Machine Learning with Python

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Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho

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شبکه:Machine Learning with Python الهند4 731 آموزش2 365...

📈 تحلیل کانال تلگرام Machine Learning with Python

کانال Machine Learning with Python (@codeprogrammer) در بخش زبانی انگلیسی بازیگری فعال است. در حال حاضر جامعه شامل 68 138 مشترک است و جایگاه 2 365 را در دسته آموزش و رتبه 4 731 را در منطقه الهند دارد.

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از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 68 138 مشترک جذب کرده است.

بر اساس آخرین داده‌ها در تاریخ 31 اوت, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر 80 و در ۲۴ ساعت گذشته برابر 1 بوده و همچنان دسترسی گسترده‌ای حفظ شده است.

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“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho”

به لطف به‌روزرسانی‌های پرتکرار (آخرین داده در تاریخ 01 سپتامبر, 2026)، کانال همواره به‌روز و دارای دسترسی بالاست. تحلیل‌ها نشان می‌دهد مخاطبان به‌طور فعال با محتوا تعامل دارند و آن را به نقطه اثرگذاری مهم در دسته آموزش تبدیل کرده‌اند.

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68 138

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🔍 Understanding Recurrent Neural Networks (RNNs) Cheat Sheet! Recurrent Neural Networks are a powerful type of neural network designed to handle sequential data. They are widely used in applications like natural language processing, speech recognition, and time-series prediction. Here's a quick cheat sheet to get you started: 📘 Key Concepts: Sequential Data: RNNs are designed to process sequences of data, making them ideal for tasks where order matters. Hidden State: Maintains information from previous inputs, enabling memory across time steps. Backpropagation Through Time (BPTT): The method used to train RNNs by unrolling the network through time. 🔧 Common Variants: Long Short-Term Memory (LSTM): Addresses vanishing gradient problems with gates to manage information flow. Gated Recurrent Unit (GRU): Similar to LSTMs but with a simpler architecture. 🚀 Applications: Language Modeling: Predicting the next word in a sentence. Sentiment Analysis: Understanding sentiments in text. Time-Series Forecasting: Predicting future data points in a series. 🔗 Resources: Dive deeper with tutorials on platforms like Coursera, edX, or YouTube. Explore open-source libraries like TensorFlow or PyTorch for implementation. Let's harness the power of RNNs to innovate and solve complex problems! 💡

#RNN #RecurrentNeuralNetworks #DeepLearning #NLP #LSTM #GRU #TimeSeriesForecasting #MachineLearning #NeuralNetworks #AIApplications #SequenceModeling #MLCheatSheet #PyTorch #TensorFlow #DataScience

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1. Master the fundamentals of Statistics Understand probability, distributions, and hypothesis testing Differentiate between descriptive vs inferential statistics Learn various sampling techniques 2. Get hands-on with Python & SQL Work with data structures, pandas, numpy, and matplotlib Practice writing optimized SQL queries Master joins, filters, groupings, and window functions 3. Build real-world projects Construct end-to-end data pipelines Develop predictive models with machine learning Create business-focused dashboards 4. Practice case study interviews Learn to break down ambiguous business problems Ask clarifying questions to gather requirements Think aloud and structure your answers logically 5. Mock interviews with feedback Use platforms like Pramp or connect with peers Record and review your answers for improvement Gather feedback on your explanation and presence 6. Revise machine learning concepts Understand supervised vs unsupervised learning Grasp overfitting, underfitting, and bias-variance tradeoff Know how to evaluate models (precision, recall, F1-score, AUC, etc.) 7. Brush up on system design (if applicable) Learn how to design scalable data pipelines Compare real-time vs batch processing Familiarize with tools: Apache Spark, Kafka, Airflow 8. Strengthen storytelling with data Apply the STAR method in behavioral questions Simplify complex technical topics Emphasize business impact and insight-driven decisions 9. Customize your resume and portfolio Tailor your resume for each job role Include links to projects or GitHub profiles Match your skills to job descriptions 10. Stay consistent and track progress Set clear weekly goals Monitor covered topics and completed tasks Reflect regularly and adapt your plan as needed

#DataScience #InterviewPrep #MLInterviews #DataEngineering #SQL #Python #Statistics #MachineLearning #DataStorytelling #SystemDesign #CareerGrowth #DataScienceRoadmap #PortfolioBuilding #MockInterviews #JobHuntingTips

