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This is a growing list of Linux commands which might come handy for the of Linux users. 1. Found out i had to set the date like this: ...

Wednesday, August 14, 2024

Interview preparation Guide for Software Engineers


- Designing Data-Intensive Applications by Martin Kleppmann
Amazon: https://amzn.to/4dlfPed

- Database Internals by Alex Petrov
Amazon: https://amzn.to/3YI515e

- System Design Interview (Volume 1) by Alex Xu
Amazon: https://amzn.to/3WJzwVV 

- System Design Interview (Volume 2) by Alex Xu
Amazon: https://amzn.to/3M7zEtv 

- Grokking the System Design Interview
https://lnkd.in/ebEwFWbP

- Grokking the Advanced System Design Interview
https://lnkd.in/e_c2CWge

- Donne Martin's System Design Primer
https://github.com/krmadhukar/system-design-primer

- Site Reliability Engineering: How Google Runs Production Systems
https://lnkd.in/edYzQwXW

- The Site Reliability Workbook: Practical Ways to Implement SRE
https://lnkd.in/e9tKypna

- Understanding Distributed Systems
https://amzn.to/4fGzg2H

- Fundamentals of Software Architecture - Mark Richards & Neal Ford
https://amzn.to/3Xdozxv

- Software Architecture: The Hard Parts - Mark Richards & Neal Ford
https://amzn.to/4cp3NyX

- System Design Interview by Lewis Lin
- Hacking the System Design Interview by Stanley Chiang
- Distributed Systems by Tanenbaum
- Building Microservices by Sam Newman

► YouTube Channels

- The Facebook E6 Guy
https://lnkd.in/ehwMYjeD

- ByteByteGo (Alex Xu)
https://lnkd.in/emgA9inH

- InfoQ
https://lnkd.in/eicU_fx3
covers Facebook's TAO architecture: https://lnkd.in/eryi_ZTT

- Jordan Has No Life
https://lnkd.in/ePUshbhX

- Usenix
https://lnkd.in/e5A5s4Xv
https://lnkd.in/ersgNbfg

- MIT Distributed Systems Course
https://lnkd.in/eDjvUJa7

- Amazon Principal Engineer's Channel (A Life Engineered)
https://lnkd.in/egxKHJxU

- Fireship : https://lnkd.in/esqeG7T9

- Martin Kleppmann
https://lnkd.in/eQQ8f2aX

- DistSys Reading Group
https://lnkd.in/ec9FZUbs

- Leslie Lamport's Video Series on Learning TLA+
https://lnkd.in/eFFABEgW

- Carnegie Mellon's Distributed Databases Course
https://lnkd.in/eTdJqU3b


P.S: Image Credits: https://lnkd.in/eAt-mmZF

LinkedIn Credits - https://www.linkedin.com/in/karan-saxena-466b07190



Sunday, July 28, 2024

Free AI/ML LLM Fundamentals Course

 Free AI/ML LLM Fundamentals Course

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Google Courses
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𝟭. 𝗠𝗮𝘁𝗵𝗲𝗺𝗮𝘁𝗶𝗰𝘀 𝗳𝗼𝗿 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴
• Linear Algebra - 3Blue1Brown:
https://lnkd.in/ejApha3z
• Immersive Linear Algebra: https://lnkd.in/ekaUs4Wz
• Linear Algebra - KA: https://lnkd.in/emCEHTq5
• Calculas - KA: https://lnkd.in/emCEHTq5
• Statistics and Probability - KA: https://lnkd.in/e6_SirMr

𝟮. 𝗣𝘆𝘁𝗵𝗼𝗻 𝗳𝗼𝗿 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴
• Real Python: https://realpython.com
• Learn Python - freecodecamp: https://lnkd.in/ejfBftNf
• Python Data Science: https://lnkd.in/g4ZysfEe
• ML for Everybody: https://lnkd.in/ehR6xaGZ
• Intro to ML - udacity: https://lnkd.in/eVudd2Zm

𝟯. 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀
• Neural Networks explained: https://lnkd.in/ehsg362K
• Deep Learning Crash Course: https://lnkd.in/edgfWdEv
• Practical Deep Learning - fast_ai: https://course.fast.ai
• PyTorch Tutorials: https://lnkd.in/dNUfmaCm

𝟰. 𝗡𝗮𝘁𝘂𝗿𝗮𝗹 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 (𝗡𝗟𝗣)
• RealPython - NLP with spaCy: https://lnkd.in/eqPbFf_d
• NLP Guide Kaggle: https://lnkd.in/eT2DsqdN
• Illustrated Word2vec by Jay: https://lnkd.in/e5wK5yg9
• PyTorch RNN from Scratch: https://lnkd.in/eJWj5fUH
• Understanding LSTMN: https://lnkd.in/ed9ZVBnf

𝟱. 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀
• ML Projects in Python: https://lnkd.in/eC_gG8WH
• Super Duper NLP Repo: notebooks.quantumstat.com

I hope you find this helpful.

Kindly Like, Repost & Comment, if you find it helpful.

