Artificial Intelligence

💡 Machine Learning Projects

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📂 Full description Supercharge your software development with the features available in Cursor. This course teaches you to install and set up Cursor, how to refactor code efficiently with AI features, introduce additional context to the AI, compose new projects from scratch, and even how to generate code from images. With AI-powered features, youll write, optimize, and build applications with unprecedented speed and accuracy, making your coding workflow more effective than ever.

🔅 Accelerate Development with Artificial Intelligence and Cursor 🌐 Author: Ray Villalobos 🔰 Level: General ⏰ Duration: 29m

🌀 Discover how Cursor can transform your coding workflow with AI-assisted development using chat. Learn to compose, refactor, and build software faster and more efficiently than ever.

📗 Topics: AI Software Development, Generative AI, Integrated Development Environments 📤 Join Artificial Intelligence and Machine Learning for more courses

Generative vs. discriminative models in ML: Generative models: - learn the distribution so they can generate new samples. - possess discriminative properties, we can use them for classification. Discriminative models don't have generative properties.

🔰 Understanding Probability Distributions for Machine Learning with Python In machine learning, probability distributions play a fundamental role for various reasons: modeling uncertainty of information and #data, applying optimization processes with stochastic settings, and performing inference processes, to name a few. Therefore, understanding the role and uses of probability distributions in machine learning is essential for designing robust machine learning models, choosing the right #algorithms, and interpreting outputs of a probabilistic nature, especially when building #models with #machinelearning-friendly programming languages like #Python. This article unveils key #probability distributions relevant to machine learning, explores their applications in different machine learning tasks, and provides practical Python implementations to help practitioners apply these concepts effectively. A basic knowledge of the most common probability distributions is recommended to make the most of this reading. 🔗 Read Free: https://machinelearningmastery.com/understanding-probability-distributions-machine-learning-python/

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🌟 MatAnyone: A model for detecting people in videos using masks. MatAnyOne is a memory-based model for video matting designed to produce stable and accurate results in real-world post-production scenarios. Unlike methods that require additional annotation, MatAnyOne uses only video frames and a target object segmentation mask defined on the first frame. MatAnyOne employs region-adaptive memory fusion, where regions with small changes retain data from the previous frame, while regions with large changes rely more on information from the current frame. This technique allows MatAnyOne to efficiently track a target object, even in complex and ambiguous scenes, while preserving sharp edges and intact foreground parts. The model was created using a unique training strategy that relies on segmentation data to improve the stability of object extraction. Unlike common practices, MatAnyOne uses this data directly in the same branch as the mask data. This is achieved by applying region-specific losses: a pixel-wise loss for core regions and an improved DDC loss for boundary regions. For training, a custom dataset VM800 was specially created, which is twice as large, more diverse and better quality than VideoMatte240K, which ultimately significantly improved the reliability of training object selection on video. In tests, MatAnyOne showed high results compared to existing methods on both synthetic and real videos: 🟠 On VideoMatte and YouTubeMatte, MatAnyOne has the best results in MAD (mean absolute difference) and dtSSD (shape transform distance); 🟢 In the real-world video benchmark, MatAnyOne achieved MAD 0.18, MSE 0.11, and dtSSD 0.95, which is significantly better than RVM10 (MAD 1.21, MSE 0.77, dtSSD 1.43) and MaGGIe12 (MAD 1.94, MSE 1.53, dtSSD 1.63). ⚠️ According to the discussion in the repository issues , MatAnyOne is capable of working locally from 4 GB VRAM and higher with short-duration videos. The developer has not published any real technical criteria. ▶️ Local installation and launch of web-demo on Gradio:

 # Clone Repo
git clone https://github.com/pq-yang/MatAnyone
cd MatAnyone

# Create Conda env and install dependencies
conda create -n matanyone python=3.8 -y
conda activate matanyone

pip install -e .

# Install python dependencies for gradio
pip3 install -r hugging_face/requirements.txt

# Launch the demo
python app.py

🟡 Project page 🟡 Model 🟡 Arxiv 🟡 Demo 🖥 GitHub

🧠 Different types of Machine Learning

📂 Full description AI is driving innovation and efficiency in the tech industry. As businesses and organizations seek to leverage AI, there's a high demand for skilled professionals who can understand, develop, and ethically implement AI technologies. In this course, award-winning tech innovator and AI/ML leader Kesha Williams helps developers to upskill and merge their existing programming knowledge with AI competencies. Learn about the concept of artificial intelligence and how it revolutionizes traditional programming methodologies. Explore the tools you need to interpret, evaluate, and harness AI technologies effectively. Through Python code examples, get an introduction to the fundamental pillars of AI, including machine learning, neural networks, and computer vision, while addressing ethical considerations for responsible development. By the end of the course, you will be ready to tackle the technological challenges of today and tomorrow with confidence and creativity.

🔅 Programming Foundations: Artificial Intelligence 🌐 Author: Kesha Williams 🔰 Level: Beginner ⏰ Duration: 1h 15m

🌀 Explore AI fundamentals, ethical implications, and practical skills, to ensure you remain at the forefront of technological innovation and ethical responsibility.

📗 Topics: Programming, AI Software Development, Artificial Intelligence 📤 Join Artificial Intelligence and Machine Learning for more courses

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💡 Your First Step is Simpler Than You Think

If you're an absolute beginner, don't jump straight into building a neural network. The most successful journeys are built on a steady progression.

1. Start with introductory Python. 2. Build your confidence. 3. Then, and only then, move into data science, machine learning, and AI. Your path will be unique. Your "why" is your compass, and these courses can be your map. The rest is up to you. So, what's your why? Once you have it, take that first step. The world of AI is waiting for you.

💡 Got Your "Why"? Time for the "How."

Alright, you’ve got your motivation locked in. Now we can talk about the hard skills. A word of caution: the landscape of online courses is vast and a new "game-changing" program launches every week. It's impossible to declare one single "best" course.

I can only recommend what has worked for me. As a visual learner who needs to see concepts in action, the following resources were world-class for my style. I recommend this progression: A Simple Learning Path to Get You Started: 1⃣ The Foundation: Learn Python. You can’t build a house without a foundation. Start with an introduction to Python programming. It’s the lingua franca of AI and ML. - Where to go: Treehouse or the vast, free tutorials on YouTube. 🔢 The Core Concepts: Dive into ML & AI. Once you're comfortable with Python, it's time to dive in. I combined a structured university-style approach with a practical, code-first method. - Udacity: Their Deep Learning & AI Nanodegree provides a fantastic, well-structured overview of the field. - fast.ai: For a more practical, "top-down" approach where you code first and understand the theory later, Practical Deep Learning for Coders (Part 1 & Part 2) is incredible and free.