AI and Machine Learning

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🌟 Pocket Flow: A Minimalistic LLM Framework in 100 Lines of Code Popular frameworks turn simple tasks into a quest to decipher someone else’s code. Endless wrappers, version conflicts, outdated documentation… All this is not just annoying, it slows down development. After a year of struggling with overloaded tools like LangChain, Microsoft Research developer Zachary Huang dedicated his free time to creating Pocket Flow , a framework that fits all the magic of LLM into 100 lines of code. Pocket Flow offers a radically different approach: minimalism. It is based on the idea that any LLM pipeline can be represented as a graph of nodes and transitions. No hidden layers, just logic and transparency. To understand how Pocket Flow works, imagine a kitchen where each node is a cooking zone. BaseNode performs three steps: preparation (collect data), execution (process the request), postprocessing (save the result). Flow manages the "recipe": decides where to pass control next. All interactions occur through a common data store - like a table on which the ingredients for all the cooks are located. An example? Let's say you're building a search agent. You create nodes: DecideAction (decides whether to search), SearchWeb (searches the web), AnswerQuestion (generates an answer). You link them into a graph, where the decision of one node determines the next step. If the model doesn't know the answer, then the search is launched, the results are added to the context, and the cycle repeats. All this is a couple hundred lines of code on top of the Pocket Flow core. The main advantage of Pocket Flow is freedom. There is no binding to specific APIs, connect any models, even local ones. No dependencies: your project remains "lightweight", and interfaces do not break after updates. Do you want query caching or stream processing? Implement it yourself, without fighting with other people's abstractions. Of course, minimalism has a price: you won’t get ready-made solutions for every task. But this is the power of Pocket Flow. It gives you control and insight into the process, rather than a ready-made, but black box. If you are tired of monster frameworks and want to start from scratch, check out the Pocket Flow repository . There are examples of agents, RAG systems, and multi-agent scenarios. 📌 Licensing: MIT License. 🟡 Article 🟡 Documentation 🟡 Community on Discord 🖥 GitHub

🔗 AI Vs Machine Learning Vs Deep Learning Vs Generative AI 1 - Artificial Intelligence (AI) It is the overarching field focused on creating machines or systems that can perform tasks typically requiring human intelligence, such as reasoning, learning, problem-solving, and language understanding. AI consists of various subfields, including ML, NLP, Robotics, and Computer Vision 2 - Machine Learning (ML) It is a subset of AI that focuses on developing algorithms that enable computers to learn from and make decisions based on data. Instead of being explicitly programmed for every task, ML systems improve their performance as they are exposed to more data. Common applications include spam detection, recommendation systems, and predictive analytics. 3 - Deep Learning It is a specialized subset of ML that utilizes artificial neural networks with multiple layers to model complex patterns in data. Neural networks are computational models inspired by the human brain’s network of neurons. Deep neural networks can automatically discover representations needed for future detection. Use cases include image and speech recognition, NLP, and autonomous vehicles. 4 - Generative AI It refers to AI systems capable of generating new content, such as text, images, music, or code, that resembles the data they were trained on. They rely on the Transformer Architecture. Notable generative AI models include GPT for text generation and DALL-E for image creation.

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📂 Full description In this course, MLOps expert Noah Gift covers small language models, their advantages, and how to run them locally using the llamafile tool. Plus, get useful demos of the Phi llamafile and the Lava llamafile. This course was created by Noah Gift. We are pleased to host this training in our library.

🔅 Small Language Models and LlamaFile 🌐 Author: Noah Gift 🔰 Level: Intermediate ⏰ Duration: 11m

🌀 Explore small language models, their advantages, and how to run them locally.

📗 Topics: LLaMA, Large Language Models, Natural Language Processing 📤 Join Artificial intelligence for more courses

Two to three years until "AI systems are better than humans at almost everything... then eventually better than all humans at everything," says Anthropic CEO.

🔰 Create a Pencil Sketch Filter in Python ✏️ A quick guide to image processing with OpenCV (CV2). The Pipeline: Original Image → Grayscale → Inverted Image → Blurred Invert → Final Sketch

By blending the grayscale and blurred invert layers, we simulate the effect of a hand-drawn sketch. A simple yet powerful technique!

Ideal for beginners looking to dive into computer vision.

