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Code with Brij

Code with Brij

前往频道在 Telegram

📈 Telegram 频道 Code with Brij 的分析概览

频道 Code with Brij (@codewithbrij) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 14 010 名订阅者,在 技术与应用 类别中位列第 9 187,并在 马来西亚 地区排名第 2 750

📊 受众指标与增长动态

невідомо 创建以来,项目保持高速增长,吸引了 14 010 名订阅者。

根据 24 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 -163,过去 24 小时变化为 -3,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 10.80%。内容发布后 24 小时内通常能获得 N/A% 的反应,占订阅者总量。
  • 帖子覆盖: 每篇帖子平均可获得 0 次浏览,首日通常累积 0 次浏览。
  • 互动与反馈: 受众积极参与,单帖平均反应数为 0

📝 描述与内容策略

尚未提供频道描述。

凭借高频更新(最新数据采集于 25 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 技术与应用 类别中的关键影响点。

14 010
订阅者
-324 小时
-287
-16330
帖子存档
68 Python notebook exercises for "Understanding Deep Learning : https://udlbook.github.io/udlbook/
68 Python notebook exercises for "Understanding Deep Learning : https://udlbook.github.io/udlbook/

As we celebrate the luminous festival of Diwali, I am reminded of the countless lights that each of you have kindled through
As we celebrate the luminous festival of Diwali, I am reminded of the countless lights that each of you have kindled through your dedication to learning and growth. It's not just the candles that illuminate our homes, but the spark of knowledge and ambition that brightens our paths. In the spirit of Diwali, I want to express my deepest gratitude for your trust and engagement. Our community is not just about sharing educational content; it's about lighting up dreams and igniting passions. It's about creating a brighter future, one enlightened mind at a time. As you celebrate this auspicious festival, take a moment to reflect on your journey so far and the paths yet to be explored. May this Diwali bring you joy, prosperity, and the courage to chase your dreams. May the light of knowledge continue to guide you in your career and beyond. Happy Diwali to you all, and here's to continuing our journey of learning and growing together. 🪔✨

𝗙𝗿𝗲𝗲 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝘁𝗼 𝗹𝗲𝗮𝗿𝗻 𝗙𝘂𝗹𝗹 𝗦𝘁𝗮𝗰𝗸 𝗣𝘆𝘁𝗵𝗼𝗻 𝘄𝗶𝘁𝗵 𝗙𝗹𝗮𝘀𝗸 & 𝗙𝗮𝘀𝘁𝗔𝗣𝗜 : https://www.linkedin.com/posts/brijpandeyji_%3F%3F%3F%3F%3F%3F%3F%3F%3F%3F%3F%3F%3F-%3F%3F-%3F%3F%3F%3F%3F-activity-7128401418197049344-lcWO

200+ DevOps Interview Questions

One of the most effective ways to learn machine learning is by getting hands-on experience and building something yourself. While finding inspiration can be challenging, exploring projects by others can open your eyes to the endless possibilities. 💡 The projects I am sharing are perfect for those new to machine learning and curious about its potential.

In the IT world, knowing the ropes of Authentication and Authorization is indispensable, regardless of your focus. To aid in your mastery, I've curated a selection of top-notch, free resources. Sharpen your skills with these treasures: 🔐 Online Courses: - "Authentication - Methods, Protocols, and Strategies" by Frontegg: https://frontegg.com/blog/authentication-types - "Secure User Authentication Methods – 2FA, Biometric, and Passwordless Login Explained" by InfosecHQ: https://www.infosecinstitute.com/log-in/ - "Microsoft Entra ID - Authentication" by Microsoft Learn: https://learn.microsoft.com/en-us/entra/verified-id/decentralized-identifier-overview 📝 Articles: - "Authentication: What It Is and Why It's Important" by OWASP: https://cheatsheetseries.owasp.org/cheatsheets/Authentication_Cheat_Sheet.html - "A Beginner's Guide to Authentication and Authorization" by Auth0: https://auth0.com/docs/get-started/identity-fundamentals/authentication-and-authorization - "Passwordless Login: What It Is and How It Works" by Okta: https://www.okta.com/resources/whitepaper-how-to-go-passwordless-with-okta/ 🎥 Videos: - "Authentication Explained" by Google Cloud Platform: https://m.youtube.com/watch?v=PAOb2hl__08 - "Multi-Factor Authentication: What It Is and Why You Need It" by Cloudflare: (https://m.youtube.com/watch?v=42iNs-v6gTw - "Passwordless Login: The Future of Authentication?" by Auth0: https://auth0.com/resources/videos/passwordless-explainer-video ---

Few resources that can elevate your understanding of AI and LLM - 1. Machine Learning Crash Course: A practical introduction to ML fundamentals, featuring interactive exercises and real-world case studies. 🔗 https://developers.google.com/machine-learning/crash-course 2. TensorFlow APIs: Whether you're a beginner or an expert, these tutorials will help you master TensorFlow's suite to build and deploy ML models. 🔗 https://www.tensorflow.org/tutorials 3. ML Practitioner Guides: For the seasoned professionals, these guides offer deep dives into solving complex ML problems effectively. 🔗 https://developers.google.com/machine-learning/guides 4. AI Explanations: Understand the workings behind AI decisions with resources focused on interpretability. 🔗 https://cloud.google.com/ai-explanations Leverage these resources to enhance your expertise and innovate in your field. 🌐

