@cola.knit: Мои мастер-классы на сайте colaknit.ru💗 #творчество #вязание #вязаниекрючком #ручнаяработа #вяжу

cola.knit

Open In TikTok:

Region: LT

Saturday 03 October 2026 06:17:51 GMT

95487

5916

80

283

Music

Download

No Watermark .mp4 (4.4MB) No Watermark(HD) .mp4 (3.89MB) Watermark .mp4 (0MB) Music .mp3

Comments

svetlana4nia :

Классно 👍 ☺️💯✅

2026-10-03 14:38:32

2

Полина Долевая :

подскажите пожалуйста какую пряжу используете?

2026-10-03 09:45:48

21

Валерия. :

1600 р мк

2026-10-03 17:18:45

1

mariajger89 :

красота

2026-10-04 11:07:30

1

♥️Tata_148♥️ :

Здравствуй, можно МК?

2026-10-03 16:06:47

2

InKaGnittO 🙃 :

это очень красиво 🥰

2026-10-04 04:40:00

1

Oxy :

Это ведь просто соединительные с накидом в поперечной технике. Но вяжите вы невероятно, не всегда из мериноса получается так аккуратно

2026-10-03 18:51:05

21

Алена 🦂🫦 :

очень✨ мило икрасиво🙌🍀🍀🍀

2026-10-04 04:48:15

2

U. Y. :

Очень красиво👍🥰

2026-10-04 06:06:13

1

Галина :

Только из мериноса получится такая ровная шапка?

2026-10-03 07:44:43

1

⚘️Kamelia :

красота какая🥰

2026-10-03 09:43:53

2

Galiya :

а где можно посмотреть видео

2026-10-04 06:06:11

1

Olga Le :

Скажи пожалуйста а какие есть способы оплаты МК?

2026-10-03 19:55:26

2

💔Морально больно💔? :

Как получит такой ровный край😳

2026-10-04 18:12:19

3

Инна :

Красиво,но так долго вязать,ещё и если меринос слабой скрутки,то это вообще ужас, пряжи уходит очень много.

2026-10-03 13:40:18

5

Елена Леонович :

а где посмотреть мастер класс

2026-10-04 08:47:05

1

Наталья Наталья :

поверю 🤗🤗🤗 я вязала манжеты для куртки, тоже крючком

2026-10-03 15:59:33

1

Мария :

Очень красиво

2026-10-03 12:06:17

1

Galini Pelekanaki :

Как👍 аккуратно🥰

2026-10-03 07:10:41

1

тгк КВОБИТ :

подскажите состав пряжи, пожалуйста🥰

2026-10-03 07:39:00

1

DICE533 :

[Red heart]

2026-10-03 07:03:15

1

Olesya handmade inspiration :

🥰🥰🥰 Очень красиво и аккуратно!!!

2026-10-03 06:27:45

2

. :

супер,подписалась,где посмотреть мастер- класс?

2026-10-03 11:54:20

1

Ⓣⓤⓢⓙⓐ ⑧⑧⑧ⓣⓘⓚⓣⓞⓚ :

А у вас есть схема перчаток?

2026-10-03 07:24:02

1

Лиззка✌🏻 :

увидела и сразу же зашла на сайт и купила несколько мк, но буду ли я вязать?🤣 купила уже несколько разных мк, но, оказывается, очень не люблю образцы и расчёты

2026-10-06 03:48:14

1

To see more videos from user @cola.knit, please go to the Tikwm homepage.

Other Videos

Em muon hon o day 😝 #viral #xuhuong #foryou #xuhuongtiktok #fyp

Em muon hon o day 😝 #viral #xuhuong #foryou #xuhuongtiktok #fyp

Jalan-jalan ke Manyar melihat bunga, Tabebuya mekar indah berseri. Mari bersama menjaga lingkungan kita, Agar Gresik hijau lestari. Tabebuya bermekaran, jalanan pun semakin cantik. Namun, keindahan ini akan terus terjaga jika kita bersama-sama merawat pohon, menjaga kebersihan, dan tidak merusak lingkungan. 📍 Jalan Raya Manyar, Gresik #DLHGresik #GresikLestari #GresikHijau #TabebuyaGresik

Jalan-jalan ke Manyar melihat bunga, Tabebuya mekar indah berseri. Mari bersama menjaga lingkungan kita, Agar Gresik hijau lestari. Tabebuya bermekaran, jalanan pun semakin cantik. Namun, keindahan ini akan terus terjaga jika kita bersama-sama merawat pohon, menjaga kebersihan, dan tidak merusak lingkungan. 📍 Jalan Raya Manyar, Gresik #DLHGresik #GresikLestari #GresikHijau #TabebuyaGresik

