#Deeple

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#Deeple Reel by @datasciencebrain (verified account) - Detaild 100 Days of AI Agents Rodamp 👇 🧠 Day 1-10: Understand AI Agents & LangChain Basics ♦️ What are AI agents, LLMs, and tool-use ♦️ Prompting
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@datasciencebrain
Detaild 100 Days of AI Agents Rodamp 👇 🧠 Day 1–10: Understand AI Agents & LangChain Basics ♦️ What are AI agents, LLMs, and tool-use ♦️ Prompting, context, memory, and output parsers ♦️ Introduction to LangChain and Gemini/GPT ♦️ Create your first basic agent with a custom tool 🛠️ Day 11–20: Build Single-Tool Agents ♦️ Wikipedia search agent ♦️ Web search + summarizer ♦️ File reader/writer tool ♦️ Email summarizer & translator ♦️ JSON, CSV analyzer ♦️ Notion/Google API tool wrappers 🤖 Day 21–35: Create Multi-Tool, Real-World Agents ♦️ Research assistant (Web + Wiki + Save) ♦️ Resume builder and reviewer ♦️ Calendar/email assistant ♦️ Blog writer with SEO tool ♦️ Fitness, finance, and coding helper agents ♦️ PDF/document chat agent 👥 Day 36–50: Multi-Agent Systems with CrewAI & Autogen ♦️ Understand multi-agent architecture ♦️ Build collaborative agents (e.g., content, code teams) ♦️ Use LangGraph for workflows ♦️ Agents with memory, planning, and delegation ♦️ Role-based communication and feedback loops 🚀 Day 51–70: Productionize Your Agents ♦️ Create REST API with FastAPI or LangServe ♦️ Add UI (Streamlit or React) ♦️ Use Pinecone/FAISS for RAG ♦️ Monitor with LangSmith or AgentOps ♦️ Deploy to Vercel, AWS Lambda 💼 Day 71–90: Build and Launch Real Products ♦️ Develop a full-stack AI SaaS app ♦️ Add Auth, analytics, and user data storage ♦️ Create Telegram/Slack bot version ♦️ Build Chrome extension or CLI agent ♦️ Run scheduled daily tasks 🏁 Day 91–100: Final Polish & Portfolio ♦️ Create docs, demo videos, and GitHub repo ♦️ Submit to HuggingFace or AgentHub ♦️ Build portfolio site ♦️ Share your work on LinkedIn/Product Hunt ♦️ Reflect and plan next AI agent projects Special Benefits for Our Instagram Subscribers 🔻 ➡️ Free Resume Reviews & ATS-Compatible Resume Template ➡️ Quick Responses and Support ➡️ Exclusive Q&A Sessions ➡️ Data Science Job Postings ➡️ Access to MIT + Stanford Notes ➡️ Full Data Science Masterclass PDFs ⭐️ All this for just Rs.45/month! . . . . . . . #datascience #machinelearning #python #ai #dataanalytics #artificialintelligence #deeple
#Deeple Reel by @studywithaffu (verified account) - 🌅 BEST TIME TO STUDY (Scientifically Proven!) 🕔 5AM - 8AM → PEAK FOCUS MODE ✨ Fresh mind 🔕 Zero distractions 🧠 Memory power at MAX 🕙 10AM - 2PM
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@studywithaffu
🌅 BEST TIME TO STUDY (Scientifically Proven!) 🕔 5AM – 8AM → PEAK FOCUS MODE ✨ Fresh mind 🔕 Zero distractions 🧠 Memory power at MAX 🕙 10AM – 2PM → DEEP LEARNING HOURS ⚡ Brain at highest alertness 📚 Best for tough subjects 💡 Perfect for new concepts 🌆 6PM – 9PM → REVISION & RECALL 🔁 Revise & reinforce 📝 Memory sticks better 🎯 Best for summaries & practice ⚡ Stop studying randomly. Start studying scientifically. 👉 Follow @studywithaffu for daily brain-boosting study hacks✨💫 . . . . #studygram #medicalstudent #motivationalreels #studytips #studytricks
#Deeple Reel by @woman.engineer (verified account) - 🚀 How to Become an AI Engineer in 2026 👩🏻‍💻Save for later Step-by-step Roadmap 🔹 PHASE 1: Foundations (0-3 Months) Don't skip this. Weak foundat
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@woman.engineer
🚀 How to Become an AI Engineer in 2026 👩🏻‍💻Save for later Step-by-step Roadmap 🔹 PHASE 1: Foundations (0–3 Months) Don’t skip this. Weak foundations = stuck later. 1️⃣ Programming (Must-Have) Python: loops, functions, OOP Libraries: NumPy, Pandas, Matplotlib / Seaborn 📌 Practice daily: LeetCode (easy) HackerRank (Python) 2️⃣ Math for AI (Enough, not PhD level) Focus only on: Linear Algebra (vectors, matrices) Probability & Statistics Basic Calculus (idea of gradients) 📌 Conceptual understanding is enough — no heavy theory. 🔹 PHASE 2: Machine Learning (3–6 Months) Learn: Supervised & Unsupervised Learning Feature Engineering Model Evaluation Algorithms: Linear & Logistic Regression KNN Decision Trees Random Forest SVM K-Means Tools: Scikit-learn 📌 Project Ideas: House price prediction Student performance prediction Credit risk model 🔹 PHASE 3: Deep Learning & AI (6–10 Months) Learn: Neural Networks & Backpropagation CNN (Images) RNN / LSTM (Text) Transformers (Basics) Frameworks: TensorFlow or PyTorch (choose ONE) 📌 Project Ideas: Face mask detection Image classifier Spam email detector Basic chatbot 🔹 PHASE 4: Modern AI (2025–2026) 🔥 This is where the JOBS are coming from. Learn: Generative AI Large Language Models (LLMs) Prompt Engineering RAG (Retrieval-Augmented Generation) Fine-tuning models Tools: OpenAI API Hugging Face LangChain Vector Databases (FAISS / Pinecone) 📌 Project Ideas: AI PDF Chat App Resume Analyzer AI Study Assistant AI Customer Support Bot 🔹 PHASE 5: MLOps & Deployment (CRITICAL) Learn: Git & GitHub Docker (basics) FastAPI / Flask Cloud basics (AWS or GCP) Deploy: ML models as APIs AI apps on the cloud 📌 Recruiters LOVE deployed projects. . . . #datascientist #aiengineer #codinglife #softwaredeveloper #programming
#Deeple Reel by @vision_nests - the work of Japanese visual artist Kensuke Koike, who uses a "no more, no less" philosophy to deconstruct vintage photographs into new, often surreal
