🔗 Keras vs. TensorFlow vs. PyTorch: The ultimate showdown for deep learning supremacy! 🚀

🤔 Keras: The user-friendly champion! Perfect for beginners and rapid prototyping.

⚡️ TensorFlow: The powerhouse! Great for complex projects with extensive capabilities.

🔥 PyTorch: The flexible innovator! With its dynamic computation graph, it’s a favorite among researchers.

👉 @codeprogrammer

Free course on learning deep learning concepts

A conceptual and architectural journey through computer vision models in #deeplearning, tracing the evolution from LeNet and AlexNet to ResNet, EfficientNet, and Vision Transformers.

The #course explains the design principles behind skip connections, bottleneck blocks, identity preservation, depth/width trade-offs, and attention.

Each chapter combines clear illustrations, historical context, and side-by-side comparisons to show why architectures look the way they do and how they process information.

Grab it on YouTube
https://youtu.be/tfpGS_doPvY?si=1L_NvEm3Lwpj_Jgl

👉 @codeprogrammer

PyTorch 2.9 has been released, an update focused on performance, portability, and developer convenience.

The fresh version brings a stable libtorch ABI for C++/CUDA extensions, symmetric memory for multi-GPU kernels, extended wheel package support for ROCm, XPU, and #CUDA 13, as well as improvements for Intel, Arm, and x86 platforms.

The release includes 3216 commits from 452 contributors, and #PyTorch 2.9 continues to develop the open source #AI ecosystem worldwide.

Full analysis: https://hubs.la/Q03NNKqW0

👉 @codeprogrammer

Question: What are Python set comprehensions?

Answer:Set comprehensions are similar to list comprehensions but create a set instead of a list. The syntax is:

{expression for item in iterable if condition}

For example, to create a set of squares of even numbers:

squares_set = {x**2 for x in range(10) if x % 2 == 0}

This will create a set with the values

{0, 4, 16, 36, 64}

https://t.iss.one/DataScienceQ 🌟

AI Engineering roadmap that beginners can actually follow. Everything is based on 100% free, open-source, and community resources

All resources can be found here: GitHub

👉  @codeprogrammer

🖥 Want your Telegram bot to respond using ChatGPT?

Do it in a couple of minutes: just install the python-telegram-bot library, add your #OpenAI #API key and bot token, and the bot will start replying to all messages using #ChatGPT.

from telegram import Update
from telegram.ext import ApplicationBuilder, MessageHandler, filters, ContextTypes
from openai import OpenAI


Specify your keys
OPENAI_API_KEY = "sk-..."  
TELEGRAM_TOKEN = "123456789:ABC..."

client = OpenAI(api_key=OPENAI_API_KEY)

async def handle_message(update: Update, context: ContextTypes.DEFAULT_TYPE):
    user_text = update.message.text

    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": user_text}]
    )

    await update.message.reply_text(response.choices[0].message.content)

app = ApplicationBuilder().token(TELEGRAM_TOKEN).build()
app.add_handler(MessageHandler(filters.TEXT & ~filters.COMMAND, handle_message))
app.run_polling()

https://t.iss.one/CodeProgrammer 👍

🎓 Stanford has released a new course: “Transformers & Large Language Models”

The authors are the Amidi brothers, and three free lectures are already available on YouTube. This is probably one of the most systematic introductory courses on modern LLMs.

Course content:

• Transformers: tokenization, embeddings, attention, architecture
• #LLM basics: Mixture of Experts, decoding types
• Training and fine-tuning: SFT, RL, LoRA
• Model evaluation: LLM/VLM-as-a-judge, best practices
• Tricks: RoPE, attention approximations, quantization
• Reasoning: scaling during training and inference
• Agentic approaches: #RAG, tool calling

If you are already familiar with this topic — it’s a great opportunity to refresh your knowledge and try implementing some techniques from scratch.

https://cme295.stanford.edu/syllabus/

https://t.iss.one/CodeProgrammer 🌟

Stack Overflow is not dead, it is making a powerful comeback

Yes, our beloved Stack Overflow, where we all once searched for answers to "why my code doesn't work," is back in the game. They have launched Stack Overflow AI
https://stackoverflow.ai/

At first glance, it might resemble ChatGPT or other AI tools, but the key difference is that it operates based on a huge developer knowledge base that Stack Overflow has been building for years

It seems Stack Overflow has found a way to be indispensable again. 💜

👉 @codeprogrammer

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