Machine Learning with Python

前往频道在 Telegram

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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网络:Machine Learning with Python 印度5 038 教育2 416...

📈 Telegram 频道 Machine Learning with Python 的分析概览

频道 Machine Learning with Python (@codeprogrammer) 英语语言赛道中的是活跃参与者。目前社区聚集了 67 813 名订阅者，在教育类别中位列第 2 416，并在印度地区排名第 5 038 位。

📊 受众指标与增长动态

自 невідомо 创建以来，项目保持高速增长，吸引了 67 813 名订阅者。

根据 09 六月, 2026 的最新数据，频道保持稳定运转。过去 30 天订阅人数变化为 70，过去 24 小时变化为 10，整体触达仍然可观。

认证状态： 未认证
互动率 (ER)： 平均受众互动率为 2.94%。内容发布后 24 小时内通常能获得 2.44% 的反应，占订阅者总量。
帖子覆盖： 每篇帖子平均可获得 1 997 次浏览，首日通常累积 1 652 次浏览。
互动与反馈： 受众积极参与，单帖平均反应数为 7。
主题关注点： 内容集中在 insidead, learning, degree, evaluation, algorithm 等核心主题上。

📝 描述与内容策略

作者将该频道定位为表达主观观点的平台：
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho”

凭借高频更新（最新数据采集于 10 六月, 2026），频道始终保持新鲜度与高覆盖。分析显示受众积极互动，使其成为教育类别中的关键影响点。

67 813

订阅者

+1024 小时

+127 天

+7030 天

1 997

帖子浏览量

~ 1 65224 小时

~ 2 22648 小时

2.94%

参与率

~ 3

每日帖子数

Ads index

beta

帖子存档

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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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ask me https://t.me/m/-nTmpj5vYzNk

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A curated collection of Kaggle notebooks showcasing how to build end-to-end AI applications using Hugging Face pretrained models, covering text, speech, image, and vision-language tasks — full tutorials and code available on GitHub: 1️⃣ Text-Based Applications 1.1. Building a Chatbot Using HuggingFace Open Source Models https://lnkd.in/dku3bigK 1.2. Building a Text Translation System using Meta NLLB Open-Source Model https://lnkd.in/dgdjaFds 2️⃣ Speech-Based Applications 2.1. Zero-Shot Audio Classification Using HuggingFace CLAP Open-Source Model https://lnkd.in/dbgQgDyn 2.2. Building & Deploying a Speech Recognition System Using the Whisper Model & Gradio https://lnkd.in/dcbp-8fN 2.3. Building Text-to-Speech Systems Using VITS & ArTST Models https://lnkd.in/dwFcQ_X5 3️⃣ Image-Based Applications 3.1. Step-by-Step Guide to Zero-Shot Image Classification using CLIP Model https://lnkd.in/dnk6epGB 3.2. Building an Object Detection Assistant Application: A Step-by-Step Guide https://lnkd.in/d573SvYV 3.3. Zero-Shot Image Segmentation using Segment Anything Model (SAM) https://lnkd.in/dFavEdHS 3.4. Building Zero-Shot Depth Estimation Application Using DPT Model & Gradio https://lnkd.in/d9jjJu_g 4️⃣ Vision Language Applications 4.1. Building a Visual Question Answering System Using Hugging Face Open-Source Models https://lnkd.in/dHNFaHFV 4.2. Building an Image Captioning System using Salesforce Blip Model https://lnkd.in/dh36iDn9 4.3. Building an Image-to-Text Matching System Using Hugging Face Open-Source Models https://lnkd.in/d7fsJEAF ➡️ You can find the articles and the codes for each article in this GitHub repo: https://lnkd.in/dG5jfBwE

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🐍📰 This tutorial will give you an overview of LangGraph fundamentals through hands-on examples, and the tools needed to build your own LLM workflows and agents in LangGraph Link: https://realpython.com/langgraph-python/

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𝗠𝗮𝘀𝘁𝗲𝗿 𝗣𝘆𝗦𝗽𝗮𝗿𝗸 𝗟𝗶𝗸𝗲 𝗮 𝗣𝗿𝗼 – 𝗔𝗹𝗹-𝗶𝗻-𝗢𝗻𝗲 𝗚𝘂𝗶𝗱𝗲 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 If you're a data engineer, aspiring Spark developer, or someone preparing for big data interviews — this one is for you. I’m sharing a powerful, all-in-one PySpark notes sheet that covers both fundamentals and advanced techniques for real-world usage and interviews. 𝗪𝗵𝗮𝘁'𝘀 𝗶𝗻𝘀𝗶𝗱𝗲? • Spark vs MapReduce • Spark Architecture – Driver, Executors, DAG • RDDs vs DataFrames vs Datasets • SparkContext vs SparkSession • Transformations: map, flatMap, reduceByKey, groupByKey • Optimizations – caching, persisting, skew handling, salting • Joins – Broadcast joins, Shuffle joins • Deployment modes – Cluster vs Client • Real interview-ready Q&A from top use cases • CSV, JSON, Parquet, ORC – Format comparisons • Common commands, schema creation, data filtering, null handling 𝗪𝗵𝗼 𝗶𝘀 𝘁𝗵𝗶𝘀 𝗳𝗼𝗿? Data Engineers, Spark Developers, Data Enthusiasts, and anyone preparing for interviews or working on distributed systems.

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Best Data Science Archive Notes

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AI vs ML vs Deep Learning vs Generative AI

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Repost from Tech Jobs Hub

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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

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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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🔹 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

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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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