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@vision_nests
the work of Japanese visual artist Kensuke Koike, who uses a "no more, no less" philosophy to deconstruct vintage photographs into new, often surreal forms. By passing a photograph of a dog through a pasta machine, Koike creates a physical representation of how a Convolutional Neural Network (CNN) processes visual data. In computer science, this serves as a metaphor for the Convolutional Layer, where "filters" scan an image to break it down into smaller, manageable pieces of data. Just as the pasta machine slices the image into uniform strips, a CNN extracts "features"—like edges, curves, and textures—rather than trying to understand the entire complex image all at once. ​Once the image is shredded, the artist rearranges the strips into a grid, which mirrors the Pooling or Downsampling stage of a neural network. This process reduces the spatial size of the data to decrease the computational power required while preserving the most critical information. The resulting "pixelated" and repetitive dogs seen at the end of the clip represent the Feature Maps that deep learning models use to identify patterns. By the final frame, the network (or the viewer) can recognize the "dogness" of the image through these simplified, reconstructed blocks, perfectly illustrating the journey from raw pixels to high-level object recognition. Interested in? Follow:-@vision_nests #science #pixilated @vision_nests
#Deeple Reel by @aidisruptor - This AI-powered tablet and capsule counting machine is redefining pharmaceutical manufacturing through flawless accuracy and zero tolerance for human
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@aidisruptor
This AI-powered tablet and capsule counting machine is redefining pharmaceutical manufacturing through flawless accuracy and zero tolerance for human error. Combining machine vision and deep learning, the system scans, counts, and verifies every unit in real time. It instantly flags broken, missing, or misaligned tablets, enforcing rigorous quality control and full compliance with pharmaceutical standards. These machines are a staple in modern medicine production lines, boosting efficiency, cutting waste, and safeguarding patient safety. This is pharmaceutical automation at its finest — where AI delivers precision, speed, and reliability in every single dose. 🚨 Follow @aidisruptor for the latest in AI and tech innovation. Credit: PharmacistRon (TT) DM for credit/removal (no copyright intended) #Artificialintelligence #technology #Automation #Robotics #MedTech
#Deeple Reel by @olaconleyy - Google just broke AI forever Google dropped Gemma 4 and this changes everything. A fully open model that runs offline, on your phone, your laptop, ev
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@olaconleyy
Google just broke AI forever Google dropped Gemma 4 and this changes everything. A fully open model that runs offline, on your phone, your laptop, even a Raspberry Pi. No API costs. No internet required. No limits. This is not just another release. This is Google making AI accessible to everyone. If you create, build, or sell using AI… this matters. Follow for daily AI tools that actually give you an edge. . . . [ai, artificial intelligence, google ai, gemma 4, open source ai, offline ai, local ai, ai tools, machine learning, generative ai, llm, large language model, ai for business, ai trends, future of ai, ai development, android ai, edge ai, ai innovation, ai creators, ai productivity, ai startups, tech news, deep learning, ai software]
#Deeple Reel by @aescripts (verified account) - Comment 'Deekay' for link! Made with Deekay Tool by @deekaymotion https://aescripts.com/deekay-tool (link in bio) #aescripts #aftereffects #deekaytoo
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@aescripts
Comment 'Deekay' for link! Made with Deekay Tool by @deekaymotion https://aescripts.com/deekay-tool (link in bio) #aescripts #aftereffects #deekaytool Customizable avatar? 👀 coming soon! Introducing “DeePle” (DeeKay + People) . . . #deeple #deekay
#Deeple Reel by @workiniterations - 4 ML papers to read this weekend instead of touching grass. Attention Is All You Need Yes, everyone talks about this one, but that's because it compl
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@workiniterations
4 ML papers to read this weekend instead of touching grass. Attention Is All You Need Yes, everyone talks about this one, but that’s because it completely changed the direction of AI. This paper introduced the Transformer architecture, which is the foundation behind most modern language models. Deep Residual Learning for Image Recognition A deceptively simple idea: skip connections. This paper made it possible to train very deep neural networks and ended up shaping how many modern architectures are built. Understanding Deep Learning Requires Rethinking Generalization This one is fascinating because it challenges a core assumption in machine learning. Neural networks can memorize random data perfectly, which raises big questions about why deep learning actually works. The Lottery Ticket Hypothesis A really intriguing idea: large neural networks might contain much smaller winning subnetworks that can train just as well as the full model. It changes how we think about efficiency and model size. Together these give a nice balance of architecture, theory, and insight into how deep learning actually works. Save this if you’re learning ML.