# Importing the Required Moduel
# pip install opencv-python
import cv2 as cv

# Reading the image
# Replace this image name to your image name
image = cv.imread("avatar.jpg")

# Converting the Image into gray_image
gray_image = cv.cvtColor(image, cv.COLOR_BGR2GRAY)

# Inverting the Imge
invert_image = cv.bitwise_not(gray_image)

# Blur Image
blur_image = cv.GaussianBlur(invert_image, (21,21), 0)

# Inverting the Blured Image
invert_blur = cv.bitwise_not(blur_image)

# Convert Image Into sketch
sketch = cv.divide(gray_image, invert_blur, scale=256.0)

# Generating the Sketch Image Named as Sketch.png
cv.imwrite("Sketch.png", sketch)

#Python #OpenCV #ComputerVision #Coding #AI

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💠 The Best Tool for Extracting Data from PDF Files! 👩🏻‍💻 Usually, PDF files like financial reports, scientific articles, or data analyses are full of tables, formulas, and complex texts. ⬅️ Most tools only extract texts and destroy the data structure, causing important information to be lost. ✅ But the tool Docling uses artificial intelligence to preserve all those structures (text, tables, formulas) exactly as they are in the file. Then it converts that data into a structured format. Meaning AI models can work on them. ⭕ The interesting point is that with just three lines of Python code, you can convert any PDF into searchable data! ┌ 🥵 Docling ├ 🔎 Article ├ 📄 Documentation └ 🐱 GitHub-Repos

🧠 Theirwork = AI helps to find your freelancer in seconds Tired of spending 3–10 hours picking freelancers — and 2 out of 3 don’t really fit? Or don’t even know where to start looking? We built Theirwork:  - A fully free, AI-first hiring assistant (not a job board) - No “HR” obstacles, no endless chats or vague profiles - AI does 95% of the heavy lifting: brief analysis, skill mapping, ranking 🔬 Benchmark: Manual search = 5–15 hours + 40% mismatch Theirwork = 2 minutes ⚡ 80–90% accuracy, totally automatic We are here to change the norms in freelance sourcing — and need your feedback to make it better. 👉 Give it a go (no signups, no fees): https://ubZirZ.short.gy/qm9h3B

Google published a 150-page report on Health AI Agents - 7,000 annotations, 1,100+ hours of expert work. But the main thing is not the metrics, but the new design philosophy. Instead of a monolithic *"Doctor-GPT"*, Google is creating a Personal Health Agent (PHA) - a system of three specialized agents: - Data Science Agent - analyzes wearable devices and lab data - Domain Expert Agent - verifies medical facts and knowledge - Health Coach Agent - conducts dialogue, sets goals, adds empathy 🧩 Everything is connected by an orchestrator with memory: user goals, barriers, insights. ⚡️ Results - Outperformed baseline models on 10 benchmarks - Users preferred PHA over regular LLMs (20 participants, 50 personas) - Experts rated answers 5.7–39% better on complex medical queries ⚙️ Design principles - Consider all user needs - Adaptively combine agents - Do not ask for data that can be inferred - Minimize latency and complexity 🧠 Tested scenarios - General health questions - Data interpretation (wearables, biomarkers) - Advice on sleep, nutrition, activity - Symptom assessment (without diagnosis) ⚠️ Limitations and future - Slower than single agents (244 s vs. 36 s) - Need bias audits, data protection, and regulatory compliance - Next step - adaptive communication style: empathy ↔️ responsibility 💡 Conclusion Google shows the way forward: not a "super doctor bot," but modular, specialized agent teams. Medicine is just the first test. Next: finance, law, education, science. Google 150 Health AI Agents: https://arxiv.org/pdf/2508.20148

📂 Full description In this course, Axel Sirota introduces Retrieval-Augmented Generation (RAG) as a powerful technique for enhancing the capabilities of Large Language Models (LLMs). Learn the foundational concepts and practical applications of RAG, focusing on creating chatbots and decision support systems across various domains. Using the MIMIC-III dataset to create a healthcare chatbot that can answer questions or suggest a diagnosis as an example, get hands-on experience in building RAG systems with TensorFlow, Keras, and HuggingFace. By the end of the course, you will be equipped to deploy RAG solutions that integrate robust retrieval mechanisms with generative models, applicable in fields like healthcare, legal, and customer service.

🔅 Building a RAG Solution from Scratch 🌐 Author: Axel Sirota 🔰 Level: Intermediate ⏰ Duration: 2h 53m

🌀 Learn to design, implement, and optimize RAG systems for chatbots and decision support, while exploring current research and ethical considerations.

📗 Topics: Retrieval-Augmented Generation, Generative AI, Artificial Intelligence 📤 Join Artificial intelligence for more courses

📺 12 comprehensive playlists to master ⬅️ machine learning, deep learning, and GenAI!

👨🏻‍💻 Each playlist is designed to be simple and understandable for beginners, and then gradually dive deeper into the topics.

😉 Machine Learning Basics (39 videos) 😉 Python for ML (9 videos) 😉 Optimization for ML (5 videos) 😉 Machine Learning with Practical Exercises (37 videos) 😉 Building Decision Trees from Scratch (13 videos) 😉 Building Neural Networks from Scratch (35 videos) 😉 Graph Neural Networks (6 videos) 😉 Computer Vision from Scratch (19 videos) 😉 Building LLM from Scratch (43 videos) 😉 Reasoning in LLMs from Scratch (22 videos) 😉 Building DeepSeek from Scratch (29 videos) 😉 Machine Learning in Production Environment (6 videos)

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