🔗 Basics of Machine Learning 👇👇

Machine learning is a branch of artificial intelligence where computers learn from data to make decisions without explicit programming. There are three main types:

1. Supervised Learning: The algorithm is trained on a labeled datasets, learning to map input to output. For example, it can predict housing prices based on features like size and location.

2. Unsupervised Learning: The algorithm explores data patterns without explicit labels. Clustering is a common task, grouping similar data points. An example is customer segmentation for targeted marketing.

3. Reinforcement Learning: The algorithm learns by interacting with an environment. It receives feedback in the form of rewards or penalties, improving its actions over time. Gaming AI and robotic control are applications.

📖 Key concepts include:

- Features and Labels: Features are input variables, and labels are the desired output. The model learns to map features to labels during training.

- Training and Testing: The model is trained on a subset of data and then tested on unseen data to evaluate its performance.

- Overfitting and Underfitting: Overfitting occurs when a model is too complex and fits the training data too closely, performing poorly on new data. Underfitting happens when the model is too simple and fails to capture the underlying patterns.

- Algorithms: Different algorithms suit various tasks. Common ones include linear regression for predicting numerical values, and decision trees for classification tasks.

In summary, machine learning involves training models on data to make predictions or decisions. Supervised learning uses labeled data, unsupervised learning finds patterns in unlabeled data, and reinforcement learning learns through interaction with an environment. Key considerations include features, labels, overfitting, underfitting, and choosing the right algorithm for the task.

🔗 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.

🔗 Link

🖥 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.

▪️ Read

🔗 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.