💻وبینار رایگان «بهبود عملکرد شخصی و حرفه‌ای با روش OKR »

👤سخنران: مهندس امیر ایمن‌پور
مدیر کسب‌وکار و مربی OKR

📌 سرفصل‌ها:
🔹خیلی کوتاه بدانیم OKR چیست
🔸مثال اول (شرکتی): ایجاد OKR در یک شرکت فعال درحوزه نرم افزار
🔹مثال دوم (شخصی): ایجاد OKR برای پدر یک خانواده

📆 تاریخ: چهارشنبه ۶ اردیبهشت | ساعت ۱۹

🔗 ثبت‌نام رایگان وبینار:
https://eseminar.tv/wb101036
➖➖➖➖➖➖➖➖➖➖ 
📱 @tiheac 
🌐 https://tihe.ac.ir 
☎️ 02186741

DeepLearning.AI announced today a new short course - ChatGPT Prompt Engineering for Developers by Andrew NG and Isa Fulford. The one-hour course covers the following topics:

✅ Prompt engineering best practices for application development
✅ Approaches for using LLMs to build your own custom chatbot
✅ Hands-on practice with the OpenAI API

The course is free for a limited time.
https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/

Free AI Course from Google: https://ai.google/education/

Free Python IBM course with Certificate: https://cognitiveclass.ai/courses/python-for-data-science

6-Month Roadmap to Becoming a Machine Learning Engineer for Free 19 free lessons to get you interview-ready and move ahead of 90% of people. Follow these steps in the specified order to ensure success: 𝗠𝗼𝗻𝘁𝗵 𝟭: 𝗠𝗮𝘁𝗵𝗲𝗺𝗮𝘁𝗶𝗰𝘀 & 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 Weeks 1-2: Study Linear Algebra concepts - https://lnkd.in/eabKGp_p Weeks 3-4: Continue with Calculus and Probability & Statistics. Practice problems to solidify your understanding - https://lnkd.in/ea2DmZ2d 𝗠𝗼𝗻𝘁𝗵 𝟮: 𝗦𝗤𝗟 & 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀 𝗮𝗻𝗱 𝗠𝗼𝗿𝗲 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 Weeks 1-2: Learn SQL basics - https://lnkd.in/ea2DmZ2d Weeks 3-4: Continue studying Probability & Statistics. Apply statistical concepts in SQL where possible. 𝗠𝗼𝗻𝘁𝗵 𝟯: 𝗖𝗼𝗿𝗲 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀 𝗼𝗳 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 Weeks 1-2: Go through Google's ML Crash Course - https://lnkd.in/eT7NiGp6 Weeks 3-4: Go through Andrew Ng's ML Course - https://lnkd.in/e964AiC7 𝗠𝗼𝗻𝘁𝗵 𝟰: 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗦𝗸𝗶𝗹𝗹𝘀 (𝗣𝘆𝘁𝗵𝗼𝗻 & 𝗟𝗶𝗯𝗿𝗮𝗿𝗶𝗲𝘀) Weeks 1-2: Learn Python basics. https://lnkd.in/euyfHHxa Weeks 3-4: Start with Python libraries for Machine Learning. • Scikit-learn - https://lnkd.in/eqFhCwXt • TensorFlow - https://lnkd.in/e6RWbe9h • PyTorch - https://lnkd.in/efhPxZPM 𝗠𝗼𝗻𝘁𝗵 𝟱: 𝗠𝗼𝗱𝗲𝗹 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 & 𝗧𝘂𝗻𝗶𝗻𝗴 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀, 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹𝘀 Weeks 1-2: Learn about model training and tuning techniques. • Intermediate ML - https://lnkd.in/e89AmkzE • Hyperparameter Tuning - https://lnkd.in/ezEnqeG2    Weeks 3-4: Start with Advanced Deep Learning Models. • Stanford's CS231n (CNNs) - http://cs231n.github.io/ • Deep Learning Book - https://lnkd.in/e_utEgZM 𝗠𝗼𝗻𝘁𝗵 𝟲: 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁, 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴, & 𝗠𝗮𝗶𝗻𝘁𝗲𝗻𝗮𝗻𝗰𝗲 𝗮𝗻𝗱 𝗥𝗲𝘀𝘂𝗺𝗲 𝗣𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻, 𝗦𝗼𝗳𝘁 𝗦𝗸𝗶𝗹𝗹𝘀 & 𝗧𝗶𝗽𝘀 Weeks 1-2: Learn about deployment, monitoring, and maintenance. • Docker - https://lnkd.in/esXHzx9k • Git - https://lnkd.in/esQ8FMxS • AWS ML - https://lnkd.in/eZcdQPee • Azure ML - https://lnkd.in/e5fvmvtk    Weeks 3-4: Prepare your resume and improve your soft skills Resume and Soft Skills & Tips, and work on projects. • 217 Machine Learning Projects - https://lnkd.in/e5kyv3Tv