Follow Arpit Singh for more...


hashtagai hashtagllm hashtagmachinelearning hashtagdatascience hashtagdeveloper

Wednesday, June 26, 2024

Cloud Computing and data storage terminology glossary


1. Control Plane 
The control plane provides management and orchestration across an organization's cloud environment. This is where configuration baselines are set, user and role access are provisioned, and applications sit so they can execute with related services.

2. Cloud computing service Types
IaaS, PaaS & SaaS

Within the cloud deployment models, there are several types of cloud services, including infrastructure, platforms, and software applications. Cloud service models are not mutually exclusive, and you can choose to use more than one in combination or even all of them at once.

Here are the three main cloud service models:
Infrastructure as a Service (IaaS)
IaaS delivers on-demand infrastructure resources, such as compute, storage, networking, and virtualization. With IaaS, the service provider owns and operates the infrastructure, but customers will need to purchase and manage software, such as operating systems, middleware, data, and applications.
Platform as a Service (PaaS)
PaaS delivers and manages hardware and software resources for developing, testing, delivering, and managing cloud applications. Providers typically offer middleware, development tools, and cloud databases within their PaaS offerings.
Software as a Service (SaaS)
SaaS provides a full application stack as a service that customers can access and use. SaaS solutions often come as ready-to-use applications, which are managed and maintained by the cloud service provider.
Serverless computing
Serverless computing in cloud service models is also called Function as a Service (FaaS). This is a relatively new cloud service model that provides solutions to build applications as simple, event-triggered functions without managing or scaling any infrastructure.

3. Containers
Containers are lightweight packages of your application code together with dependencies such as specific versions of programming language runtimes and libraries required to run your software services.
In this way, containers virtualize the operating system and run anywhere, from a private data center to the public cloud or even on a developer’s personal laptop. From Gmail to YouTube to Search, everything at Google runs in containers.

4. What are the benefits of containers?
Separation of responsibility
Containerization provides a clear separation of responsibility, as developers focus on application logic and dependencies, while IT operations teams can focus on deployment and management instead of application details such as specific software versions and configurations.
Workload portability
Containers can run virtually anywhere, greatly easing development and deployment: on Linux, Windows, and Mac operating systems; on virtual machines or on physical servers; on a developer’s machine or in data centers on-premises; and of course, in the public cloud.
Application isolation
Containers virtualize CPU, memory, storage, and network resources at the operating system level, providing developers with a view of the OS logically isolated from other applications.

5. Kubernetes
Kubernetes, also known as K8s, is an open source system for automating deployment, scaling, and management of containerized applications.

It groups containers that make up an application into logical units for easy management and discovery.

6. Virtual Machines
A virtual machine (VM) is a digital representation of a physical computer. It behaves like an actual computer, with its own CPU, memory, and storage. VMs run within a software-based environment, separate from the host system. 
Here are some common uses and benefits of VMs:

Cloud Deployment: VMs are used to build and deploy applications in cloud environments.
Testing and Development: Developers create VMs to test new operating systems or software.
Efficient Resource Utilization: VMs allow multiple virtual environments to run on a single physical infrastructure, reducing the need for many physical servers.
Isolation and Portability: VMs remain independent of each other and can be easily moved between different hosts.
In summary, VMs provide flexibility, cost savings, and efficient resource management. 

7.Virtual Machines vs Containers



8. Microservices
Microservices are an architectural and organizational approach to software development. In this model, software is composed of small, independent services that communicate over well-defined APIs. Here are some key points about microservices:

Independence: Each microservice is developed, deployed, operated, and scaled independently without affecting other services. They don’t share code or implementation with each other.

Specialization: Each service focuses on solving a specific problem or business capability. If a service becomes complex, it can be broken down into smaller services.

Agility: Microservices foster small, independent teams that take ownership of their services. This allows faster development cycles and better overall throughput.

Flexible Scaling: Services can be independently scaled to meet demand for specific features, optimizing infrastructure needs and maintaining availability.

                            Breaking a monolithic application into microservices

9. Monolithic vs. Microservices Architecture

With monolithic architectures, all processes are tightly coupled and run as a single service. This means that if one process of the application experiences a spike in demand, the entire architecture must be scaled. Adding or improving a monolithic application’s features becomes more complex as the code base grows. This complexity limits experimentation and makes it difficult to implement new ideas. Monolithic architectures add risk for application availability because many dependent and tightly coupled processes increase the impact of a single process failure.

With a microservices architecture, an application is built as independent components that run each application process as a service. These services communicate via a well-defined interface using lightweight APIs. Services are built for business capabilities and each service performs a single function. Because they are independently run, each service can be updated, deployed, and scaled to meet demand for specific functions of an application.

10. Edge Computing
Edge computing is a form of computing that is done on-site or near a particular data source, minimizing the need for data to be processed in a remote data center. Edge computing is a distributed computing standard that brings compute services and data storage close to the site where it is needed to speed up the response times and preserve bandwidth.


Data Storage Terminologies
Split Brain situation - In a synchronous replication configuration - a split-brain situation where both arrays serve data for the same volume.


