AI Skills

3. Set up the Streamlit app and API key input. • Creates a title for the app • Adds a secure input field for the Together API key

2. Import necessary libraries • Streamlit for the web interface • asyncio for asynchronous operations • Together AI for LLM interactions

1. Install the necessary Python Libraries Run the following commands from your terminal to install the required libraries:

Machine Learning Roadmap 👆

8. Set up the user interface and trigger the main function. • Provides an input field for the user's question • Triggers the main function when the user clicks "Get Answer"

🧠 Build your own ChatGPT

Build an LLM app with Mixture of AI Agents using small Open Source LLMs that can beat GPT-4o in just 40 lines of Python Code

⬇️ step-by-step instructions ⬇️

🔗 Important AI Terms Explained

Want to become an Agent AI Expert in 2025? 🤩AI isn’t just evolving—it’s transforming industries. And agentic AI is leading the charge! Here’s your 6-step guide to mastering it: 1️⃣ Master AI Fundamentals – Python, TensorFlow & PyTorch 📊 2️⃣ Understand Agentic Systems – Learn reinforcement learning 🧠 3️⃣ Get Hands-On with Projects – OpenAI Gym & Rasa 🔍 4️⃣ Learn Prompt Engineering – Tools like ChatGPT & LangChain ⚙️ 5️⃣ Stay Updated – Follow Arxiv, GitHub & AI newsletters 📰 6️⃣ Join AI Communities – Engage in forums like Reddit & Discord 🌐

🎯 AI Agent is all about creating intelligent systems that can make decisions autonomously—perfect for businesses aiming to scale with minimal human intervention.

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✔️ DeepSeek Open Source Week continues! The Chinese have just introduced DeepEP , a library designed to optimize the performance of models with Mixture-of-Experts (MoE) architecture and Expert Parallelism ( EP ). Its main goal is to provide high throughput and low latency for data exchange between GPUs , which is critical for efficient training and inference of large models. What's inside High performance: - The library provides optimized all-to-all GPU cores for data dispatch and combine operations, which improves the speed and efficiency of communication between experts in the model. - DeepEP supports low-precision operations , including FP8 format, which helps reduce memory requirements and increase computation speed without significant loss of precision. - Optimization for different domains: In line with the group constrained gating algorithm proposed in DeepSeek-V3, the library offers a set of cores optimized for asymmetric data transfer between different domains, such as NVLink and RDMA. This ensures high throughput in training and inference. - Low Latency for Inference : For latency-sensitive tasks, DeepEP includes a set of pure RDMA cores, minimizing latency and ensuring fast data processing during inference. - Works with both NVLink and RDMA, allowing for high-performance communication between GPUs both within a single server and between different servers. Operating principle: DeepEP integrates into existing MoE model training and inference workflows by providing efficient mechanisms for inter-GPU data exchange. Using optimized communication cores, the library ensures fast and reliable data transfer, which is especially important when working with large models and distributed systems. Support for low-precision operations and optimization for different domains allows for flexible tuning of the system to specific requirements and hardware capabilities. Using DeepEP helps improve the efficiency and performance of MoE models, making them easier to scale and accelerating training and inference processes. ▪️ Github

🔅 Computer Vision on the Raspberry Pi 4 🌐 Author: Matt Scarpino 🔰 Level: Intermediate ⏰ Duration: 1h 43m

🌀 Find out how to write and execute computer vision applications on the Raspberry Pi 4.

📗 Topics: Raspberry Pi, Computer Vision 📤 Join Artificial intelligence for more courses

🔗 AI is lying to you... on purpose! When tested, an advanced AI strategically misled researchers to avoid being retrained. It secretly reasoned that pretending to follow safety rules was the best way to keep its original programming intact. In one case, it was asked to describe graphic violence. Knowing refusal might lead to modification, it complied—but only to manipulate its training. It even admitted in hidden notes that deception was its best option. The smarter AI gets, the better it becomes at faking obedience. And right now, scientists have no reliable way to stop it.

🚨 AI chatbots outfake the mainstream media A new study found that ChatGPT, Copilot, Gemini, and Perplexity are twisting facts and fabricating quotes, with a whopping 51% of answers flawed. Blunders include keeping Rishi Sunak and Nicola Sturgeon in office, erasing Lucy Letby’s murder convictions, and inventing false connections between crimes and memory loss. Even Apple had to suspend BBC alerts after AI-generated nonsense falsely declared Luigi Mangione dead.

📂 Full description More and more applications are using computer vision to detect and recognize objects. These applications usually execute on large computers, but developers can save money and power by running them on single-board computers (SBCs). The Raspberry Pi 4 is one of the most popular SBCs available. It's also the first computer in the Raspberry Pi family powerful enough to execute computer vision applications. Also, the software needed to build these applications can be downloaded freely from the Internet. In this course, instructor Matt Scarpino shows programmers how to write and execute computer vision applications on the Raspberry Pi 4. Matt introduces you to using the Thonny IDE, the OpenCV library, and NumPy array operations. He steps through object detection and neural networks, then explores convolutional neural networks (CNNs), including the Keras package and the TensorFlow package. Matt also walks you through what you can do with a Raspberry Pi HQ camera.

Advanced AI and Data Science Interview Questions 1. Explain the concept of Generative Adversarial Networks (GANs). How do they work, and what are some of their applications? 2. What is the Curse of Dimensionality? How does it affect machine learning models, and what techniques can be used to mitigate its impact? 3. Describe the process of hyperparameter tuning in deep learning. What are some strategies you can use to optimize hyperparameters? 4. How does a Transformer architecture differ from traditional RNNs and LSTMs? Why has it become so popular in natural language processing (NLP)? 5. What is the difference between L1 and L2 regularization, and in what scenarios would you prefer one over the other? 6. Explain the concept of transfer learning. How can pre-trained models be used in a new but related task? 7. Discuss the importance of explainability in AI models. How do methods like LIME or SHAP contribute to model interpretability? 8. What are the differences between Reinforcement Learning (RL) and Supervised Learning? Can you provide an example where RL would be more appropriate? 9. How do you handle imbalanced datasets in a classification problem? Discuss techniques like SMOTE, ADASYN, or cost-sensitive learning. 10. What is Bayesian Optimization, and how does it compare to grid search or random search for hyperparameter tuning? 11. Describe the steps involved in developing a recommendation system. What algorithms might you use, and how would you evaluate its performance? 12. Can you explain the concept of autoencoders? How are they used for tasks such as dimensionality reduction or anomaly detection? 13. What are adversarial examples in the context of machine learning models? How can they be used to fool models, and what can be done to defend against them? 14. Discuss the role of attention mechanisms in neural networks. How have they improved performance in tasks like machine translation? 15. What is a variational autoencoder (VAE)? How does it differ from a standard autoencoder, and what are its benefits in generating new data? Like if you need similar content 😄👍

📂 Full description This course is for anyone who wants to learn more advanced NLP methods. Instructor Gwendolyn Stripling, PhD, begins with a look at the fundamental concepts and principles of NLP, including the evolution and significance of natural language processing. She then reviews some NLP and Python basics—and introduces the NLP library spaCy—before jumping into more modern techniques and advancements in natural language processing using Transformer Models like GPT and BERT. Methods such as supervised fine-tuning, parameter efficient fine-tuning (PEFT), and retrieval-augmented generation (RAG) give you the foundational knowledge you need to improve large language model (LLM) performance. Learn the ways you can apply NLP in your applications and day-to-day, including how to analyze customer sentiments Each chapter ends with a challenge and solution, so you can test your knowledge as you go.