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
Follow @javascript_resources for more
💿 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.

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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.

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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