Why no one is talking about this new feature(adding notes on posts) of instagram? It is really awesome 👌

@techinethio

Apple Maps launches on the web to challenge Google Maps

Apple Maps is now available on the web via a public beta, which means you can now access the service directly from your browser.
@Techinethio

Mark vs Musk

Mark
> Successful marriage
> Loving Father
> Makes Katanas and wrestles on his break
> Physically Fit
> Runs companies loved by employees
> Went from Villain to most liked CEO
> Calm, Collected and Precise
> Deeply Genius and Philosophical
> Reads the Bible

Elon
> Obsessed with work
> Multiple failed marriages
> Child wants to never be associated
> Runs companies hated by employees
> Runs False Marketing
> Spends online time insulting others
> Doesn't believe in God

Not picking sides cause I like them both. But clearly I have a favorite, and there's clearly something to be learnt from both. Lol

#Random #Thoughts
@Dagmawi_Babi

Right😂

OpenAI introduced SearchGPT, a prototype search feature. You can join the waitlist to try it.

Key Features
✔️Conversational Search: Offers direct, up-to-date answers with source links.
✔️Publisher Collaboration: Partners with publishers to highlight quality content and support journalism.
✔️User Feedback: Open to a small group for feedback, with plans to integrate into ChatGPT.

@techinethio

We got you recording of our last podcast with Amanuael kebede

https://youtu.be/07H9Sj9ATtE?si=1wGJ9myuEEfABWVD

@Techinethio
@Blogrammer

It's been an incredibly exciting week in the world of AI:

- OpenAI launched a new search tool called SearchGPT
- Meta updated its Llama language model to version 3.1
- Mistral AI released a new and improved Mistral Large 2 model
- DeepMind's AI achieved a silver medal at the International Math Olympiad
- Elon Musk announced plans to develop Grok 2 and 3

TikTok rolls out a new feature that lets you find songs by singing or humming them.
Read More

@techinethio

https://youtu.be/YMwCPfABwHg?si=NMxVvQ3uyiQs4Yk3

Evan You creator of Vue js and vite really great video

Techኢት Podcast S02E07🎙

Guest: Bereket Engida(Telegram)
Host: Tolosa
Date: July 30, 2024
Time: 8:00 PM (2:00LT)

We're excited to feature Beka on Techኢት Podcast! 🎉 Beka is a top developer known for turning unique ideas into reality and solving real-life problems with code. 🚀

Projects💻✨
Loglib:Privacy-first, open-source web analytics.🔒📊
Better Fetch:Efficient data-fetching library.🌐⚡️
Better Call: Enhances remote procedure call functionality. 📞🔧
And more!

Current Role:
Beka currently contributes to Read21 as a remote developer, working on a multi-sensory web application designed to assist struggling readers and students with dyslexia. 📚🌍Additionally, he develops for Elemental, a local company.🏢🔧

✅Apart from his coding endeavors, Beka is also a talented music producer, specializing in creating music for adverts and film scores.🎶🎬

Don't miss out on this fun episode! 🤩 We'll dive into Beka's tech journey, uncover his innovative projects, and learn how he juggles roles at Read21 and Elemental. Plus, we'll geek out on tech trends and soak up his wisdom for budding developers. 🌟💡

@Techinethio
📅 Stay tuned! 🎧📢✨
#TechኢትPodcast

Types of Machine Learning Algorithms

🔍 Supervised Learning
Supervised learning algorithms are trained using labeled data, where input data is tagged with the correct output. They learn a mapping from inputs to outputs, enabling predictions for new data. Common supervised learning algorithms include:
1. Linear Regression: Models the relationship between a dependent variable and independent variables by fitting a linear equation to observed data.
2. Logistic Regression: Estimates probabilities for binary classification tasks using a logistic function.
3. Decision Trees: Predicts the value of a target variable by learning simple decision rules from data features.
4. Random Forests: Ensemble of decision trees used for classification and regression, improving model accuracy and controlling overfitting.
5. Support Vector Machines (SVM): Effective in high-dimensional spaces, primarily used for classification but also regression.
6. Neural Networks: Powerful models that capture complex non-linear relationships, widely used in deep learning applications.

🔍 Unsupervised Learning
Unsupervised learning algorithms work with data sets without labeled responses, aiming to infer the natural structure within the data. Common unsupervised learning techniques include:
1. Clustering: Algorithms like K-means, hierarchical clustering, and DBSCAN group objects based on similarities.
2. Association: Finds rules describing relationships within the data, such as market basket analysis.
3. Principal Component Analysis (PCA): Uses statistical techniques to transform correlated variables into uncorrelated ones.
4. Autoencoders: Special neural networks used to learn efficient codings of unlabeled data.

🔍 Reinforcement Learning
Reinforcement learning algorithms learn to make a sequence of decisions to achieve a goal in an uncertain environment. The agent follows a policy based on actions and learns from the consequences through rewards or penalties. Examples of reinforcement learning algorithms include:
1. Q-learning: A model-free algorithm that learns the value of actions in specific states.
2. Deep Q-Networks (DQN): Combines Q-learning with deep neural networks, learning policies directly from high-dimensional sensory inputs.
3. Policy Gradient Methods: Optimize policy parameters directly instead of estimating action values.
4. Monte Carlo Tree Search (MCTS): Used in decision processes to find optimal decisions by simulating scenarios, notably in games like Go.

These categories provide an overview of common machine learning algorithms, each with its strengths and ideal use cases. Depending on the task at hand, certain algorithms may be better suited than others.
#MachineLearning

Here’s a motivational nudge for your friend to start solving DSA problems on leetCode:

>LeetCode owners check your rank to see how many users are on the platform😂

@techinethio

Well 😁