If you’re serious about learning AI, Machine Learning, Deep Learning, and LLMs, don’t subscribe to 50 channels and watch random videos. Pick a few that match your learning stage. 👇 🧠 1. ANDREJ KARPATHY Best for: Deep Learning + LLMs + AI from first principles Learn: • Neural Networks • Backpropagation • Transformers • LLMs • GPT-style models • AI engineering His “Neural Networks: Zero to Hero” material is especially useful for learning by building. 📊 2. STATQUEST WITH JOSH STARMER Best for: Understanding ML & statistics Learn: • Statistics • Probability • Regression • Decision Trees • Random Forests • PCA • Neural networks 🎯 Why watch: Complex concepts are broken down into remarkably understandable explanations. 📐 3. 3BLUE1BROWN Best for: Mathematical intuition Learn: • Linear Algebra • Calculus • Neural Networks • Gradient Descent • Probability If ML equations look like alien hieroglyphics, this is where you start translating. 🧮 🤖 4. DEEplearning.AI Best for: Modern AI & practical learning Learn: • Machine Learning • Deep Learning • Generative AI • LLMs • RAG • AI Agents • AI Engineering 🎯 Great for staying connected to modern AI workflows. 💻 5. FREECODECAMP Best for: Long-form hands-on tutorials Find courses covering: 🐍 Python 🤖 Machine Learning 🧠 Deep Learning 📊 Data Science 🗄️ SQL ☁️ Cloud 🌐 Software Development 🎯 Great when you want a complete tutorial instead of a 10-minute overview. 🔥 6. SENTDEX Best for: Python + practical ML Learn through coding-focused tutorials covering: 🐍 Python 📊 Data Analysis 🤖 Machine Learning 🧠 Neural Networks 👁️ Computer Vision 🧠 7. GOOGLE DEEPMIND Best for: Research & cutting-edge AI Explore: 🔬 AI research 🧠 Deep Learning 🎮 Reinforcement Learning 👁️ Computer Vision 🧬 AI for science Great once you’ve built enough fundamentals to appreciate research-level ideas. 🗺️ HOW TO USE THESE CHANNELS Beginner: Python → Statistics → Linear Algebra ⬇️ ML: StatQuest → ML algorithms → Projects ⬇️ Deep Learning: 3Blue1Brown → Karpathy → PyTorch ⬇️ GenAI: Transformers → LLMs → RAG → Agents ⬇️ Advanced: Research papers → Reproduce papers → Build your own systems ⚠️ DON’T MAKE THIS MISTAKE ❌ Watch 500 hours of tutorials ❌ Take 20 courses ❌ Collect playlists ❌ Never build anything Instead: Watch → Code → Build → Break → Debug → Repeat. 🔄 The goal isn’t to become a professional YouTube watcher. The goal is to become someone who can actually build AI systems. 🚀 🔖 Save this list for your AI/ML learning journey. #ArtificialIntelligence #AI #MachineLearning #DeepLearning #creatorsearchinsights

If you’re serious about learning AI, Machine Learning, Deep Learning, and LLMs, don’t subscribe to 50 channels and watch random videos. Pick a few that match your learning stage. 👇 🧠 1. ANDREJ KARPATHY Best for: Deep Learning + LLMs + AI from first principles Learn: • Neural Networks • Backpropagation • Transformers • LLMs • GPT-style models • AI engineering His “Neural Networks: Zero to Hero” material is especially useful for learning by building. 📊 2. STATQUEST WITH JOSH STARMER Best for: Understanding ML & statistics Learn: • Statistics • Probability • Regression • Decision Trees • Random Forests • PCA • Neural networks 🎯 Why watch: Complex concepts are broken down into remarkably understandable explanations. 📐 3. 3BLUE1BROWN Best for: Mathematical intuition Learn: • Linear Algebra • Calculus • Neural Networks • Gradient Descent • Probability If ML equations look like alien hieroglyphics, this is where you start translating. 🧮 🤖 4. DEEplearning.AI Best for: Modern AI & practical learning Learn: • Machine Learning • Deep Learning • Generative AI • LLMs • RAG • AI Agents • AI Engineering 🎯 Great for staying connected to modern AI workflows. 💻 5. FREECODECAMP Best for: Long-form hands-on tutorials Find courses covering: 🐍 Python 🤖 Machine Learning 🧠 Deep Learning 📊 Data Science 🗄️ SQL ☁️ Cloud 🌐 Software Development 🎯 Great when you want a complete tutorial instead of a 10-minute overview. 🔥 6. SENTDEX Best for: Python + practical ML Learn through coding-focused tutorials covering: 🐍 Python 📊 Data Analysis 🤖 Machine Learning 🧠 Neural Networks 👁️ Computer Vision 🧠 7. GOOGLE DEEPMIND Best for: Research & cutting-edge AI Explore: 🔬 AI research 🧠 Deep Learning 🎮 Reinforcement Learning 👁️ Computer Vision 🧬 AI for science Great once you’ve built enough fundamentals to appreciate research-level ideas. 🗺️ HOW TO USE THESE CHANNELS Beginner: Python → Statistics → Linear Algebra ⬇️ ML: StatQuest → ML algorithms → Projects ⬇️ Deep Learning: 3Blue1Brown → Karpathy → PyTorch ⬇️ GenAI: Transformers → LLMs → RAG → Agents ⬇️ Advanced: Research papers → Reproduce papers → Build your own systems ⚠️ DON’T MAKE THIS MISTAKE ❌ Watch 500 hours of tutorials ❌ Take 20 courses ❌ Collect playlists ❌ Never build anything Instead: Watch → Code → Build → Break → Debug → Repeat. 🔄 The goal isn’t to become a professional YouTube watcher. The goal is to become someone who can actually build AI systems. 🚀 🔖 Save this list for your AI/ML learning journey. #ArtificialIntelligence #AI #MachineLearning #DeepLearning #creatorsearchinsights