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Step-by-Step Guide to Deploying Machine Learning Models with FastAPI and Docker https://machinelearningmastery.com/step-by-step-guide-to-deploying-machine-learning-models-with-fastapi-and-docker/

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Get important resources from books and courses The number is very limited https://t.me/+r_Tcx2c-oVU1OWNi

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🔹 Title: IllumiCraft: Unified Geometry and Illumination Diffusion for Controllable Video Generation 🔹 Publication Date: Published on Jun 3 🔹 Abstract: IllumiCraft integrates geometric cues in a diffusion framework to generate high-fidelity, temporally coherent videos from textual or image inputs. AI-generated summary Although diffusion-based models can generate high-quality and high-resolution video sequences from textual or image inputs, they lack explicit integration of geometric cues when controlling scene lighting and visual appearance across frames. To address this limitation, we propose IllumiCraft, an end-to-end diffusion framework accepting three complementary inputs: (1) high-dynamic-range (HDR) video maps for detailed lighting control; (2) synthetically relit frames with randomized illumination changes (optionally paired with a static background reference image) to provide appearance cues; and (3) 3D point tracks that capture precise 3D geometry information. By integrating the lighting, appearance, and geometry cues within a unified diffusion architecture, IllumiCraft generates temporally coherent videos aligned with user-defined prompts. It supports background-conditioned and text-conditioned video relighting and provides better fidelity than existing controllable video generation methods. Project Page: https://yuanze-lin.me/IllumiCraft_page 🔹 Links: - arXiv Page: https://arxiv.org/abs/2506.03150 - PDF: https://arxiv.org/pdf/2506.03150 - Project Page: https://yuanze-lin.me/IllumiCraft_page/ - Github: https://github.com/yuanze-lin/IllumiCraft 🔹 Models citing this paper: No models found 🔹 Datasets citing this paper: No datasets found 🔹 Spaces citing this paper: No spaces found

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Mathematics for Computer Science Book Details - Discrete Mathematics: An Open Introduction - By Oscar Levin - 2025 Edition - 547 pages 🔗 Download the Book discrete.openmathbooks.org/pdfs/dmoi4.pdf

#MathematicsForCS #DiscreteMathematics #ComputerScience #MathForProgrammers #OpenSourceBooks #CSFundamentals #OscarLevin #MathForDevelopers #LearnDiscreteMath #CS2025

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Repost from Github Top Repositories

🐍Looking to get started with Deep Learning using PyTorch? This well-structured GitHub repository is a goldmine for beginners who want to learn PyTorch with hands-on examples and clear explanations📖. 🗂 What’s Inside?

🈂 Jupyter Notebooks with interactive code. 🧠 Step-by-step tutorials on Tensors, Autograd, and Neural Networks. 🖼 Real-world mini-projects like image classification. ⌛ Practical guides on using GPU with PyTorch. ✅ Beginner-friendly but also great for revision.

💡If you're serious about learning AI, this is one of the best free resources to kick off your journey🤝. 🖥 GitHub

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If anyone wants you to donate stars, please donate by gifts, it will be better for us. There are gifts at a price of 10 stars

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🥇 40+ Real and Free Data Science Projects 👨🏻‍💻 Real learning means implementing ideas and building prototypes. It's time to skip the repetitive training and get straight to real data science projects! 🔆 With the DataSimple.education website, you can access 40+ data science projects with Python completely free ! From data analysis and machine learning to deep learning and AI. ✏️ There are no beginner projects here; you work with real datasets. Each project is well thought out and guides you step by step. For example, you can build a stock forecasting model, analyze customer behavior, or even study the impact of major global events on your data. ┌🏳️‍🌈 40+ Python Data Science Projects └ 🌎 Website

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Repost from Machine Learning with Python

This channels is for Programmers, Coders, Software Engineers. 0️⃣ Python 1️⃣ Data Science 2️⃣ Machine Learning 3️⃣ Data Visualization 4️⃣ Artificial Intelligence 5️⃣ Data Analysis 6️⃣ Statistics 7️⃣ Deep Learning 8️⃣ programming Languages ✅ https://t.me/addlist/8_rRW2scgfRhOTc0 ✅ https://t.me/Codeprogrammer

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Self-attention in LLMs, clearly explained

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Supervised Learning: Classification and Regression Download: https://faculty.ucmerced.edu/mcarreira-perpinan/teaching/CSE176/lecturenotes.pdf

#SupervisedLearning #MachineLearning #Classification #Regression #MLNotes #DataScience #AIResources #MLTheory #MLLectures #LearnML

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