often ask what courses are great for getting into ML/DL and the two I started with is ML and DL specialization both by Andrew Ng. Below you'll find both affiliate and non-affiliate links if you want to check it out. The pricing for you is the same but a small commission goes back to the channel if you buy it through the affiliate link.
ML Course (affiliate): https://bit.ly/3qq20Sx
DL Specialization (affiliate): https://bit.ly/30npNrw
ML Course (no affiliate): https://bit.ly/3t8JqA9
DL Specialization (no affiliate): https://bit.ly/3t8JqA9

GitHub Repository:
https://github.com/aladdinpers....son/Machine-Learning

✅ Equipment I use and recommend:
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PyTorch Playlist:
https://www.youtube.com/playli....st?list=PLhhyoLH6Ijf

In this video we go through how to implement a dynamic algorithm for solving the sequence alignment or edit distance problem. This is also referred to as the Needleman Wunsch algorithm, it seems as if this algorithm has quite many names :)

Code repository:

https://github.com/AladdinPerz....on/Algorithms-Collec

I recommend watching the explanation video before watching this implementation:

https://youtu.be/bQ7kRW6zo9Y

Welcome to the next tutorial covering deep learning with Python, Tensorflow, and Keras. We've been working on a cryptocurrency price movement prediction recurrent neural network, focusing mainly on the pre-processing that we've got to do. In this tutorial, we're going to be finishing up by building our model and training it.

Text tutorials and sample code: https://pythonprogramming.net/....crypto-rnn-model-dee

Discord: https://discord.gg/sentdex
Support the content: https://pythonprogramming.net/support-donate/
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All Rights Belong To Sony Pictures

All rights owned by Universal Pictures

Gulshan Kumar & T-Series Presents A Sohail Khan Productions "Ready" Directed by Anees Bazmee starring Salman Khan and Asin in the lead roles. It also features Paresh Rawal, Arya Babbar, and Mahesh Manjrekar in supporting roles, while Ajay Devgn, Sanjay Dutt, Kangana Ranaut, Zarine Khan, and Arbaaz Khan in cameo appearances.

Movie Credits:
Movie Name: Ready
Actors: Salman Khan, Asin, Paresh Rawal, Arya Babbar, and Mahesh Manjrekar
Director: Anees Bazmee
Producer: Bhushan Kumar, Sohail Khan, Krishan Kumar
Music: Pritam, Devi Sri Prasad
Lyrics: Neelesh Misra, Amitabh Bhattacharya, Kumaar, Ashish Pandit
Music Label: T-Series
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Visit https://brilliant.org/PythonProgrammer/ to get started for free and get 20% off your annual subscription.

Thanks to Brilliant for sponsoring this video :-)

Grokking Deep Learning is a great introduction to Deep Learning.

You can buy the book here:
Amazon affiliate link https://amzn.to/353S5gG

📚 My favourite python books for beginners (affiliate links)
📗 Python Crash Course 2nd Edition https://amzn.to/33tATAE
📘 Automate the Boring Stuff with Python https://amzn.to/3qM1DFl
📙 Python Basics - A Practical Introduction to Python 3 https://amzn.to/3fHRMdb
📕 Python Programming An Introduction to Computer Science https://amzn.to/33VeQCr
📗 Invent Your Own Computer Games with Python https://amzn.to/3FM3H4b

🆓 Free Python Resource
https://python-programming.qua....ntecon.org/intro.htm
(This is a great introduction to python)

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Deep Learning is the the most exciting subfield of Artificial Intelligence, yet the necessary hardware costs keep many people from participating in its research and development. I wanted to see just how cheap a deep learning PC could be built for in 2020, so I did some research and put together a deep learning PC build containing brand new parts that comes out to about 450 US dollars. I chose NewEgg for the parts because it has a global shipping policy, deep learning belongs to the world not just the United States. In this episode, I’m going to walk you through what the deep learning stack looks like (CUDA, Jupyter, PyTorch, etc.) , why i chose the various hardware components, and then I’ll show you how to setup the full deep learning software stack on your PC. Enjoy!

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Please subscribe for more educational videos! It means a lot to me.

