TCS CodeVita Cheat Sheet .pdf

📚 Title: Introduction to Artificial Intelligence (2024)

📸 Book in private resources channel

https://t.me/c/2109572262/1427

Channel links

Want to be a backend architect ?

learn :

1. Microservices Design
Service decomposition, Bounded contexts, Resilience (Circuit Breaker, Bulkheads)

2. Distributed Systems Fundamentals
CAP Theorem, Event sourcing, CQRS, Data consistency models (ACID vs. BASE)

3. High-Performance Data Management
Database partitioning, Index optimization, NoSQL data modeling

4. Advanced API Design
gRPC, GraphQL, API Gateways, Asynchronous APIs

5. Event-Driven Architecture
Kafka, Message queues, Pub/Sub patterns, Saga pattern

6. Cloud-Native Patterns
Container orchestration (Kubernetes), Serverless, Multi-cloud strategies

7. Observability
Distributed tracing (OpenTelemetry), Centralized logging (ELK), Real-time monitoring

8. Infrastructure as Code
Terraform, Helm, Configuration management best practices

9. Advanced Security
Zero Trust, OAuth2, JWT, Data encryption in transit and at rest

10. Scaling Strategies
Load balancing, Sharding, Horizontal vs. vertical scaling

JavaScript Array Methods

👉 JavaScript provides powerful built-in array methods that eliminate the need for traditional loops, making code more readable and maintainable.
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✔️ 𝐂𝐨𝐫𝐞 𝐀𝐫𝐫𝐚𝐲 𝐌𝐞𝐭𝐡𝐨𝐝𝐬 𝐄𝐱𝐩𝐥𝐚𝐢𝐧𝐞𝐝
1. 𝐦𝐚𝐩()
◾️ Creates a new array by transforming each element
◾️ Returns: New array of same length
◾️ Example:
const numbers = [1, 2, 3];
const doubled = numbers. map(num => num * 2); // [2, 4, 6]

2. 𝐟𝐢𝐥𝐭𝐞𝐫()
◾️ Creates new array with elements that pass a test
◾️ Returns: New array (possibly shorter)
◾️ Example:
const numbers = [1, 2, 3, 4, 5];
const evenNumbers = numbers.filter(num => num % 2 === 0); // [2, 4]
@javascript_resources
3. 𝐟𝐢𝐧𝐝()
◾️ Returns first element that matches condition
◾️ Returns: Single element or undefined
◾️ Example:
const numbers = [1, 2, 3, 4, 5];
const firstEven = numbers.find(num => num % 2 === 0); // 2

4. 𝐟𝐢𝐧𝐝𝐈𝐧𝐝𝐞𝐱()
◾️ Returns index of first matching element
◾️ Returns: Number (index) or -1 if not found
◾️ Example:
const numbers = [1, 2, 3, 4, 5];
const firstEvenIndex = numbers.findIndex(num => num % 2 === 0); // 1

5. 𝐟𝐢𝐥𝐥()
◾️ Fills array elements with static value
◾️ Returns: Modified original array
◾️ Example:
const array = [1, 2, 3, 4];
array.fill(0); // [0, 0, 0, 0]

6. 𝐬𝐨𝐦𝐞()
◾️ Tests if ANY element passes condition
◾️ Returns: Boolean
◾️ Example:
const numbers = [1, 2, 3, 4, 5];
const hasEven = numbers.some(num => num % 2 === 0); // true

7. 𝐞𝐯𝐞𝐫𝐲()
◾️ Tests if ALL elements pass condition
◾️ Returns: Boolean
◾️ Example:
const numbers = [2, 4, 6, 8];
const allEven = numbers.every(num => num % 2 === 0); // true

✔️ 𝐊𝐞𝐲 𝐁𝐞𝐧𝐞𝐟𝐢𝐭𝐬
➥ More readable code
➥ Reduced chance of errors
➥ Chainable operations
➥ Immutable operations (for methods that return new arrays)
➥ Built-in iteration handling
@javascript_resources
✔️𝐁𝐞𝐬𝐭 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞𝐬
➥ Use map() for transformations
➥ Use filter() for subset creation
➥ Use find() for single element search
➥ Prefer these methods over traditional for loops when possible
➥ Chain methods for complex operations

12 Essential Math Theories for AI
Understanding AI requires a foundation in core mathematical concepts. Here are twelve key theories that deepen your AI knowledge:

Curse of Dimensionality:
Challenges with high-dimensional data.
Law of Large Numbers:
Reliability improves with larger datasets.
Central Limit Theorem:
Sample means approach a normal distribution.
Bayes' Theorem:
Updates probabilities with new data.
Overfitting & Underfitting:
Finding balance in model complexity.
Gradient Descent:
Optimizes model performance.
Information Theory:
Efficient data compression.
Markov Decision Processes:
Models for decision-making.
Game Theory:
Insights on agent interactions.
Statistical Learning Theory:
Basis for prediction models.
Hebbian Theory:
Neural networks learning principles.
Convolution:
Image processing in AI.

