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#Deeplearning Reel by @virlyacitradewi - 10 Aplikasi ini bisa bapa ibu gunakan sebagai alternatif dalam menciptakan pembelajaran interaktif di kelas Kalo butuh tutor penggunaan, bisa komen "
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@virlyacitradewi
10 Aplikasi ini bisa bapa ibu gunakan sebagai alternatif dalam menciptakan pembelajaran interaktif di kelas Kalo butuh tutor penggunaan, bisa komen “tutor” akan saya buatkan video tutor selanjutnya ☺️ #gurukontenkreator #guruhebat #deeplearning #pembelajaraninteraktif #pembelajaranmendalam
#Deeplearning Reel by @code_helping - A neural network visualizer that shows how an MLP learns step by step. Runs in the browser, trained with PyTorch, and works best on desktop. . Source:
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@code_helping
A neural network visualizer that shows how an MLP learns step by step. Runs in the browser, trained with PyTorch, and works best on desktop. . Source: 🎥 DFinsterwalder (X) . . #coding #programming #softwaredevelopment #computerscience #cse #software #ai #ml #machinelearning #computer #neuralnetwork #mlp #ai #machinelearning #deeplearning #visualization #threejs #pytorch #webapp #tech
#Deeplearning 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
#Deeplearning Reel by @awomanindatascience - It's Day 14 of building a LLM from scratch ✨ Most people think LLMs are complex because of code. They're complex because of configuration and scale.
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@awomanindatascience
It’s Day 14 of building a LLM from scratch ✨ Most people think LLMs are complex because of code. They’re complex because of configuration and scale. Today I broke down the GPT-2 config that defines how the model thinks, remembers, and attends. GPT-2 is just a set of numbers that define scale: vocab size, context length, embedding dimension, layers, and attention heads. Breaking down the GPT-2 (124M) configuration: 50,257-token vocabulary, 1,024-token context, 768-dimensional embeddings, 12 transformer layers with 12 attention heads, dropout 0.1, and bias-free QKV projections. Understanding these parameters is key to scaling LLMs efficiently. #deeplearning #generativeai #womenwhocode #largelanguagemodels
#Deeplearning Reel by @deeprag.ai - Neural networks don't "understand" the world the way humans do... they transform it. What you're seeing here is how artificial neural networks actual
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@deeprag.ai
Neural networks don’t “understand” the world the way humans do... they transform it. What you’re seeing here is how artificial neural networks actually learn: raw inputs are passed through multiple layers, where each layer reshapes the data slightly, amplifying useful signals and suppressing noise. Over time, these layered transformations turn messy inputs into structured representations the model can act on. There’s no magic. No intuition. Just math, weights, activations, and gradients, stacked layer by layer. This is the foundation behind machine learning, deep learning, computer vision, LLMs, and everything from game AI to self-driving systems. Learning happens not in one leap, but through thousands of small adjustments that slowly bend data into meaning. Understanding this process is key to understanding how modern AI really works and why it’s so powerful. Follow @deeprag.ai for clear, visual explanations of AI, neural networks, and the ideas shaping the future of intelligence. . . . . . . . #artificialintelligence #neuralnetworks #deeplearning #machinelearning #aiexplained computerscience datascience aieducation llm futureofai aiarchitecture techlearning deepragai
#Deeplearning Reel by @datasciencebrain (verified account) - 🎓 FREE Stanford AI & ML Courses You Can't Miss! Stanford just dropped 11 game-changing courses that'll take you from ML basics to cutting-edge LLMs
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@datasciencebrain
🎓 FREE Stanford AI & ML Courses You Can't Miss! Stanford just dropped 11 game-changing courses that'll take you from ML basics to cutting-edge LLMs - and they're all FREE! 🚀 Whether you're starting your AI journey or leveling up your skills, this is your roadmap: ✅ Machine Learning fundamentals ✅ Deep Learning & Computer Vision ✅ Reinforcement Learning ✅ NLP & Transformers ✅ Generative AI & LLMs ✅ Building Language Models from scratch No fluff. No gatekeeping. Just world-class education from Stanford's top professors. The best part? You can learn at your own pace and build a portfolio that stands out. Which course are you starting with? Drop a number 1-11 in the comments! 👇 ⚠️ ALL COURSES ARE ON YOUTUBE. JUST SEARCH WITH THE NAMES. Save this post and share it with someone leveling up their AI career in 2025! 💡 📲 Follow @datasciencebrain #datasciencebrain for Daily Notes 📝, Tips ⚙️ and Interview QA🏆 . . . . . . [datascienceroadmap, airoles, mlengineerpath, datasciencejobs, analyticscareer, datatechskills, mlopsengineer, dataengineerskills, aiindustrytrends, techlearningguide] #datascience #machinelearning #python #ai #dataanalytics #artificialintelligence #deeplearning #bigdata #agenticai #aiagents #statistics #dataanalysis #datavisualization #analytics #datascientist #neuralnetworks #100daysofcode #genai #llms #datasciencebootcamp
#Deeplearning Reel by @sopi.iscoding (verified account) - Hi friends ! 😊 If you are new to deep learning and looking for beginner projects to start with. Here are some ideas 📚Digit recognizer with MNIST
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@sopi.iscoding