🤖 3 RAG PROJECTS YOU CAN BUILD Want to learn Retrieval-Augmented Generation (RAG) by actually building something? Don’t start with another basic “chat with PDF” demo. Build these 3 projects 👇 1️⃣ PERSONAL KNOWLEDGE ASSISTANT 📚 Difficulty: Beginner → Intermediate Build an AI assistant that can answer questions from your own: 📄 PDFs 📝 Notes 📚 Books 📊 Documents 💻 Markdown files Architecture: Documents ↓ Chunking ↓ Embeddings ↓ Vector Database ↓ Retriever ↓ LLM ↓ Answer + Sources 🛠️ Stack: Python • LangChain/LlamaIndex • Qdrant/Chroma • Sentence Transformers • LLM API • Streamlit 🔥 Add: ✅ Source citations ✅ Conversation memory ✅ Metadata filtering ✅ Document upload ✅ Reranking 2️⃣ RESEARCH PAPER RAG ASSISTANT 🔬 Difficulty: Intermediate Build an AI research assistant that can search and reason over academic papers. The system should: 📄 Ingest research papers 🔎 Retrieve relevant sections 🧠 Compare papers 📌 Extract findings 🔗 Provide citations 📊 Identify research gaps Architecture: Research Papers ↓ PDF Parsing ↓ Semantic Chunking ↓ Embeddings ↓ Vector + Keyword Search ↓ Reranking ↓ LLM ↓ Cited Answer 🔥 Advanced features: • Hybrid search • Cross-encoder reranking • Query rewriting • Paper metadata • Citation tracking • Multi-document comparison This starts looking like a real research tool, not just a chatbot. 3️⃣ AGENTIC RAG RESEARCH ASSISTANT 🤖 Difficulty: Advanced Now make your RAG system agentic. Instead of: Question → Retrieve → Answer Build: Question ↓ 🤖 Agent ↓ 🧠 Plan ↓ 🔎 Retrieve ↓ 📊 Evaluate Context ↓ 🔄 Search Again if Needed ↓ 🛠️ Use Tools ↓ 📝 Generate ↓ ✅ Verify ↓ 💬 Answer + Sources Give the agent access to: 🔎 Web search 📚 Vector database 🗄️ SQL database 🐍 Python tools 📊 Data analysis tools 📄 Document collections 🛠️ POSSIBLE STACK Frontend: React / Streamlit Backend: FastAPI Agent orchestration: LangGraph Vector DB: Qdrant Embeddings: Sentence Transformers Reranker: BGE / Cross-Encoder LLM: GPT / Claude / Gemini / open-source model Database: PostgreSQL Deployment: Docker + Cloud 🚀 BUILD THEM IN THIS ORDER Project 1: Learn RAG fundamentals ⬇️ Project 2: Learn advanced retrieval ⬇️ Project 3: Learn Agentic RAG + tool use By the end, you should understand: Chunking → Embeddings → Retrieval → Reranking → Generation → Evaluation → Agents → Deployment 💡 Don’t just build a RAG chatbot. Build a system that can retrieve, reason, use tools, verify information, and show its sources. That’s where RAG becomes much more interesting. 🔥 🔖 Save this if you’re learning RAG and AI Engineering. #RAG #AgenticRAG #GenerativeAI #creatorsearchinsights #datascience