Familiarity with these theories will greatly enhance understanding of AI development and its underlying principles. Each concept builds a foundation for advanced topics and applications.

Top 20 OS for Cyber Security Nerds:

Here's a complete list of the top virtual machines designed for various cybersecurity domains, from Pentesting and Red Teaming to Digital Forensics and Privacy:
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💿 Kali Purple (SOC-in-a-box):
https://lnkd.in/d63U2jst

💿 Kali Linux (Pentesting):
https://lnkd.in/dfvvCUeh

💿 Predator-OS (Pentesting):
https://predator-os.ir/

💿 BlackArch Linux (Pentesting):
https://lnkd.in/dQuQV4SK

💿 BackBox (Pentesting):
https://www.backbox.org/

💿 Kookarai (Pentesting):
https://lnkd.in/d-4ckJ97

💿 Parrot Safety Operating System (Red and Blue Equipment Operation):
https://parrotsec.org/

💿 VM command (Windows-based Pentesting/Red Teaming):
https://lnkd.in/dec8_V3B

💿 Whonix (Privacy and Anonymity):
https://lnkd.in/dpWagU2f

💿 Tails (Privacy and Anonymity):
https://tails.net/

💿 Qubes OS (hypervisor):
https://www.qubes-os.org/

💿 Mandiant Threat Pursuit (Windows-based threat intelligence and hunting):
https://lnkd.in/d-N4Dt9x

💿 Tsurugi Linux (Digital Forensics and OSINT):
https://lnkd.in/dsr-ekeB

💿 SIFT (Digital Forensics) Workstation:
https://lnkd.in/dmnZRNNP

💿 CSI Linux (Digital Forensics):
https://csilinux.com/

💿 CAINE (Digital Forensics):
https://lnkd.in/dYn9b7Hs

💿 RedHunt Labs-OS Linux (adversary emulation and threat hunting):
https://lnkd.in/db5sd6h3
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💿 FLARE-VM (Reverse Engineering):
https://lnkd.in/ds9s4Wdz

💿 REMnux (Reverse Engineering/Malware Analysis):
https://remnux.org/

💿 Trace Labs OSINT VM (OSINT to find missing persons):
https://lnkd.in/dsymX2KG

💿 Security Onion Solutions, LLC (threat hunting, network security monitoring, and log management):
https://lnkd.in/d4r6myav

Free Courses by Google
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1 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐭𝐨 𝐀𝐥:In Generative AI with Large Language Models (LLMs), you’ll learn the fundamentals of how generative AI works, and how to deploy it in real-world applications.

🪢Check this out

https://lnkd.in/gzJqEsR9

2.𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈 𝐰𝐢𝐭𝐡 𝐋𝐚𝐫𝐠𝐞 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥𝐬:

🔗Check this out

https://lnkd.in/guWGktXk

3.𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐝𝐯𝐞𝐫𝐬𝐚𝐫𝐢𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤𝐬 (𝐆𝐀𝐍𝐬) 𝐒𝐩𝐞𝐜𝐢𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Break into the GANs space. Master cutting-edge GANs techniques through three hands-on courses!

🪢Check this out

https://lnkd.in/gt6wZfij

4.𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐭𝐨 𝐀𝐫𝐭𝐢𝐟𝐢𝐜𝐢𝐚𝐥 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 (𝐀𝐈)

🔗Check this out

https://lnkd.in/gwztqdAA

5.𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈 𝐏𝐫𝐢𝐦𝐞𝐫

🪢Check this out

https://lnkd.in/gzjfhy5r

6. 𝐍𝐚𝐭𝐮𝐫𝐚𝐥 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 𝐒𝐩𝐞𝐜𝐢𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧

🔗Check this out

https://lnkd.in/gHjEK4DC

7. 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐭𝐨 𝐀𝐥: An overview of AI tools for project managers, executives, and students starting their AI career.

🪢Check this out

https://lnkd.in/grZQem-b

8. 𝐖𝐡𝐚𝐭 𝐈𝐬 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐥?: Learn about the basics, history, working principles, and ethical implications of Generative AI.

🔗Check this out

https://lnkd.in/ghhvM9Ri

9. 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐥:

🪢Check this out

https://lnkd.in/gDb-Gqgf

10. 𝐒𝐭𝐫𝐞𝐚𝐦𝐥𝐢𝐧𝐢𝐧𝐠 𝐘𝐨𝐮𝐫 𝐖𝐨𝐫𝐤 𝐰𝐢𝐭𝐡 𝐁𝐢𝐧𝐠 𝐂𝐡𝐚𝐭:
Utilize Microsoft Bing Chat to automate and streamline tasks effectively.

🔗Check this out

https://lnkd.in/gZNhsSS4

11. 𝐄𝐭𝐡𝐢𝐜𝐬 𝐢𝐧 𝐭𝐡𝐞 𝐀𝐠𝐞 𝐨𝐟 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐥: Address ethical concerns in deploying Generative AI, understanding the ethical analysis framework.