Hey folks! 🌟 I just found this cool, free Data Engineering Bootcamp and thought it could be a big help to many of us here. It's got some awesome topics covered. Check it out at https://dezoomcamp.streamlit.app/. I think you’ll find it really useful. Happy learning! 🚀

There is another Hands-on Free session on building AI app using React. Anyone can join and learn this art at no cost.Join me 🙋‍♂️ in this FREE hands-on session, where we'll learn the magic of building an AI app with React! 🚀✨ 📅 Mark your calendar! 𝗧𝗵𝘂𝗿𝘀𝗱𝗮𝘆, 𝗢𝗰𝘁𝗼𝗯𝗲𝗿 𝟮𝟲𝘁𝗵 𝟭𝟬:𝟬𝟬𝗮𝗺 𝗣𝗗𝗧 ⏰ Set that alarm! 🔗 𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗻𝗼𝘄 𝘁𝗼 𝘀𝗲𝗰𝘂𝗿𝗲 𝘆𝗼𝘂𝗿 𝗙𝗥𝗘𝗘 𝘀𝗽𝗼𝘁 ➡️ https://brij.guru/ai

Over the months,I've noticed how many of you have been tirelessly searching for the content and tutorials that I poured my heart into. I want every single one of you to have unrestricted access to these resources at a single place to learn and grow. With that in mind, I've decided to open source all my materials, making them freely available for the benefit of all. However, I need a little help in doing so. I'm on the lookout for a kind soul who can assist me in creating a beautiful and user-friendly GitHub page to showcase this work. This is more than just a repository; it's a testament to our shared journey of growth and learning. If you can help, or know someone who can, please reach out. The person should know how to host pages on Github. Let's make this knowledge easily accessible for everyone, present and future.

100% Free Let's learn how to build high performance LLM Apps using inventory, product, and review data in a hands-on interactive session. Join me in this upcoming webinar for a hands-on, interactive session on how to build high-performance LLM apps using inventory, product, and review data. 🗓️ Don't miss out! This 𝗧𝘂𝗲𝘀𝗱𝗮𝘆, 𝗢𝗰𝘁𝗼𝗯𝗲𝗿 𝟮𝟰𝘁𝗵, at 𝟭𝟬:𝟬𝟬 𝗮𝗺 𝗣𝗗𝗧 👉 𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗵𝗲𝗿𝗲: https://brij.guru/ai

Data types are foundational in computing, and it's essential to understand them to work effectively in any programming environment. Let's take a dive into the top ten commonly used data types: 1. Integer (int): - Represents whole numbers. - Examples: -2, -1, 0, 1, 2, 3 2. Floating Point (float/double): - Represents numbers with decimals. - Examples: -2.5, 0.0, 3.14 3. Character (char): - Represents single characters. - Examples: 'A', 'b', '1', '%' 4. String: - Represents sequences of characters, basically text. - Examples: "Hello", "ChatGPT", "1234" 5. Boolean (bool): - Represents true or false values. - Examples: True, False 6. Array: - Represents a collection of elements, often of the same type. - Examples: [1, 2, 3], ["apple", "banana", "cherry"] 7. Object: - Used in object-oriented programming, represents a combination of data and methods to manipulate the data. - Examples: A Car object might have data like color and speed and methods like drive() and park(). 8. Date & Time: - Represents date and time values. - Examples: 23-10-2023, 12:30:45 9. Byte & Binary: - Represents raw binary data. - Examples: 01010101 (Byte), 101000111011 (Binary) 10. Enum: - Represents a set of named constants. - Examples: Days of the week (Monday, Tuesday...), Colors (Red, Blue, Green)

Essential API Tools for 2023 APIs are now the backbone of most digital projects. 🌐 To make sure you're all geared up for 2023, I've laid down a bunch of top tools by category. Dive in! 🏊‍♂️ 🔍 𝗗𝗲𝘀𝗶𝗴𝗻 & 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁: - Swagger (OpenAPI) for spec 📝 - RAML, PAML & API Blueprint for languages 🖊️ - Postman for that nifty design & testing 🛠️ - Mockoon for mock servers 👨‍💻 💼 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 & 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: - API {REST} & FakeRest for deployment 🚀 - Big names like Apigee, MuleSoft Anypoint, IBM API Connect & more for management 🏢 🧪 𝗧𝗲𝘀𝘁𝗶𝗻𝗴: - APACHE JMeter™ & JMeter for load testing ⚖️ - SoapUI for functional tests 🧫 - Rest-Assured & more for mock & test 🧐 🔄 𝗖𝗼𝗱𝗲 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻: - Swagger & RAML Codegen to help churn out that code 💻 🔐 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆: - From general tools like Postman to niche ones like OWASP ZAP & API Fortress 🛡️ - Autho, Okta & Keycloak got your back for auth 🚪 📊 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴: - Monitoring gods like New Relic, Datadog, & AppDynamics 🖥️ 🛡 𝗣𝗿𝗼𝘁𝗲𝗰𝘁𝗶𝗼𝗻: - Cloudflare, AWS WAF & Azure Firewall for that sturdy protection 🚫