🤖 3 RAG PROJECTS YOU CAN BUILD Want to learn Retrieval-Augmented Generation (RAG) by actually building something? Don’t start with another basic “chat with PDF” demo. Build these 3 projects 👇 1️⃣ PERSONAL KNOWLEDGE ASSISTANT 📚 Difficulty: Beginner → Intermediate Build an AI assistant that can answer questions from your own: 📄 PDFs 📝 Notes 📚 Books 📊 Documents 💻 Markdown files Architecture: Documents ↓ Chunking ↓ Embeddings ↓ Vector Database ↓ Retriever ↓ LLM ↓ Answer + Sources 🛠️ Stack: Python • LangChain/LlamaIndex • Qdrant/Chroma • Sentence Transformers • LLM API • Streamlit 🔥 Add: ✅ Source citations ✅ Conversation memory ✅ Metadata filtering ✅ Document upload ✅ Reranking 2️⃣ RESEARCH PAPER RAG ASSISTANT 🔬 Difficulty: Intermediate Build an AI research assistant that can search and reason over academic papers. The system should: 📄 Ingest research papers 🔎 Retrieve relevant sections 🧠 Compare papers 📌 Extract findings 🔗 Provide citations 📊 Identify research gaps Architecture: Research Papers ↓ PDF Parsing ↓ Semantic Chunking ↓ Embeddings ↓ Vector + Keyword Search ↓ Reranking ↓ LLM ↓ Cited Answer 🔥 Advanced features: • Hybrid search • Cross-encoder reranking • Query rewriting • Paper metadata • Citation tracking • Multi-document comparison This starts looking like a real research tool, not just a chatbot. 3️⃣ AGENTIC RAG RESEARCH ASSISTANT 🤖 Difficulty: Advanced Now make your RAG system agentic. Instead of: Question → Retrieve → Answer Build: Question ↓ 🤖 Agent ↓ 🧠 Plan ↓ 🔎 Retrieve ↓ 📊 Evaluate Context ↓ 🔄 Search Again if Needed ↓ 🛠️ Use Tools ↓ 📝 Generate ↓ ✅ Verify ↓ 💬 Answer + Sources Give the agent access to: 🔎 Web search 📚 Vector database 🗄️ SQL database 🐍 Python tools 📊 Data analysis tools 📄 Document collections 🛠️ POSSIBLE STACK Frontend: React / Streamlit Backend: FastAPI Agent orchestration: LangGraph Vector DB: Qdrant Embeddings: Sentence Transformers Reranker: BGE / Cross-Encoder LLM: GPT / Claude / Gemini / open-source model Database: PostgreSQL Deployment: Docker + Cloud 🚀 BUILD THEM IN THIS ORDER Project 1: Learn RAG fundamentals ⬇️ Project 2: Learn advanced retrieval ⬇️ Project 3: Learn Agentic RAG + tool use By the end, you should understand: Chunking → Embeddings → Retrieval → Reranking → Generation → Evaluation → Agents → Deployment 💡 Don’t just build a RAG chatbot. Build a system that can retrieve, reason, use tools, verify information, and show its sources. That’s where RAG becomes much more interesting. 🔥 🔖 Save this if you’re learning RAG and AI Engineering. #RAG #AgenticRAG #GenerativeAI #creatorsearchinsights #datascience

💻 CODING Coding isn’t just typing code. It’s: 🧠 Thinking 🔍 Problem-solving 🐛 Debugging 📚 Learning 🛠️ Building 💡 Creating 🔄 Failing and trying again Sometimes you spend 5 minutes writing code and 3 hours figuring out why it doesn’t work. 😭 Then suddenly… It works. 🔥 That feeling is why we keep coding. Learn → Build → Break → Debug → Improve → Repeat. One line at a time. 💻🚀 #Coding #Programming #DeveloperLife #creatorsearchinsights #machinelearningengineer

💻 CODING Coding isn’t just typing code. It’s: 🧠 Thinking 🔍 Problem-solving 🐛 Debugging 📚 Learning 🛠️ Building 💡 Creating 🔄 Failing and trying again Sometimes you spend 5 minutes writing code and 3 hours figuring out why it doesn’t work. 😭 Then suddenly… It works. 🔥 That feeling is why we keep coding. Learn → Build → Break → Debug → Improve → Repeat. One line at a time. 💻🚀 #Coding #Programming #DeveloperLife #creatorsearchinsights #machinelearningengineer

#dremanali #zeeshan_rokhari #Chakwal #chakwalpunjab #سنگت_ویڈیو_وائرل_کریسو_آپ_ہیں🥀🌼🙈✨🎇 @Dr Eman ali @Dr Eman ali 2

#dremanali #zeeshan_rokhari #Chakwal #chakwalpunjab #سنگت_ویڈیو_وائرل_کریسو_آپ_ہیں🥀🌼🙈✨🎇 @Dr Eman ali @Dr Eman ali 2

About

Robot
API

Legal

Privacy Policy