pyhton 
text = "Khoor Plnh"
shift = 3

def ceasar(message, offset, mode="encrypt"):
    alphabet = "abcdefghijklmnopqrstuvwxyz"
    encrypted_text = ""
    
    if mode == "decrypt":
        offset = -offset  # Reverse the shift for decryption

    for char in message.lower():
        if char == " ":
            encrypted_text += char
        else:
            index = alphabet.find(char)
            new_index = (index + offset) % len(alphabet)
            encrypted_text += alphabet[new_index]
    
    print(f"{mode.capitalize()}ed text:", encrypted_text)

# Decrypt the text
ceasar(text, shift, mode="decrypt")

it takes me a while to figure how it works and it was so simple afterwards

Hey everyone,

It’s that time of the semester—final exams, assignments, and projects all hitting at once! I’ll be taking a short break(like I did in the past😁) from posting to give these my full attention
and I’ll be back soon with more to share once the chaos settles. Wish me luck, and good luck to anyone else in the same boat!🫡

---

I have a confession to make
Tech Guys are such an amazing souls :)

Nvidia Keynoat at CES
• youtube.com/watch?v=k82RwXqZHY8

Watching a bunch of epic things. Like they trained a neural net to generate pixels so that in the consumer devices not a lot of computation happens.

Also unveiled the new GeForce RTX 50 series with the Blackwell architecture

#TechEvents #Events #Nvidia
@Dagmawi_Babi

Thoughts also have Thermal Motion.

When you expose a piece of metal to fire, it really heats up, and that happens (among other reasons) cause the atoms inside the metal are moving around so fast and causing so much friction to happen. That's called thermal motion.

I think it's the same with our thoughts. When you're exposed to heat, your head is filled with so many moving thoughts. Often causing so much friction and heating you up even more.

So next time your thoughts are causing too much friction, remember to literally cool down as well.

🌟 My Python + ML Journey: The Last Few Days in a Nutshell 🌟

Hey friends! 🚀 The past few days have been a rollercoaster of learning, and I’m super excited to share my progress with you! Here’s the breakdown:

🔹 Day 1 & 2: Tackled the basics of Python! 🐍
- ✅ List handling
- ✅ Dictionaries
- ✅ File handling

🔹 Day 3: The BIG one! 🧠✨
I finally took the first step into the Machine Learning world—something I’ve been hyped about for over a month! But before we get to that...

Here’s a funny little hiccup: I got home from uni all pumped to start, only to realize… I LEFT MY PC CHARGER AT UNIVERSITY! 🤦‍♂️
Shoutout to my lifesaver, *Gumball*, for coming to the rescue with his charger. You’re the real MVP! 🙌

Once I powered up, here’s what I got done:
1️⃣ Set up my Anaconda environment 🛠 for smooth ML workflows.
2️⃣ Configured Jupyter Notebook for interactive coding goodness.

Then, I dived into NumPy, the backbone of data science! 📊
Here’s what I explored:
- Understanding Data Types & Attributes
- Creating & manipulating arrays
- Viewing matrices like a pro
- Comparing arrays and calculating:
- Mean
- Standard deviation
- Variance

The hiccup delayed me a bit, but honestly, every step felt like leveling up in a game! 🎮

🔥 I’m more pumped than ever for what’s next! Stay tuned as I continue diving into the world of ML. And let me know if you’ve got tips or resources to share! 👇

#PythonJourney #MachineLearning #NumPy #CodingAdventures #NeverStopLearning

🌟Today’s Highlights!** 🌟

Hey friends! 🚀 Another day, another step forward in my Python + Machine Learning adventure. Here’s what I explored today—and let me tell you, it was as exciting as it was challenging!

---

🔹 **Arithmetic Operations & Dot Products** 🔢
- Got my hands dirty with matrix arithmetic**—adding, subtracting, and multiplying like a math wizard.(already forgot APPLIED 1😁)
- Took it a notch higher with **dot products, and let’s just say… vectors have never been cooler! 🎯

🔹 **Sorting Like a Pro** 🗂
- Experimented with sorting arrays efficiently and gained some cool insights into how to arrange data seamlessly!

🔹 **Extracting Data from Images as Arrays** 🖼➡️📊
- This part was absolutely mind-blowing!
- I learned how to treat **images as Numpy arrays**, exploring their data like a detective. 🕵️‍♂️
- It’s amazing to think how ML models “see” images and work with their numerical data.

---

🔥 **Today’s Takeaway**: I’m slowly starting to see how these smaller building blocks come together for big-picture machine learning applications. It feels like assembling a puzzle—and each piece makes me more eager to see the final masterpiece!

Got any resources, tips, or fun challenges I should try? Let me know below! 👇

#PythonJourney #MachineLearning #NumPy #ImageProcessing #CodingLife #NeverStopLearning

here some exercises to tackle Numpy basics

And the V3 model was trained for just $5.6 million—significantly less than competitors like OpenAI, which spent over $100 million to train GPT-4.🤯

🔅 Trump calls China's DeepSeek AI a "wake-up call"

The sudden rise of a Chinese startup called DeepSeek sent U.S. tech stocks tumbling Monday. DeepSeek says it created an artificial intelligence model in much less time and for much less money than U.S. companies. President Trump called it a "wake-up call."

ChatGPT going personal 😂

the time i kill is killing me.

🚀 **Diving into Pandas: Data Mastery Begins! 🐼📊

Today, I took a deep dive into **Pandas, one of the most powerful Python libraries for data manipulation! Here’s what I explored:

🔹 **Anatomy of DataFrames – Understanding the core structure of rows, columns, and indexes.
🔹 Series & DataFrames – The building blocks of Pandas for handling structured data.
🔹 Working with CSVs – Importing, exporting, and managing datasets with ease.
🔹 Denoscriptive Statistics – Summarizing data using `.describe()`, `.info()`, and other key methods.
🔹 Selecting & Viewing Data – Filtering rows, slicing columns, and accessing data efficiently.
🔹 Manipulating Data** – Adding, removing, and transforming data like a pro!

Pandas is the backbone of data science and machine learning, and I’m just getting started. Let’s keep pushing forward! 💡📈

#Python #Pandas #DataScience #LearningJourney

some of the main functions from todays lesson
🔹 `regex()` & `str.replace()` – Cleaning messy text data using pattern matching. 🔍✨
🔹 `crosstab()`– Summarizing relationships between categorical variables like a pro. 📊
🔹 `fillna()`– Handling missing data by filling in gaps with smart defaults. 🔄
🔹 'dropna()`– Removing incomplete data to keep things clean and accurate. 🧹

With these tools, data manipulation is becoming second nature! On to the next challenge. 🚀💡

#Python #Pandas #DataScience #DataCleaning

What Is Generative AI?

Author: Pinar Seyhan Demirdag
Level: Beginner

⏰ Duration: 1h 3m

Learn about the basics of generative AI, including its history, popular models, how it works, ethical implications, and much more.

Topics: Generative AI Tools, Generative AI, Artificial Intelligence