Machine Learning with Python

How I cut Codex API costs without changing my workflow I wanted a cheaper Codex endpoint, but price means little if requests fail halfway through a coding task. Relyven supports the Responses API used by Codex. Its dashboard shows route status, latency, usage, cost, and request logs, so failures are easier to trace. For gpt-5.6-sol, the current rates are: • Input: $0.30 per 1M tokens • Output: $1.80 per 1M tokens • Cache read: $0.03 per 1M tokens That is under 10% of OpenAI’s standard API rates. Input cache hit rates can exceed 90%, which keeps repeated context inexpensive during Codex sessions. New accounts receive $1 in free test credit, enough to configure the endpoint and run a real coding task before adding balance. Codex setup guide: https://tglink.io/8b868c7b656a00

Personal AI assistant in 5 minutes No code. No card. Free 😳 Works in Telegram, WhatsApp, or Discord — just send it tasks by voice or text. It gets things done, not just tells you how to do them.

• reads and sends emails • creates and edits Google Sheets • uploads files to Google Drive • works in Notion • sends reminders • generates PDFs, images, and videos • actually makes life and work easier

✅ Create your personal AI assistant here → getamplify.team

Tensor Algebra: A Small Concept That Has a Big Impact in AI 🧠 One thing I realized while learning deep learning is that tensors are everywhere. Whether you're working with TensorFlow, PyTorch, or building transformer models, almost everything revolves around tensor operations. Although we often think of tensors as multi-dimensional arrays in machine learning, they're the structures that allow neural networks to efficiently represent and process complex data. Here's a quick summary: - Scalar (Rank 0): A single value - Vector (Rank 1): A one-dimensional collection of values - Matrix (Rank 2): A two-dimensional arrangement of values - Tensor (Rank 3 or higher): A higher-dimensional representation used to model complex data A few places where tensors show up every day: - Images are represented as 3D tensors (Height × Width × Channels). - Mini-batches become 4D tensors during model training. - Transformer models process embeddings, attention scores, and hidden states as tensors throughout the network. - Operations like matrix multiplication, broadcasting, reshaping, tensor contraction, and automatic differentiation power modern deep learning. I created the infographic below as a simple visual reference while revisiting tensor algebra. I hope it's helpful for anyone learning deep learning or refreshing the fundamentals. I'm curious. How did you first learn about tensors? - Through mathematics? - While using TensorFlow or PyTorch? - During your first deep learning project? - Or was there another resource that made the concept finally click? I'd love to hear your experience and any resources you'd recommend for beginners. Looking forward to learning from your experiences and recommendations. #DeepLearning #TensorFlow #PyTorch #AI #MachineLearning #Tensors ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

Contributing to the advertising campaign may be beneficial to you, as all ads on our channel are real and not fake.

🙌 If I only had one weekend to master Claude, I would start with these resources. 👩🏻‍💻 Stop saving dozens of different Claude guides that you'll never actually read! This list contains only the resources that are truly useful for real-world projects. 💗 Level 1 — Basic Fundamentals (17 minutes) 🟡 Claude Explained Simply (For Beginners) 🟡 Getting Started with Claude ➖ ➖ ➖ 💗 Level 2 — Real-World Workflows (1 hour) 🟠 Working with Claude Daily 🟠 Claude for Work Teams 🟠 Brainstorming and Design with Claude 🟠 Combining Teamwork and Project Management 🟠 Creating Presentations with Claude 🟠 Claude Skills ➖ ➖ ➖ 💗 Level 3 — Professional Level (3.5 hours) 🔵 How to Avoid Generic and Machine-Like Responses from Claude? 🔵 Coding with Claude 🔵 The Basics of Claude 🔵 How to Avoid Reaching Claude's Limit? 🔵 Saying Goodbye to Traditional Prompt Engineering ➖ ➖ ➖ 💗 Level 6 — Expert Level (8 hours) 🟢 Understanding Claude's Computational Capabilities ➖ ➖ ➖ 💡 Remember, you don't need dozens of different guides; you just need the right resources, in the right order. 🤖 Claude 101