🪢Check this out

https://lnkd.in/dD63DHUs
Follow @javascript_resources for more
12. 𝐌𝐢𝐜𝐫𝐨𝐬𝐨𝐟𝐭 𝐀𝐳𝐮𝐫𝐞 𝐀𝐈 𝐅𝐮𝐧𝐝𝐚𝐦𝐞𝐧𝐭𝐚𝐥𝐬
Learn how to use Azure Machine Learning to create and publish models without writing code.

🔗Check this out

https://lnkd.in/dM6bnkKH
Follow @javascript_resources for more

Top 50 system design interview terminologies

System design interview performance is essential to assess a candidate's ability to develop scalable and efficient systems. Familiarity with key terminologies is crucial for success in these interviews. Here are the top 50 system design interview terminologies, complete with definitions, examples, and additional resources for learning.

Scalability: The capacity of a system to handle increased load by adding resources, such as more servers to manage rising web traffic.

Load Balancer: Dividing incoming network traffic among multiple servers to distribute the load evenly, illustrated by load balancing web traffic across multiple EC2 instances using AWS Elastic Load Balancer Service.

Microservices: An architectural pattern that structures applications as a collection of loosely coupled services, like breaking down a monolithic application into independent services for user management, payments processing, and notifications.

CAP Theorem: A principle that in a distributed system, only two out of three guarantees can be achieved; Consistency, Availability, and Partition Tolerance must be balanced.

Sharding: Breaking a large database into smaller shards for better management, for instance, segmenting a user database based on geographic region.

Latency: The time it takes for data to travel between two points, notable in message delivery delay through a chat application.

Throughput: The amount of data a system processes in a given timeframe, such as requests processed by a web server in one second.

Cache: A component that stores data to speed up future requests, like implementing Redis caching for repeated database queries.

Content Delivery Network (CDN): A geographically dispersed server system that delivers web content based on user location, exemplified by using Cloudflare CDN for faster webpage loading.

REST API: A style of architectural design for building web services where data is accessed via HTTP requests, commonly seen in Social Media APIs.

GraphQL: A powerful querying language for data, more efficient and flexible than REST APIs, as demonstrated in querying user information with a single request.

ACID: A set of properties ensuring reliable database transaction processing; Atomicity, Consistency, Isolation, and Durability.

BASE: An alternative to ACID prioritizing Availability and Partition tolerance over strict Consistency, with the core principles of Basically Available, Soft state, and Eventually consistent systems.

NoSQL: A database type focused on storing and retrieving data modeled differently from traditional relational databases, like using MongoDB for document-based data stores.

SQL: The standard language for managing data in relational databases, involving writing queries to retrieve data.

Database Indexing: A technique to enable quick data searching and access in databases, such as creating an index on the User ID column for faster searches.

Replication: Copying and maintaining database objects across multiple databases in a distributed system to improve availability, demonstrated by replicating a database across geographical locations.

Failover: A backup operational mode where system components take over functions if the primary component fails, like automatic failovers in internet applications.

API Gateway: A server that manages API requests, applies security policies, and forwards requests to back-end services, shown in the utilization of AWS API Gateway.

Service Mesh: An infrastructure layer facilitating service-to-service communication in microservices, commonly seen in the integration of Istio for managing interactions.

Serverless Computing: Cloud computing allowing dynamic resource allocation without server provisioning, as seen in running backend code with AWS Lambda.

System Design Interview: Design WhatsApp

In this system design interview scenario, we’re asked to design a messaging app similar to WhatsApp.

While a real interview might focus on one or more functionalities of the app, in this article, we’ll take a high-level overview of the system’s architecture, and then you could explore specific areas in more depth if needed.

Clarifying Functional Requirements
Clarifying Non-functional requirements
Estimation: Data Math
With 10 billion daily messages, we have roughly 10B messages / 86,400 seconds per day = 115,740 messages per second (MPS). Doubling within a year means we should plan for 115,740 * 2 = 231,480 MPS.

Assuming 200 bytes per message, daily storage is 10B messages * 200 bytes = 2 terabytes (TB). And yearly storage with growth is approximately 2TB * 365 days * 2 = 1.5 petabytes (PB).
It’s important to note that we calculated averages, but systems need to handle peak traffic, which could be significantly higher than the average MPS.

🔗 Read More

⌨️ Encrypt PDF files using Python

Data Structures Cheat Sheet

In this article, we will provide an introduction to data structures, offering examples of each structure and illustrating how they could be represented in Memgraph. Among these structures, graphs stand out as non-linear data structures composed of a finite set of nodes, connected by relationships. @javascript_resources
They are used to tackle real-world problems in areas such as networks, knowledge graphs or fraud detection cases.

🔗 Read More

Don't worry guys i am moving some copyrighted books to private resources channel. Just to avoid copyright ©️ 🙂 🙃 😌
Stay tuned.

Here is the private channel link for resources like books and courses:https://t.me/+eweUkdFwlho4YjRl

If you have any particular request for book or course send it here
This is the chat group aka backup link:

https://t.me/+2KE896TMwHkxOWE1