𝗬𝗼𝘂𝗿 𝟰-𝗠𝗼𝗻𝘁𝗵𝘀 𝗕𝗮𝗰𝗸𝗲𝗻𝗱 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗣𝗹𝗮𝗻 🚀 1️⃣ 𝗠𝗼𝗻𝘁𝗵 𝟭: 𝗚𝗲𝘁 𝘆𝗼𝘂𝗿 𝗯𝗮𝘀𝗶𝗰𝘀 𝗿𝗶𝗴𝗵𝘁 1. Learn Python: https://lnkd.in/eb4ke-9P 2. Python Projects: https://lnkd.in/eNWBfNzk 3. DSA with Python: http://bit.ly/3G3Dh0V     2️⃣ 𝗠𝗼𝗻𝘁𝗵 𝟮: 𝗗𝗶𝘃𝗲 𝗶𝗻𝘁𝗼 𝗙𝗹𝗮𝘀𝗸 𝗮𝗻𝗱 𝗔𝗣𝗜 Learn Flask: https://lnkd.in/eqAg3jZP Flask Projects: https://lnkd.in/eqnf7h-W Learn REST API with Flask: https://lnkd.in/e-TTahQf 3️⃣ 𝗠𝗼𝗻𝘁𝗵 𝟯: 𝗠𝗮𝘀𝘁𝗲𝗿 𝗮𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗰𝗼𝗻𝗰𝗲𝗽𝘁𝘀 𝗮𝗻𝗱 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀 1. Learn Multithreading, Multiprocessing, Asyncio: https://lnkd.in/e_99Jiwp 2. Gunicorn & Nginx with Flask: https://lnkd.in/eWxgTNdB 3. TDD with Python & Flask: https://lnkd.in/eMjweHuZ 4. Basic RDBMS: https://lnkd.in/ebkPd8-q 5. Learn SQL: https://sqlbolt.com/ & W3Schools.com 6. PostgreSQL with Python: https://lnkd.in/esKUqNdt 7. Flask App with PostgreSQL: https://lnkd.in/eTzpcwNc     4️⃣ 𝗠𝗼𝗻𝘁𝗵 𝟰: 𝗣𝗼𝗹𝗶𝘀𝗵 𝘆𝗼𝘂𝗿 𝘀𝗸𝗶𝗹𝗹𝘀 𝗮𝗻𝗱 𝗽𝗿𝗲𝗽𝗮𝗿𝗲 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗷𝗼𝗯 𝗺𝗮𝗿𝗸𝗲𝘁 1. Basics of Bash: https://lnkd.in/eZnG8cP6 2. Basics of Docker: https://lnkd.in/eFEK_aXW 3. Deploy Flask App with Docker: https://lnkd.in/eTjnFW8Y 4. GIT & GitHub: https://lnkd.in/ejshTxFw 5. Python Portfolio on Github: https://lnkd.in/eB2AanXj 6. Python Resume Ideas: https://lnkd.in/e_Fb7uNi

دوره رایگان ماشین لرنینگ با SciKit-Learn
توسط دولوپر های کتابخانه SciKit-Learn
به مدت ۳۶ ساعت به همراه آزمون و سرتیفیکیشن
شروع دوره از ۸ نوامبر
لینک ثبت نام
https://www.fun-mooc.fr/en/courses/machine-learning-python-scikit-learn/

دوره احتمال برای کامپیوتر ساینس از دانشگاه استنفورد (بروز) به تازگی منتشر شد
Probability for Computer Scientists (CS109).

Video lecture: https://www.youtube.com/watch?v=2MuDZIAzBMY&list=PLoROMvodv4rOpr_A7B9SriE_iZmkanvUg
Course website: https://web.stanford.edu/class/archive/cs/cs109/cs109.1232/

👀تمامی دوره های وبسایت data science 365 به مدت دوهفته رایگان قابل دسترسی هستند از ۶ الی ۲۰ نوامبر
این دوره ها دارای سرتیفیکیشن شرکت در دوره هستند و از تنوع خوبی برخوردارند و سطح علمی قابل قبولی برای استفاده کاربردی دارند .