DIY Deep Learning PC parts list (about $450):
-------------------------------------------------------
GPU (GTX 1650): https://bit.ly/31Jb4Hu
Motherboard (MSI A320M ): https://bit.ly/2uyCaop
Hard Drive (Seagate Firecuda 1TB): https://bit.ly/2tKxUSi
RAM (SK Hynix 8 GB): https://bit.ly/2UMpWD8
Power Supply (Corsair 450W): https://bit.ly/2w74yOT
CPU (AMD Ryzen 3 Series 4 Core 3.1 ghz): https://bit.ly/31HwiFl
PC Case (2 fans built-in): https://bit.ly/39p7IMo
--------------------------------------------------------
Note* - each part price is always fluctuating +/- 10 dollars in price

The ABS $600 pre-built pc:
https://bit.ly/2OJjcCh

PyTorch’s Image Classifier Example:
https://pytorch.org/tutorials/....beginner/blitz/cifar

Linus Tech Tips POV PC Build Guide:
https://www.youtube.com/watch?v=v7MYOpFONCU

Instructables PC Build Guide:
https://www.instructables.com/....id/Build-a-Gaming-Co

Nvidia’s CUDA Documentation:
https://docs.nvidia.com/

Docker:
http://docker.com/

Petronetto’s Deep Learning Docker Image:
https://github.com/petronetto/....docker-python-deep-l

Another Deep Learning Docker Image:
https://github.com/NVAITC/ai-lab

Are you a total beginner to machine learning? Watch this:
https://www.youtube.com/watch?v=Cr6VqTRO1v0

Learn Python:
https://www.youtube.com/watch?v=T5pRlIbr6gg

Live C Programming:
https://www.youtube.com/watch?v=giF8XoPTMFg

CUDA Explained:
https://www.youtube.com/watch?v=1cHx1baKqq0

Hit the Join button above to sign up to become a member of my channel for access to exclusive live streams!

Signup for my newsletter for exciting updates in the field of AI:
https://goo.gl/FZzJ5w

Can't afford a PC right now? That's OK, use Google Colab for a free cloud GPU:
https://colab.research.google.com/

Credits:
Nvidia team
PyTorch team
Image/GIF assets are from across the Web, i take no credit for them
(except some memes)
Comedy Central (“Nathan for you” clip)

And please support me on Patreon:
https://www.patreon.com/user?u=3191693

Patreon: https://www.patreon.com/mlst
Discord: https://discord.gg/ESrGqhf5CB

"Symmetry, as wide or narrow as you may define its meaning, is one idea by which man through the ages has tried to comprehend and create order, beauty, and perfection." and that was a quote from Hermann Weyl, a German mathematician who was born in the late 19th century.

The last decade has witnessed an experimental revolution in data science and machine learning, epitomised by deep learning methods. Many high-dimensional learning tasks previously thought to be beyond reach -- such as computer vision, playing Go, or protein folding -- are in fact tractable given enough computational horsepower. Remarkably, the essence of deep learning is built from two simple algorithmic principles: first, the notion of representation or feature learning and second, learning by local gradient-descent type methods, typically implemented as backpropagation.

While learning generic functions in high dimensions is a cursed estimation problem, many tasks are not uniform and have strong repeating patterns as a result of the low-dimensionality and structure of the physical world.

Geometric Deep Learning unifies a broad class of ML problems from the perspectives of symmetry and invariance. These principles not only underlie the breakthrough performance of convolutional neural networks and the recent success of graph neural networks but also provide a principled way to construct new types of problem-specific inductive biases.

This week we spoke with Professor Michael Bronstein (head of graph ML at Twitter) and Dr.
Petar Veličković (Senior Research Scientist at DeepMind), and Dr. Taco Cohen and Prof. Joan Bruna about their new proto-book Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges.

Enjoy the show!

Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
https://arxiv.org/abs/2104.13478

