🔥𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐃𝐞𝐯𝐎𝐩𝐬 𝐜𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠 : https://www.edureka.co/devops-....certification-traini (𝐔𝐬𝐞 𝐂𝐨𝐝𝐞: 𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎)
This Edureka session on ‘Git Interview Questions’ will discuss the most frequently asked questions that you might face in an interview.
This Session will focus on pointers like:
00:00 Introduction
00:50 Beginner Interview Questions
12:28 Intermediate Interview Questions
26:27 Advanced Interview Questions
📝Feel free to comment your doubts in the comment section below, and we will be happy to answer📝

-------𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧---------

🔵 DevOps Online Training:https://bit.ly/3r7xtvQ
🌕 AWS Online Training: https://bit.ly/3r6sawS
🔵 Azure DevOps Online Training:https://bit.ly/3r8shaX
🌕 Tableau Online Training: https://bit.ly/3LMOLGE
🔵 Power BI Online Training: https://bit.ly/3J9uOrP
🌕 Selenium Online Training: https://bit.ly/3jeSvEx
🔵 PMP Online Training: https://bit.ly/3DNgUKX
🌕 Salesforce Online Training: https://bit.ly/3j8VyxW
🔵 Cybersecurity Online Training: https://bit.ly/3LJBoGV
🌕 Java Online Training: https://bit.ly/35K5hrk
🔵 Big Data Online Training: https://bit.ly/3ugVAua
🌕 RPA Online Training: https://bit.ly/3LIqcKT
🔵 Python Online Training:https://bit.ly/3jbsAxr
🌕 Azure Online Training:https://bit.ly/3j8WOBa
🔵 GCP Online Training: https://bit.ly/3LHJb8g
🌕 Microservices Online Training:https://bit.ly/3r7Xwmt
🔵 Data Science Online Training: https://bit.ly/3r9dgFX

---------𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐑𝐨𝐥𝐞-𝐁𝐚𝐬𝐞𝐝 𝐂𝐨𝐮𝐫𝐬𝐞𝐬---------

🔵 DevOps Engineer Masters Program: https://bit.ly/37p4goY
🌕 Cloud Architect Masters Program: https://bit.ly/35LP0SV
🔵 Data Scientist Masters Program: https://bit.ly/3NULA1q
🌕 Big Data Architect Masters Program:https://bit.ly/38qZTud
🔵 Machine Learning Engineer Masters Program:https://bit.ly/3ueP9rm
🌕 Business Intelligence Masters Program: https://bit.ly/3x9qpT5
🔵 Python Developer Masters Program:https://bit.ly/3j8YYkg
🌕 RPA Developer Masters Program: https://bit.ly/3JgqQ0g
🔵 Web Development Masters Program: https://bit.ly/3KlZJT1
🌕 Computer Science Bootcamp Program : https://bit.ly/3Kg70Uh
🔵 Cyber Security Masters Program: https://bit.ly/3uo98UN
🌕 Full Stack Developer Masters Program : https://bit.ly/3NUlVGb
🔵 Automation Testing Engineer Masters Program : https://bit.ly/3E0rFcZ
🌕 Python Developer Masters Program : https://bit.ly/3j8YYkg
🔵 Azure Cloud Engineer Masters Program: https://bit.ly/3NQb9Ax

--------𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐏𝗼𝘀𝘁 𝗚𝗿𝗮𝗱𝘂𝗮𝘁𝗲 𝐂𝐨𝐮𝐫𝐬𝐞𝐬--------
🔵 Artificial and Machine Learning PGD with E & ICT Academy
NIT Warangal: https://bit.ly/3Kji6bb
🌕 Post Graduate Program in DevOps with Purdue University: https://bit.ly/3uoajDH

📢📢 𝐓𝐨𝐩 𝟏𝟎 𝐓𝐫𝐞𝐧𝐝𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐭𝐨 𝐋𝐞𝐚𝐫𝐧 𝐢𝐧 𝟐𝟎𝟐𝟐 𝐒𝐞𝐫𝐢𝐞𝐬 📢📢
⏩𝐓𝐨𝐩 𝟏𝟎 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐭𝐨 𝐋𝐞𝐚𝐫𝐧 𝐢𝐧 𝟐𝟎𝟐𝟮: https://bit.ly/3yhEs7S
⏩𝐓𝐨𝐩 𝟏𝟎 𝐇𝐢𝐠𝐡𝐞𝐬𝐭 𝐏𝐚𝐲𝐢𝐧𝐠 𝐉𝐨𝐛𝐬 𝐅𝐨𝐫 𝟐𝟎𝟐𝟐: https://bit.ly/3HcpPG6
⏩𝐓𝐨𝐩 𝟏𝟎 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞𝐬 𝐟𝐨𝐫 𝟐𝟎𝟐𝟐: https://bit.ly/3FW6F69
⏩𝐓𝐨𝐩 𝟏𝟎 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 𝐟𝐨𝐫 𝟐𝟎𝟐𝟐: https://bit.ly/3gH57mr

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

About Course
What will you learn as a part of this DevOps course?
This DevOps training course is designed keeping in mind the latest trends in the industry. The course focuses on creating a strong base for various DevOps tools including Git, Jenkins, Docker, Ansible, Kubernetes, Prometheus and Grafana, and Terraform. The training is completely hands-on oriented and designed in a way that will help you in becoming a certified practitioner by providing you an intensified training for the best practices about Continuous Development, Continuous Testing, Configuration Management, including Continuous Integration and Continuous Deployment and finally Continuous Monitoring of the software throughout its development life cycle.

