Machine Learning Roadmap 2026
#MachineLearning #DeepLearning #AI #NeuralNetworks #DataScience #DataAnalysis #LLM #python
https://news.1rj.ru/str/CodeProgrammer
#MachineLearning #DeepLearning #AI #NeuralNetworks #DataScience #DataAnalysis #LLM #python
https://news.1rj.ru/str/CodeProgrammer
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Perfect for: Training neural networks without selling your kidney. Running Jupyter 24/7. Testing that crazy idea at 3 AM. Deploying models that actually need to scale. Scraping data without rate limits ruining your day.
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https://fotbo.com/
Built by devs who got tired of overpriced cloud providers. Also, there's a monkey 🐒
The deal: Fast NVMe storage, European servers, full control. No surprise bills, no corporate jargon, no waiting days for support.
The specs: ⚡ NVMe SSD that actually makes a difference 🌍 Netherlands • Poland • Germany 💰 Starting at €4.80/month (yeah, really) 🔧 Do whatever you want — it's your server 📊 Outperforms AWS, DigitalOcean & Vultr in benchmarks
Perfect for: Training neural networks without selling your kidney. Running Jupyter 24/7. Testing that crazy idea at 3 AM. Deploying models that actually need to scale. Scraping data without rate limits ruining your day.
🎁 -35% OFF FIRST MONTH Coupon: MONKEY35
https://fotbo.com/
Built by devs who got tired of overpriced cloud providers. Also, there's a monkey 🐒
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YOLO Training Template
Manual data labeling has become significantly more convenient. Now the process looks like in the usual labeling systems - you just outline the object with a frame and a bounding box is immediately created.
The platform allows:
• to upload your own dataset
• to label manually or auto-label via DINOv3
• to enrich the data if desired
• to train a #YOLO model on your own data
• to run inference immediately
• to export to ONNX or NCNN, which ensures compatibility with edge hardware and smartphones
All of this is available for free and can already be tested on #GitHub.
Repo:
https://github.com/computer-vision-with-marco/yolo-training-template
👍 Top Channels on Telegram 🌟
Manual data labeling has become significantly more convenient. Now the process looks like in the usual labeling systems - you just outline the object with a frame and a bounding box is immediately created.
The platform allows:
• to upload your own dataset
• to label manually or auto-label via DINOv3
• to enrich the data if desired
• to train a #YOLO model on your own data
• to run inference immediately
• to export to ONNX or NCNN, which ensures compatibility with edge hardware and smartphones
All of this is available for free and can already be tested on #GitHub.
Repo:
https://github.com/computer-vision-with-marco/yolo-training-template
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Forwarded from Machine Learning with Python
🤖 Machine Learning Tutorials Repository
1. Python
2. Computer Vision: Techniques, algorithms
3. NLP
4. Matplotlib
5. NumPy
6. Pandas
7. MLOps
8. LLMs
9. PyTorch/TensorFlow
🔗 GitHub: https://github.com/patchy631/machine-learning/tree/main
⭐️ https://news.1rj.ru/str/DataScienceT
1. Python
2. Computer Vision: Techniques, algorithms
3. NLP
4. Matplotlib
5. NumPy
6. Pandas
7. MLOps
8. LLMs
9. PyTorch/TensorFlow
git clone https://github.com/patchy631/machine-learning🔗 GitHub: https://github.com/patchy631/machine-learning/tree/main
⭐️ https://news.1rj.ru/str/DataScienceT
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Collection of books on machine learning and artificial intelligence in PDF format
Repo: https://github.com/Ramakm/AI-ML-Book-References
#MACHINELEARNING #PYTHON #DATASCIENCE #DATAANALYSIS #DeepLearning
👉 @codeprogrammer
Repo: https://github.com/Ramakm/AI-ML-Book-References
#MACHINELEARNING #PYTHON #DATASCIENCE #DATAANALYSIS #DeepLearning
👉 @codeprogrammer
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Best GitHub repositories to learn AI from scratch in 2026:
1. Andrej Karpathy
https://github.com/karpathy/nn-zero-to-hero
2. Hugging Face Transformers
https://github.com/huggingface/transformers
3. FastAI/fastbook
https://github.com/fastai/fastbook
4. Made-With-ML
https://github.com/GokuMohandas/Made-With-ML
5. ML System Design
https://github.com/chiphuyen/machine-learning-systems-design
6. Awesome Generative AI guide
https://github.com/aishwaryanr/awesome-generative-ai-guide
7. Dive into Deep Learning
https://github.com/d2l-ai/d2l-en
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Господи, похоже, это канал о самых конченных и отвратительных выходках отечественных горе-бизнесменов.
Если ты думал, что ниже Вайлдберрис, К&Б и Пятерочки падать уже некуда, то ты сильно заблуждался.
Подписан — значит вооружен!
Если ты думал, что ниже Вайлдберрис, К&Б и Пятерочки падать уже некуда, то ты сильно заблуждался.
Подписан — значит вооружен!
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Beautiful Soup — a library for extracting data from HTML and XML files, ideal for web scraping.
pip install beautifulsoup4
from bs4 import BeautifulSoup
import requests
html_doc = "<html><body><p class='text'>Hello, world!</p></body></html>"
soup = BeautifulSoup(html_doc, 'html.parser') # or 'lxml', 'html5lib'
print(soup.p.text) # Hello, world!
# First found element
first_p = soup.find('p')
# Search by class or attribute
text_elem = soup.find('p', class_='text')
text_elem = soup.find('p', {'class': 'text'})
# All elements
all_p = soup.find_all('p')
all_text_class = soup.find_all(class_='text')
a_tag = soup.find('a')
print(a_tag['href']) # value of the href attribute
print(a_tag.get_text()) # text inside the tag
print(a_tag.text) # alternative# Moving to parent, children, siblings
parent = soup.p.parent
children = soup.ul.children
next_sibling = soup.p.next_sibling
# Finding the previous/next element
prev_elem = soup.find_previous('p')
next_elem = soup.find_next('div')
response = requests.get('https://example.com')
soup = BeautifulSoup(response.text, 'html. parser')
noscript = soup.noscript.text
links = [a['href'] for a in soup.find_all('a', href=True)]# More powerful and concise search
items = soup.select('div.content > p.text')
first_item = soup.select_one('a.button')
🟢 Web scraping and data collection🟢 Processing HTML/XML reports🟢 Automating data extraction from websites🟢 Preparing data for analysis and machine learning
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