[00:00:00] Tim Intro
[00:01:55] Fabian Fuchs article
[00:04:05] High dimensional learning and curse
[00:05:33] Inductive priors
[00:07:55] The proto book
[00:09:37] The domains of geometric deep learning
[00:10:03] Symmetries
[00:12:03] The blueprint
[00:13:30] NNs don't deal with network structure (TedX)
[00:14:26] Penrose - standing edition
[00:15:29] Past decade revolution (ICLR)
[00:16:34] Talking about the blueprint
[00:17:11] Interpolated nature of DL / intelligence
[00:21:29] Going tack to Euclid
[00:22:42] Erlangen program
[00:24:56] “How is geometric deep learning going to have an impact”
[00:26:36] Introduce Michael and Petar
[00:28:35] Petar Intro
[00:32:52] Algorithmic reasoning
[00:36:16] Thinking fast and slow (Petar)
[00:38:12] Taco Intro
[00:46:52] Deep learning is the craze now (Petar)
[00:48:38] On convolutions (Taco)
[00:53:17] Joan Bruna's voyage into geometric deep learning
[00:56:51] What is your most passionately held belief about machine learning? (Bronstein)
[00:57:57] Is the function approximation theorem still useful? (Bruna)
[01:11:52] Could an NN learn a sorting algorithm efficiently (Bruna)
[01:17:08] Curse of dimensionality / manifold hypothesis (Bronstein)
[01:25:17] Will we ever understand approximation of deep neural networks (Bruna)
[01:29:01] Can NNs extrapolate outside of the training data? (Bruna)
[01:31:21] What areas of math are needed for geometric deep learning? (Bruna)
[01:32:18] Graphs are really useful for representing most natural data (Petar)
[01:35:09] What was your biggest aha moment early (Bronstein)
[01:39:04] What gets you most excited? (Bronstein)
[01:39:46] Main show kick off + Conservation laws
[01:49:10] Graphs are king
[01:52:44] Vector spaces vs discrete
[02:00:08] Does language have a geometry? Which domains can geometry not be applied? +Category theory
[02:04:21] Abstract categories in language from graph learning
[02:07:10] Reasoning and extrapolation in knowledge graphs
[02:15:36] Transformers are graph neural networks?
[02:21:31] Tim never liked positional embeddings
[02:24:13] Is the case for invariance overblown? Could they actually be harmful?
[02:31:24] Why is geometry a good prior?
[02:34:28] Augmentations vs architecture and on learning approximate invariance
[02:37:04] Data augmentation vs symmetries (Taco)
[02:40:37] Could symmetries be harmful (Taco)
[02:47:43] Discovering group structure (from Yannic)
[02:49:36] Are fractals a good analogy for physical reality?
[02:52:50] Is physical reality high dimensional or not?
[02:54:30] Heuristics which deal with permutation blowups in GNNs
[02:59:46] Practical blueprint of building a geometric network architecture
[03:01:50] Symmetry discovering procedures
[03:04:05] How could real world data scientists benefit from geometric DL?
[03:07:17] Most important problem to solve in message passing in GNNs
[03:09:09] Better RL sample efficiency as a result of geometric DL (XLVIN paper)
[03:14:02] Geometric DL helping latent graph learning
[03:17:07] On intelligence
[03:23:52] Convolutions on irregular objects (Taco)

The Intelligent Inspection toolset, powered by Deep Learning technology, provides powerful and robust on-device anomaly detection as well as object classification that is not possible with rule-based machine vision.

The user-friendly interface, on-device traning and example-based approach makes it easy to ensure that inspected items fulfill required quality and sorting demands, which helps to improve yield, reliability, productivity and increase customer satisfaction.

Intelligent Inspection toolset is part of the SICK Nova 2D SensorApp and runs directly on the InspectorP6xx 2D vision sensors.

And that’s not all, traditional rule-based machine vision tools are also included, combining benefits with deep learning side-by-side.

Click here for more information:
https://www.sick.com/Intelligent_Inspection

If you like this video, please give us a like below and subscribe to our channel.

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For information and general inquiries please contact:
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Learn how to get started with Hugging Face and the Transformers Library in 15 minutes! Learn all about Pipelines, Models, Tokenizers, PyTorch & TensorFlow integration, and more!

Get your Free Token for AssemblyAI Speech-To-Text API 👇https://www.assemblyai.com/?utm_source=youtube&utm_medium=referral&utm_campaign=yt_pat_26

Hugging Face Tutorial
Hugging Face Crash Course
Sentiment Analysis, Text Generation, Text Classification

Resources:
Website: https://huggingface.co
Course: https://huggingface.co/course
Finetune: https://huggingface.co/docs/transformers/training

▬▬▬▬▬▬▬▬▬▬▬▬ CONNECT ▬▬▬▬▬▬▬▬▬▬▬▬

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Timestamps:
00:00 Intro
00:40 Installation
01:02 Pipeline
04:37 Tokenizer & Model
08:32 PyTorch / TensorFlow
11:07 Save / Load
11:35 Model Hub
13:25 Finetune

HuggingFace Tutorial
HuggingFace Crash Course

#MachineLearning #DeepLearning #HuggingFace

$648 for 4K Video and 20MP Photos!
Buy: http://amzn.to/2xWloyB

What's up filmmaking and photography friends! This last week I had a chance to take the Panasonic Lumix DMC-ZS100 for a test drive in the epic landscapes of Utah....this is what I found. Check out my full write-up here: http://blog.pictureline.com/pa....nasonic-zs100-review

*UPDATE: In the video I mention the extreme shutter sound when in Panorama mode. This can be silenced by simply switching the camera to "Silent Mode" in the main menu.

This uniquely epic little point and shoot camera has some amazing features including 20MP raw photos, 4K video (3840x2160), 4K speed burst mode and post focus abilities......all for under $700!