What are the skills that you will be learning with our DevOps course?
Upon completion of the DevOps training course, you will be able to:

Understand the DevOps Process and Lifecycle
Manage and keep a track of different versions of the source code using GIT
Use Jenkins and maven to build the application and integrate the CI/CD Pipeline
Manage your infrastructure using Ansible
Build and Deploy containers using Docker
Orchestrate your containerized environment with Kubernetes

Resources that was very useful for me when learning about GNNs that you can check out for more information and from which I've used in the slides:

Cs224w: https://youtube.com/playlist?l....ist=PLoROMvodv4rPLKx
https://distill.pub/2021/gnn-intro/
https://distill.pub/2021/understanding-gnns/
https://www.youtube.com/playli....st?list=PLV8yxwGOxvv
https://youtu.be/8owQBFAHw7E
https://youtu.be/uF53xsT7mjc
https://youtu.be/w6Pw4MOzMuo

0:00 Introduction
1:24 Why graphs
4:13 What is a graph
7:06 Common graph tasks
11:08 Representation of a graph
12:46 - How does a GNN work?
14:35 - Understanding information propagation
17:24 - Key property: Permutation Invariance
19:33 - Key property: Permutation Equivariance
22:22 - Message passing computation
23:53 - GNN Variant: Convolution
26:37 - GNN Variant: Attention
28:39 - Ending

In this video I go through a simple cryptography algorithm in Python. I try to explain everything in simple terms and make it beginner friendly!

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

Check out my previous video on the Vigenere cipher also if you have any questions about this algorithm.

In this video I explain LDA and go through a tutorial paper on how it works using collapsed gibbs sampling. The next video will be a implementation video :)

This method is an old school method and it's not clear how useful it is today, but a lot of course teach this topic so it must have some historical value I feel. It's one of Andrew Ng's most cited papers!

Paper tutorial: https://coli-saar.github.io/cl....19/materials/darling

Timestamps:
0:00 - Introduction
1:18 - What is topic modelling?
2:41 - LDA
13:30 - Posterior Inference
15:09 - Gibbs Sampling
18:06 - Collapsed Gibbs
21:50 - Finding conditional probability
26:48 - Implementation
30:08 - Pseudo code
32:25 - Ending

Prof. Geoffrey Ye Li (Imperial College London)

It has been demonstrated recently that deep learning (DL) has great potential to break the bottleneck of conventional communication systems. In this talk, we present our recent work in DL in wireless communications, including physical layer processing and resource allocation.

DL can improve the performance of each individual (traditional) block in a conventional communication system or jointly optimize the whole transmitter or receiver. Therefore, we can categorize the applications of DL in physical layer communications into with and without block processing structures. For DL-based communication systems with block structures, we present joint channel estimation and signal detection based on a fully connected deep neural network, model-drive DL for signal detection. For those without block structures, we provide our recent endeavors in developing end-to-end learning communication systems with the help of deep reinforcement learning (DRL) and generative adversarial net (GAN).

Judicious resource (spectrum, power, etc.) allocation can significantly improve the efficiency of wireless networks. The traditional wisdom is to explicitly formulate resource allocation as an optimization problem and then exploit mathematical programming to solve it to a certain level of optimality. Deep learning represents a promising alternative due to its remarkable power to leverage data for problem-solving and can help solve optimization problems for resource allocation or can be directly used for resource allocation. We will first present our research results in using deep learning to reduce the complexity of mixed-integer non-linear programming (MINLP). We will then discuss how to use deep reinforcement learning directly for wireless resource allocation with applications in vehicular networks.

Bio:
Dr. Geoffrey Ye Li is the Chair Professor in Wireless Systems in Department of EEE, Imperial College London. Before joining Imperial College London in 2020, he was a professor with Georgia Institute of Technology, GA, USA, for 20 years and a Principal Technical Staff Member with AT&T (Bell) Labs – Research in New Jersey, USA, for around 5 years. He is currently focusing on machine learning and statistical signal processing for wireless communications. His research topics in the past couple decades include machine learning for wireless signal detection and resource allocation, cognitive radios, cross-layer optimisation for spectrum- and energy-efficient wireless networks, OFDM and MIMO techniques for wireless systems, and blind signal processing.

Dr. Geoffrey Ye Li was awarded IEEE Fellow for his contributions to signal processing for wireless communications in 2005. He won several prestigious awards from IEEE Signal Processing Society (Donald G. Fink Overview Paper Award in 2017), IEEE Vehicular Technology Society (James Evans Avant Garde Award in 2013 and Jack Neubauer Memorial Award in 2014), and IEEE Communications Society (Stephen O. Rice Prize Paper Award in 2013, Award for Advances in Communication in 2017, and Edwin Howard Armstrong Achievement Award in 2019). He also received the 2015 Distinguished ECE Faculty Achievement Award from Georgia Tech. He has been recognised as the Highly-Cited Researcher by Thomson Reuters almost every year.

- - -

Wireless ML Seminars is a series of lectures focused on Machine Learning in the wireless space. Invited speakers for the series are leaders in their fields, hailing from respected research institutions worldwide. The seminar series is curated by Prof. Jeff Andrews and Prof. Hyeji Kim from the Wireless Networking and Communications Group at the University of Texas at Austin.

Find out more here: https://sites.utexas.edu/wirelessmlseminars/