📌Beyond-NanoGPT: Concise and annotated implementations of key deep learning ideas. If you want to not just run pre-built models, but understand how they work "under the hood," the Beyond-NanoGPT repository is what you need. This project, created by a CS graduate student at Stanford University, serves as a bridge between simple examples like nanoGPT and complex implementations, offering dozens of implementations of modern deep learning methods. Everything is written from scratch in PyTorch, with detailed comments – perfect for those who are tired of abstract papers and ruthless production code. Each line of code is written in a way that makes it clear how to use it in practice. Stuck at the level of reading endless tutorials and want to move forward? This repository is a great step. It won't make you an expert in a week, but it will give you the tools to understand modern papers and start your own experiments. And yes, there's no fancy web interface or ready-made SaaS solutions here – just code, comments, and your curiosity. As it should be in research. Getting started is very simple: clone the repository, install the dependencies, and you can start diving into the code. Architectures? There's a Vision Transformer for image classification, a Diffusion Transformer for generation, ResNet, and even an MLP-Mixer. Each script is a separate experiment. For example, to train DiT on the CIFAR-10 dataset, you just need to run

train_dit.py

. Everything is designed for a single GPU, so you can practice even without access to powerful clusters. And if you want to understand the mechanisms of attention, separate notebooks will show you how Grouped-Query, linear, sparse, or cross-attention work – with visualizations and explanations. The project isn't just about architectures; there are also practical techniques. Want to speed up the inference of a language model? Take a look at the implementation of KV-caching or speculative decoding – methods that are actively used in LLM infrastructure. Interested in RL? The reinforcement learning section includes classics like DQN and PPO for Cartpole, and plans include a neural network for chess with MCTS. Moreover, the code not only works but also explains the nuances: why a baseline is important in REINFORCE, how to avoid gradient explosion in transformers, or what makes RoPE embeddings better than standard ones. Some sections (Flash Attention, RLHF) are still under development. But the plans are ambitious: the author promises everything from weight quantization to distributed RL. 📌Licensing: MIT License. 🖥GitHub

Xento Banner 1 Xento Why not win $200 while you're at it? 🏆 Xento — Complete quests. Earn real cash. Top 10 win every week. Join free 👇 Ad. 18+

Learn to Code in Python 3: Programming beginner to advanced Python3 programming made easy with exercises, challenges and lots of real life examples. Learn to code today!Programming 🏷 Category: Development 🌍 Language: English 👥 Students: 333,817 students ⭐️ Rating: 4.5/5.0 💰 Price: $69.99 ⟹ FREE 🆔 Coupon: AUGUSTFREE22026 ⚡ Opens instantly — your free link unlocks on its own in seconds, no ad required. 💎 By: https://t.me/Udemy26 #Programming #Coding #Development #Tech #Python #DataScience

🎓 Need help turning your academic or technical idea into a real project? Whether you're a university student, graduate researcher, or working on your final-year project, ResearchHub AI can help you move forward with expert guidance and practical technical support. 🔬 Get support with: • Thesis & Dissertation Projects • Research Design & Methodology • Data Analysis, Statistics & SPSS • Machine Learning & Artificial Intelligence • Python, Computer Vision & Software Development • Graduation & Final-Year Projects • MATLAB, ANSYS, CFD & Engineering Simulations • Academic Writing, Editing & Publication Support • Research Consultation & Project Planning • Professional Websites, Dashboards, APIs & Custom Software 💡 Have an idea but don't know where to start? Tell the team what you want to achieve and turn your idea into a clear, actionable project plan. 🚀 From the first research question to data analysis, AI models, programming, simulations, and technical implementation — get focused support for serious academic and technical work. 🌐 ResearchHub AI — Start Your Project #Research #Students #Thesis #GraduationProject #ArtificialIntelligence #Programming