See more photo examples on my Facebook page:
http://www.Facebook.com/ExploringFilm...

Instagram goodness at:
http://www.instagram.com/jamesdcawley

Special thanks to my good buddy Ben for helping me with this video, check out his awesome photos at:
https://www.instagram.com/benbphotogr...

Want to buy your own ZS100 or other awesome gear?
http://www.Pictureline.com

Official Music Video for "MAD WORLD" from the album III available here:
https://linktr.ee/thewinerydogs

The Winery Dogs are:
Richie Kotzen (guitar/vocals)
Billy Sheehan (bass/vocals)
Mike Portnoy (drums/vocals)

http://www.thewinerydogs.com
https://www.instagram.com/thewinerydogs/
http://www.facebook.com/TheWineryDogs
http://twitter.com/TheWineryDogs

🔥𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐁𝐥𝐨𝐜𝐤𝐜𝐡𝐚𝐢𝐧 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐂𝐨𝐮𝐫𝐬𝐞 : https://www.edureka.co/blockchain-training (𝐔𝐬𝐞 𝐂𝐨𝐝𝐞: 𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎)
This Edureka's Blockchain video consists of the frequently asked Blockchain Interview Questions which will help you in the preparation of Blockchain Interviews.

🔴 Subscribe to our channel to get video updates. Hit the subscribe button above: https://goo.gl/6ohpTV

🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬

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🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐑𝐨𝐥𝐞-𝐁𝐚𝐬𝐞𝐝 𝐂𝐨𝐮𝐫𝐬𝐞𝐬

🔵 DevOps Engineer Masters Program: http://bit.ly/3Oud9PC
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🔵 Azure Cloud Engineer Masters Program: http://bit.ly/3AEBHzH

🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐔𝐧𝐢𝐯𝐞𝐫𝐬𝐢𝐭𝐲 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐬

🌕 Professional Certificate Program in DevOps with Purdue University: https://bit.ly/3Ov52lT

🔵 Advanced Certificate Program in Data Science with E&ICT Academy, IIT Guwahati: http://bit.ly/3V7ffrh

🌕 Artificial and Machine Learning PGD with E&ICT Academy
NIT Warangal: http://bit.ly/3OuZ3xs

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📌𝐒𝐥𝐢𝐝𝐞𝐒𝐡𝐚𝐫𝐞: https://www.slideshare.net/EdurekaIN
📌𝐂𝐚𝐬𝐭𝐛𝐨𝐱: https://castbox.fm/networks/505?country=IN
📌𝐌𝐞𝐞𝐭𝐮𝐩: https://www.meetup.com/edureka/
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𝐄𝐝𝐮𝐫𝐞𝐤𝐚'𝐬 𝐀𝐖𝐒 𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐩𝐫𝐨𝐠𝐫𝐚𝐦 (Use code "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎"): https://www.edureka.co/aws-certification-training
This video gives you a brief introduction to Amazon Polly. It also discusses the working and use-cases of Amazon Polly. The topics that we will cover in this session are as follows:
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🌕 Professional Certificate Program in DevOps with Purdue University: https://bit.ly/3Ov52lT

🔵 Advanced Certificate Program in Data Science with E&ICT Academy, IIT Guwahati: http://bit.ly/3V7ffrh

🌕 Artificial and Machine Learning PGD with E&ICT Academy
NIT Warangal: http://bit.ly/3OuZ3xs

📢📢 𝐓𝐨𝐩 𝟏𝟎 𝐓𝐫𝐞𝐧𝐝𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐭𝐨 𝐋𝐞𝐚𝐫𝐧 𝐢𝐧 2023 𝐒𝐞𝐫𝐢𝐞𝐬 📢📢
⏩ NEW Top 10 Technologies To Learn In 2023 - https://youtu.be/udD_GQVDt5g

📌𝐓𝐞𝐥𝐞𝐠𝐫𝐚𝐦: https://t.me/edurekaupdates
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📌𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧: https://www.linkedin.com/company/edureka
📌𝐈𝐧𝐬𝐭𝐚𝐠𝐫𝐚𝐦: https://www.instagram.com/edureka_learning/
📌𝐅𝐚𝐜𝐞𝐛𝐨𝐨𝐤: https://www.facebook.com/edurekaIN/
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📌𝐂𝐚𝐬𝐭𝐛𝐨𝐱: https://castbox.fm/networks/505?country=IN
📌𝐌𝐞𝐞𝐭𝐮𝐩: https://www.meetup.com/edureka/
📌𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲: https://www.edureka.co/community/

Got a question on the topic? Please share it in the comment section below and our experts will answer it for you.

Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free) for more information.