Thursday, May 23, 2024

𝐅𝐑𝐄𝐄 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 𝐲𝐨𝐮 𝐰𝐢𝐥𝐥 𝐫𝐞𝐠𝐫𝐞𝐭 𝐧𝐨𝐭 𝐭𝐚𝐤𝐢𝐧𝐠 𝐢𝐧 𝟐𝟎𝟐𝟒


1 Introduction Generative Al
imp.i384100.net/5gNjVj

2. Generative AI with Large Language Models
imp.i384100.net/k0qRez

2 a) React Fundamentals
imp.i384100.net/9gYeRW

2 b) Angular: imp.i384100.net/eKWR9r

2 c) SEO: imp.i384100.net/xkGnW5

3. Generative Adversarial Networks (GANs) Specialization
imp.i384100.net/DKNLPn

4. Introduction to Artificial Intelligence (AI)
imp.i384100.net/QyQKoA

5. AI Engineering
imp.i384100.net/9gYeRy

6. Natural Language Processing Specialization
imp.i384100.net/rQPgZR

7. Deep Learning Specialization
imp.i384100.net/jrL1k5

8. Generative AI for Data Scientists Specialization
imp.i384100.net/k0qReN

9. IBM Data Science Professional Certificate
imp.i384100.net/AWNK91

10. Introduction to Data Science
imp.i384100.net/GmNDek

11. Learn SQL Basics for Data Science
imp.i384100.net/Vm54E3

12. Excel for Business
imp.i384100.net/g1EojB

13. Python for Everybody
imp.i384100.net/B0MKrL

14. Machine Learning Specialization
imp.i384100.net/WqkYnM

15. SQL for Data Science
imp.i384100.net/Vm54E3

Like and Share with your friends and Help Them 😊

#oops #c++ #cpp #interviewquestions #interview #prep #it #cse #cs #systemdesign

Learning Data Structures and Algorithms from scratch

If I had to start learning Data Structures and Algorithms from scratch, I would begin with these 20 articles to get a head start:
1) 𝐓𝐢𝐦𝐞 𝐂𝐨𝐦𝐩𝐥𝐞𝐱𝐢𝐭𝐲: https://lnkd.in/gWDD83fm

2) 𝐁𝐢𝐠-𝐎 𝐂𝐡𝐞𝐚𝐭 𝐒𝐡𝐞𝐞𝐭: https://lnkd.in/gsaAWbSs

3) 𝐒𝐨𝐫𝐭𝐢𝐧𝐠 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬: https://lnkd.in/g9npW9JN

4) 𝐋𝐢𝐧𝐤𝐞𝐝 𝐋𝐢𝐬𝐭: https://lnkd.in/gXQux4zj

5) 𝐐𝐮𝐞𝐮𝐞: https://lnkd.in/gJaGSafT

6) 𝐒𝐭𝐚𝐜𝐤𝐬: https://lnkd.in/gBtqxeJH

7) 𝐇𝐚𝐬𝐡 𝐓𝐚𝐛𝐥𝐞𝐬: https://lnkd.in/gCfWr7Eg

8) 𝐇𝐞𝐚𝐩𝐬: https://lnkd.in/gS6SVF5A

9) 𝐑𝐞𝐜𝐮𝐫𝐬𝐢𝐨𝐧: https://lnkd.in/gQiasy8H

10) 𝐁𝐚𝐜𝐤𝐭𝐫𝐚𝐜𝐤𝐢𝐧𝐠: https://lnkd.in/g8Vge2p9

11) 𝐓𝐫𝐞𝐞: https://lnkd.in/gRfmJVDf

12) 𝐁𝐢𝐧𝐚𝐫𝐲 𝐒𝐞𝐚𝐫𝐜𝐡 𝐓𝐫𝐞𝐞: https://lnkd.in/g7QYyVWy

13) 𝐓𝐫𝐢𝐞𝐬: https://lnkd.in/gTp3n4CP

14) 𝐁𝐢𝐧𝐚𝐫𝐲 𝐒𝐞𝐚𝐫𝐜𝐡: https://lnkd.in/gKEm_qUK

15) 𝐆𝐫𝐞𝐞𝐝𝐲 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦: https://lnkd.in/gUMnuQ26

16) 𝐃𝐲𝐧𝐚𝐦𝐢𝐜 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠: https://lnkd.in/gtXQsyXT

17) 𝐆𝐫𝐚𝐩𝐡 𝐓𝐡𝐞𝐨𝐫𝐲: https://lnkd.in/g9m8wAmp

18) 𝐃𝐅𝐒 𝐓𝐫𝐚𝐯𝐞𝐫𝐬𝐚𝐥: https://lnkd.in/gNKGuY2q

19) 𝐁𝐅𝐒 𝐓𝐫𝐚𝐯𝐞𝐫𝐬𝐚𝐥: https://lnkd.in/g6bSBgz5

20) 𝐃𝐢𝐣𝐤𝐬𝐭𝐫𝐚: https://lnkd.in/gZEp6FMZ

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