#Deeple Reel by @shivamchhuneja (verified account) - NVIDIA has released free online courses and workshops covering a wide range of AI and deep learning topics, from accelerated computing to generative A
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@shivamchhuneja
NVIDIA has released free online courses and workshops covering a wide range of AI and deep learning topics, from accelerated computing to generative AI. These courses are available through the NVIDIA Deep Learning Institute and cover beginner to intermediate level content, making it easy for anyone to learn about the latest advancements in AI technology. . . #ai #onlinecourse #course #nvidia #deeplearning #careeradvice #careertips #careergrowth #careerdevelopment
#Deeple Reel by @codingmermaid.ai (verified account) - You're one decision away from success no one in your circle has seen. Millions want to learn AI. Almost none do it right. Some think generating sl
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@codingmermaid.ai
You're one decision away from success no one in your circle has seen. Millions want to learn AI. Almost none do it right. Some think generating slop = AI expertise. Others build "second brains" that don't actually work. Both groups have one thing in common: they're losing money. The real investment isn't ChatGPT prompts. It's knowing how to build models that make decisions at scale. Here's the plan that got thousands of my students hired: Month 1–3 · Foundation Python, data preprocessing, and software engineering fundamentals — the base everything else is built on. Month 4–6 · Core Machine Learning Tree models, ensemble learning, deep learning, and experiment tracking with real projects. Month 7–9 · Transformers & LLMs Transformer architecture, OpenAI + Anthropic APIs, RAG pipelines, and vector databases. Month 10–12 · Production AI Fine-tuning, AI agents, multi-agent systems, MLOps and LLMOps — shipping real systems. I'm teaching you how to build impactful and value-driven tools using classic machine learning and modern AI Comment "plan" for an interactive learning guide that gives you traction and momentum and helps you learn the right tools! Follow @codingmermaid.ai
#Deeple Reel by @amanrahangdale_2108 (verified account) - Read Here ⬇️ 1️⃣ AI & Machine Learning Why: AI is driving automation and smart decision-making Learn: Python, ML, Deep Learning, GenAI Roles: AI Eng
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@amanrahangdale_2108
Read Here ⬇️ 1️⃣ AI & Machine Learning Why: AI is driving automation and smart decision-making Learn: Python, ML, Deep Learning, GenAI Roles: AI Engineer, ML Engineer, Data Scientist 2️⃣ Data Science & Data Analytics Why: Businesses rely on data-driven decisions Learn: Python, SQL, Statistics, Visualization Roles: Data Analyst, Data Scientist 3️⃣ Cloud Computing & DevOps Why: Modern apps need scalability and reliability Learn: AWS/GCP, Docker, Kubernetes, CI/CD Roles: Cloud Engineer, DevOps Engineer 4️⃣ Cybersecurity Why: Cyber threats are increasing rapidly Learn: Network Security, Ethical Hacking, OWASP Roles: Security Analyst, Cybersecurity Engineer 5️⃣ Agentic AI Why: Next-gen AI that can plan and act autonomously Learn: LLMs, AI Agents, LangChain, Automation Roles: AI Engineer, Automation Engineer 6️⃣ Full-Stack Web Development + AI Integration Why: Companies need complete AI-powered products Learn: React, Node.js, Databases, AI APIs Roles: Full-Stack Developer, Product Engineer 💬 Comment the skill name for detailed learning roadmap & best resources 🚀 📱 Follow @amanrahangdale_2108 for more Free Courses, Tech Updates, and Career Tips every week 💡
#Deeple Reel by @ssktechy - A GitHub repository just shared free university lectures and courses in Artificial Intelligence, Machine Learning, Deep Learning, and core Computer Sc
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@ssktechy
A GitHub repository just shared free university lectures and courses in Artificial Intelligence, Machine Learning, Deep Learning, and core Computer Science. Top university AI courses, ML lectures, deep learning resources, and CS fundamentals. All in one open GitHub repo. Link in bio or comment git and I’ll send it. #AICourses #MachineLearning #DeepLearning #ComputerScience #ssktechy

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