لینک ثبت نام

دیتا کمپ هم به از ۶ نوامبر تا ۱۲ نوامبر دسترسی به تمام کورس ها رو رایگان کرده که میتونید استفاده کنید
https://www.datacamp.com/freeweek

یکی از رودمپ های خوب به همراه منابع برای یادگیری مباحث CS بصورت خود خوان

دوره ویدیویی کتاب محبوب Islr با پایتون در یوتوب منتشر شده میتونید استفاده کنید
YouTube
Pdf

Introduction to the MLX - Apple's array framework for machine learning on Apple silicon

The Apple Machine Learning Research team released a Python library yesterday to support array processing on Apple silicon machines (e.g., M1, M2, M3). This framework is inspired by similar frameworks like NumPy, PyTorch, Jax, and ArrayFire, with the goal of levering Apple CPU architecture for optimal performance 🚀.

Installation: 𝒑𝒊𝒑 𝒊𝒏𝒔𝒕𝒂𝒍𝒍 𝒎𝒍𝒙

License: 𝐌𝐈𝐓 🦄

𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬 📚
Source code: https://github.com/ml-explore/mlx
Documentation: https://ml-explore.github.io/mlx/build/html/index.html
Examples: https://github.com/ml-explore/mlx-examples/tree/main

فایل زیر شامل 71پروژه کوچک با پایتون به همراه کده اگر در حال یادگیری این زبان برنامه نویسی هستین پیشنهاد میکنم استفاده کنید

Amazon recently rolled out an impressive $12 million scholarship program for generative AI! 𝗠𝗮𝘀𝘁𝗲𝗿 𝗔𝗜 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀: Dive into free courses and practical projects, solidifying your understanding of key AI concepts and techniques. 𝗟𝗲𝘃𝗲𝗹 𝘂𝗽 𝘂𝗻𝗱𝗲𝗿 𝗲𝘅𝗽𝗲𝗿𝘁 𝗴𝘂𝗶𝗱𝗮𝗻𝗰𝗲: Receive continuous coaching from industry professionals at every step, refining your skills and gaining valuable insights. 𝗕𝘂𝗶𝗹𝗱 𝗮 𝘀𝘁𝗮𝗻𝗱𝗼𝘂𝘁 𝗽𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼: Develop real-world projects that showcase your AI prowess and impress potential employers. 𝗧𝗮𝗽 𝗶𝗻𝘁𝗼 𝗮 𝘀𝘂𝗽𝗽𝗼𝗿𝘁 𝗻𝗲𝘁𝘄𝗼𝗿𝗸: Get instant help from on-demand technical tutors and connect with experienced industry mentors for career guidance. Land your dream AI job: Access exclusive career development resources and refine your job search strategy to land your ideal AI role.
𝗦𝘁𝗮𝗴𝗲 𝟭: 𝗔𝗪𝗦 𝗗𝗲𝗲𝗽𝗥𝗮𝗰𝗲𝗿 𝗦𝘁𝘂𝗱𝗲𝗻𝘁 (𝟭𝟲+ 𝘄𝗼𝗿𝗹𝗱𝘄𝗶𝗱𝗲) Build and race self-driving cars in a virtual world! Master the basics and unlock scholarship prerequisites.
https://student.deepracer.com/home
𝗦𝘁𝗮𝗴𝗲 𝟮: 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝗶𝗻𝗴 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝘄𝗶𝘁𝗵 𝗔𝗪𝗦 (𝗙𝗿𝗲𝗲 𝗳𝗼𝗿 𝘀𝗰𝗵𝗼𝗹𝗮𝗿𝘀𝗵𝗶𝗽 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝗻𝘁𝘀) Deepen your understanding of this cutting-edge technology. Earn a valuable $250 course and certificate.
https://aws.amazon.com/machine-learning/scholarship/
𝗦𝘁𝗮𝗴𝗲 𝟯: 𝗔𝗜 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗣𝘆𝘁𝗵𝗼𝗻 𝗡𝗮𝗻𝗼𝗱𝗲𝗴𝗿𝗲𝗲 (𝟮,𝟬𝟬𝟬 𝗮𝗻𝗻𝘂𝗮𝗹 𝘀𝗰𝗵𝗼𝗹𝗮𝗿𝘀𝗵𝗶𝗽𝘀, $𝟰,𝟬𝟬𝟬 𝗲𝗮𝗰𝗵) Become a Python pro with Udacity's top-rated nanodegree. Get personalized support, attend exclusive events, and connect with industry mentors.
https://aws.amazon.com/machine-learning/scholarship/
𝗦𝘁𝗮𝗴𝗲 𝟰: 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 & 𝗠𝗟 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝗡𝗮𝗻𝗼𝗱𝗲𝗴𝗿𝗲𝗲 (𝗧𝗼𝗽 𝟱𝟬𝟬 𝗳𝗿𝗼𝗺 𝗦𝘁𝗮𝗴𝗲 𝟯) Level up your skills and become a Deep Learning & ML master. Receive guidance from industry experts and build your dream career.
https://aws.amazon.com/machine-learning/scholarship/