🔗 Machine Learning from Scratch by Danny Friedman

This book is for readers looking to learn new machine learning algorithms or understand algorithms at a deeper level. Specifically, it is intended for readers interested in seeing machine learning algorithms derived from start to finish. Seeing these derivations might help a reader previously unfamiliar with common algorithms understand how they work intuitively. Or, seeing these derivations might help a reader experienced in modeling understand how different algorithms create the models they do and the advantages and disadvantages of each one.

This book will be most helpful for those with practice in basic modeling. It does not review best practices—such as feature engineering or balancing response variables—or discuss in depth when certain models are more appropriate than others. Instead, it focuses on the elements of those models.

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🖥 How to Install Deep Seek Locally Using Ollama LLM on Ubuntu 24.04

A detailed tutorial from TecMint demonstrating how to install and run the DeepSeek model locally on Linux (Ubuntu 24.04) using Ollama.

The guide covers all installation steps: updating the system, installing Python and Git, configuring Ollama to control DeepSeek, and running the model via the command line or using a convenient Web UI.

▪️ The guide also includes instructions for automatically launching the Web UI at system startup via systemd, which makes working with the model more comfortable and accessible.

Suitable for those who want to explore the possibilities of working with large language models without being tied to cloud services, providing full control over the model and its settings.

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🔗 01. Small Language Models - An Emerging Technique in AI

Unlike large language models, which rely on vast amounts of data, small language models focus on high-quality, curated training datasets. This approach allows them to potentially outperform larger models in specific tasks, especially when specialized training is applied.

💡 Key Advantages of Small Language Models:

1. Compact Size: Small language models are significantly smaller in size compared to their larger counterparts. This compactness makes inference (the process of making predictions) much easier and more efficient, as they do not require large GPUs or extensive computational resources.

2. Efficient Training: Training small language models is more efficient because they do not need to process "essentially unlimited" data. This reduces the computational resources required for both training and inference.

3. Easier Deployment: One of the most promising aspects of small language models is their potential for deployment on edge devices. While this capability is still emerging, the instructor predicts that we will soon see small language models customized for specific hardware, such as drones, phones, or other devices. This would enable these models to perform specialized tasks directly on the device, without the need for cloud-based processing.

4. Specialization: Small language models can be tailored for specific tasks, potentially outperforming larger models in those areas. This makes them highly suitable for applications where task-specific performance is more critical than general-purpose capabilities.

💡 Future Prospects:

The video highlights that small language models are likely to play a significant role in the future of edge-based computing. As hardware capable of supporting machine learning models becomes more prevalent, small language models could be integrated into a wide range of devices, enabling real-time, on-device AI capabilities.

💡 Conclusion:

Small language models represent a promising area of research in AI, offering several advantages over large language models, including efficiency, ease of deployment, and the potential for task-specific optimization. As the technology evolves, we can expect to see these models increasingly used in edge devices, driving innovation in specialized AI applications. Understanding the benefits and potential of small language models is essential for anyone interested in the future of AI and machine learning.

🔗 02. Demo of Phi - A Small Language Model by Microsoft Research

Phi is a small language model developed by Microsoft Research, as one of the most notable examples of small language models created recently. Phi is designed with 2.7 billion parameters, making it significantly smaller than many large language models, yet it demonstrates impressive performance due to its focus on high-quality, textbook-level training data.

💡 Key Features of Phi:

1. High-Quality Data: Unlike large language models that rely on vast amounts of data, Phi is trained on curated, high-quality datasets. This focus on quality over quantity allows Phi to achieve superior performance in tasks such as reasoning, language understanding, and mathematical problem-solving.

2. Performance Benchmarks: Despite its smaller size, Phi outperforms larger models like Llama 2 in specific areas. For example, it achieves more than three times better performance in mathematical tasks and nearly double the coding performance compared to other small models. This demonstrates that small language models can compete with or even surpass larger models in specialized tasks.

3. Efficiency and Speed: One of the key advantages of Phi is its compact size (only 1.96 gigabytes), which makes it easy to run on standard hardware. The presenter demonstrates how Phi can be quickly executed using tools like cURL or Python, and it provides fast responses to queries, such as calculating the square root of 16 or generating equations for linear optimization.

4. Specialization: Phi's ability to excel in specific tasks, such as math and coding, highlights the potential of specialized small language models. The presenter suggests that this could be a future trend, where small models are tailored for particular applications, allowing them to run efficiently on smaller devices and in smaller form factors.

💡 Running Phi:

The video provides a practical demonstration of how to run Phi using the Mozilla Llama file. The process is straightforward, requiring only a simple command to execute the model. The presenter shows how Phi can quickly respond to prompts, showcasing its speed and accuracy in real-time.

💡Future Implications:

The presenter emphasizes that Phi represents a promising direction in AI development. By focusing on specialized, high-quality training data, small language models like Phi can achieve surprisingly good performance while being more efficient and easier to deploy. This could lead to a future where small language models are increasingly used in edge devices and other resource-constrained environments.

💡 Conclusion:

Phi is a canonical example of how small language models can leverage high-quality data and specialized training to outperform larger models in specific tasks. Its compact size, efficiency, and speed make it a powerful tool for applications requiring real-time, on-device AI capabilities. As the field evolves, we can expect to see more small language models like Phi being developed for specialized tasks, driving innovation in AI and machine learning.