Hi friends ! 😊 If you are new to deep learning and looking for beginner projects to start with. Here are some ideas 📚Digit recognizer with MNIST The goal of this project is to identify digits from handwritten images. This dataset can be both introductory to deep learning and computer vision. I suggest this one because there are so many resources available.With this dataset, you can learn to build CNN from scratch. You can get the overall pipeline of the image classification task before moving on to projects with more challenging datasets. Dataset : https://www.kaggle.com/competitions/digit-recognizer/code 📚Object classification with CIFAR-10 Classify 10 classes of objects such as airplane, bird and dog. This dataset is good starting point. Dataset : https://www.cs.toronto.edu/~kriz/cifar.html I recommend starting with image classification in your first deep learning project. The problem is simple to understand. You can later extend to more challenging tasks such as detection and segmentation. 📚Sentimental Analysis This project is a good option to start if you are interested in NLP. You classify whether a tweet is a disaster or not. With this project, you learn about basic language text processing with nltk, and build a sequential model with LSTMs or RNNs. Dataset: https://www.kaggle.com/competitions/nlp-getting-started/overview 📚Face Mask detection In this project, you detect whether a person is wearing a mask, not wearing a mask or wearing one but incorrectly. You can learn to use many famous pretrained models for object detections (or you can build your own !) Dataset: https://www.kaggle.com/datasets/ashishjangra27/face-mask-12k-images-dataset I have done almost all of these projects! They are helpful based on my experience. I got my first job as a ML research assistant at uni with some of these projects 🙌 Feel free to save this reel for later ✨ Follow me @sopi.iscoding if you are interested in ML/AI learning journey, useful ML resources and sometimes humorous reels.😬
#Deeplearning Reel by @dailymathvisuals - The Kernel Trick explained in 75 seconds ✨ Ever wondered how machine learning separates data that seems impossible to separate? Here's the secret:
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@dailymathvisuals
The Kernel Trick explained in 75 seconds ✨ Ever wondered how machine learning separates data that seems impossible to separate? Here's the secret: → In 2D, no line can separate this data → But lift it into 3D... → A simple plane does the job perfectly This is why Support Vector Machines are so powerful 🧠 Save this for later 🔖 — Follow @dailymathvisuals for daily ML & math visualizations #machinelearning #artificialintelligence #datascience #python #coding #svm #kerneltrick #ai #tech #programming #learnwithreels #educationalreels #mathvisualization #deeplearning #engineering
#Deeplearning Reel by @etrainbrain - Convolutions are fundamental operations in deep learning, especially within Convolutional Neural Networks (CNNs). They work by applying a small matrix
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@etrainbrain
Convolutions are fundamental operations in deep learning, especially within Convolutional Neural Networks (CNNs). They work by applying a small matrix, known as a kernel (or filter), that slides across an input image to detect key features like edges, textures, or shapes. At each step, the kernel multiplies its values with the pixels underneath and sums them up, creating a feature map that emphasizes certain patterns. For instance, in the MNIST dataset, a 3×3 kernel can scan a 28×28 grayscale image of a digit (like “6”), capturing small regions and turning them into abstract representations. These representations help the network distinguish between similar digits (like “6” and “8”). Convolutions are often followed by pooling and deeper layers, allowing CNNs to build complex feature hierarchies that make them so effective for image recognition tasks. #deeplearning #machinelearning #computerscience #datascience #education #math #programming #coding #ai #cnn #etrainbrain #etrainbrainacademy #mathematics
#Deeplearning Reel by @aravind_ai_verse (verified account) - Basics of Deep Learning 😮‍💨 . . • Instagram Algorithm • How Instagram Works • Deep Learning Instagram • Deep Learning
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@aravind_ai_verse
Basics of Deep Learning 😮‍💨 . . • Instagram Algorithm • How Instagram Works • Deep Learning Instagram • Deep Learning
#Deeplearning Reel by @worldof.aix - ASML is a Netherlands-based technology company headquartered in Veldhoven, and despite staying largely out of the spotlight, it holds enormous influen
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@worldof.aix
ASML is a Netherlands-based technology company headquartered in Veldhoven, and despite staying largely out of the spotlight, it holds enormous influence over the future of computing. Established in 1984, ASML specializes in building the most advanced lithography machines in the world — systems that use highly precise light to etch microscopic circuits onto silicon wafers. These circuits form the core of every modern chip, powering everything from smartphones and laptops to AI data centers and supercomputers. What sets ASML apart is its monopoly on Extreme Ultraviolet (EUV) lithography. No other company on the planet can manufacture these machines, and without them, producing the most advanced semiconductor chips is impossible. As a result, giants like TSMC, Samsung, and Intel rely entirely on ASML to push chip technology forward. Strip ASML out of the equation, and progress in AI, high-performance computing, and next-generation devices would come to a halt. Credit: YouTube (ASML) For the latest updates on AI, future tech & groundbreaking innovations — follow @WorldOf.AIx 🤖🚀 #AI #ArtificialIntelligence #FutureTech #MachineLearning #DeepLearning #TechNews #Innovation #AIUpdates #TechReels #AIFuture #TechTrends #AITools #NeuralNetworks #AICommunity #asml
#Deeplearning Reel by @insightforge.ai - This is a live demonstration of a convolutional neural network (CNN) recognizing handwritten digits in real time. In the video, a person writes numbe
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@insightforge.ai
This is a live demonstration of a convolutional neural network (CNN) recognizing handwritten digits in real time. In the video, a person writes numbers on a touchscreen tablet while the connected system processes the image step by step. Viewers can watch the digit flow through different CNN layers, visualized as animated tensors, showing how features are extracted and transformed. By the end, the model correctly identifies the handwritten number, giving a clear, intuitive look at how CNNs perform classification behind the scenes. C: okdalto #cnn #machinelearning #deeplearning #computervision #datascience

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