⭐️ Hello my advertiser friend! I’m Eng. Hussein Sheikho 👋 and I’m excited to share our special promotional offer with you! 🎯 💥 Promo Offer: Promote your ad across all our listed channels for only $45! 💰 📢 We accept all types and formats of advertisements. ✅ Publishing Plan: Your ad will be published for 20 days across all our channels, plus it will be pinned for 7 days 🔝 🧑‍💻 For Programming Channel Owners Only: Want your tech channel to grow fast? 🚀 You can add your channel to our promo folder for just $20/month — average growth rate 2000+ subscribers/month 📈 📩 Contact me for more details: 👉 t.me/HusseinSheikho 🌱 Let’s grow together! Our Share folder (our channels) 👇 https://t.me/addlist/8_rRW2scgfRhOTc0

Follow the Machine Learning with Python channel on WhatsApp: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

Don't forget to try it; it's free and includes most AI models.

🔥 More models. Lower cost. One API key. Access GPT, Claude, Grok, Gemini, DeepSeek, Kimi, Qwen and more through one gateway. 💰 Better value Access leading models at prices below official API list rates. 🔌 One unified gateway Connect apps, agents and coding tools with one Smart API key. 📈 Clear costs Track every request, token and cost in one place. 🛡️ Reliable access Choose model groups with ordered fallback options. ⚡️ Mode Website: https://modelflare.dev/ 👉 Models & pricing: https://modelflare.dev/pricing 💬 Join the ModelFlare community: https://t.me/+GxEEPAsQ0ERiOGUx

It's a paid partnership, but the experience is unique and I really liked it. Try it yourself.

🔥 More models. Lower cost. One API key. Access GPT, Claude, Grok, Gemini, DeepSeek, Kimi, Qwen and more through one gateway. 💰 Better value Access leading models at prices below official API list rates. 🔌 One unified gateway Connect apps, agents and coding tools with one Smart API key. 📊 Clear costs Track every request, token and cost in one place. 🛡 Reliable access Choose model groups with ordered fallback options. 👉 Models & pricing: https://modelflare.dev/pricing?utm_source=telegram&utm_medium=organic_social&utm_campaign=telegram_cn_202608&utm_content=value_models_one_api_v1 ⚡️ Create an account: https://modelflare.dev/sign-up?utm_source=telegram&utm_medium=organic_social&utm_campaign=telegram_cn_202608&utm_content=value_models_one_api_signup_v1 💬 Join the ModelFlare community: https://t.me/+GxEEPAsQ0ERiOGUx

🚨 SURPRISE ALERT! 🚨 Stop paying full price on Udemy. Seriously. 💸 I built a bot that hunts down 100% FREE Udemy coupons 24/7 — while you sleep, eat, or scroll. 🎯 Here's the magic: 📚 Mini App catalog — every active free coupon in one place 🔔 Auto-push — new courses land straight in your chat 📢 Live channel — never miss a deal Why it matters? Most people pay $200+ for courses you can grab for $0 — if you know where to look. Now you have a bot that does the looking for you. ⚡ 🎓 Try it now: https://t.me/UdemySybot?start=ref_channel Your future self (and your wallet) will thank you. 💜

🎓 New Free Course Alert! Data Structures & Algorithms (Python): Practice Exams Ace technical coding interviews with 200 questions on Big O, Graphs, Hash Maps, and Dynamic Programming.… 📁 Category: Development / Software Engineering 🎯 Level: Intermediate Level 🗣 Language: English 👨‍🎓 Enrolled: 317 students ⭐ Rating: 0/5.0 💵 Price: $49.99 ➜ FREE (100% OFF) 🎟 Coupon: BC7C61AF20F4AE188D90 ⚡ Your free link unlocks automatically in a few seconds — no ad required. 💎 By: https://t.me/Udemy26 #Programming #Coding #Development #Tech #Python #DataScience