Ethics of AI

The Ethics of AI is a free online course created by the University of Helsinki. The course is for anyone who is interested in the ethical aspects of AI – we want to encourage people to learn what AI ethics means, what can and can’t be done to develop AI in an ethically sustainable way, and how to start thinking about AI from an ethical point of view.
Artificial intelligence is already a part of our daily lives. When we post pictures to social media, search online or ask questions from chatbots, we’re interacting with AI. Authorities, such as cities, rely on AI for providing public services. And governments are seeking solutions to global problems by using algorithmically produced knowledge. The goal of this course is to help you to develop your own skills for ethical thinking. You can complete the course at your own pace.

🆓 Free Online Course
🏃‍♂️ Self paced
Teacher 👨‍🏫 :Anna-Mari Rusanen, Jukka K. Nurminen, Santeri Raisanen, Sasu Tarkoma, Saara Halmetoja
Source: University of Helsinki

🔗 Course Link

#AI #Ethics
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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.

Apache Airflow free courses

Apache Airflow
🎬 34 video lesson
Duration ⏰: 6 hours worth of material
Teacher: Marc Lamberti
🔗 Course link
Airflow - zero to hero🎬 7 video lessons
Duration ⏰: 1-2 hours worth of material
🔗 Course link

Apache Airflow with Python
🎬 11 video lessons
Duration ⏰: 1-2 hrs
🔗 Course link

Airflow 2.0
🎬 5 video lesson
Duration ⏰: 1-2 hours worth of material
Teacher: CodinEric
🔗 Course link

THE ULTIMATE GUIDE TO APACHE AIRFLOW (No Video)
📔 Read online
Resource: Qubole
🔗 Course link

Apache Airflow Tutorial (No Video)by: Rafael Pierre
🔗 Part 1: Data Pipeline Orchestration on Steroids🔗 Part 2: Complete Guide for a Basic Production Installation Using LocalExecutor

Official tutorialhttps://airflow.apache.org/docs/apache-airflow/stable/tutorial.html

Book
Data Pipelines with Apache Airflow book 📚
Authors: Bas Harenslak, Julian de Ruiter
📑 482 pages
🔗 Book link

#Apache #Airflow #Airflow_with_Python
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Large Language Models with Python

Learn how to build your own large language model, from scratch. This course goes into the data handling, math, and transformers behind large language models. You will use Python.

✅ Free Online Course
⌛️Duration: 5-6 hours
? 15-20 major concepts
? Video Lecture
?‍♂️ Self paced
Source: freecodecamp

? Course Link

#LLM #programming #Python
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YouTube

Create a Large Language Model from Scratch with Python – Tutorial

Learn how to build your own large language model, from scratch. This course goes into the data handling, math, and transformers behind large language models. You will use Python. ***✏️*** Course developed by @elliotarledge ***💻*** Code and course resources: https…

I've created this pdf explaining basic features of newest GPT-4o

let me know if you need more content like this

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