#Supervised machine learning

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This came to me in a vision

[govindhtech]

Define machine learning: 5 machine learning types to know

Machine learning (ML) can be used in computer vision, large language models (LLMs), speech recognition, self-driving cars, and many more use cases to make decisions in healthcare, human resources, finance, and other areas.

However, ML’s rise is complicated. ML validation and training datasets are generally aggregated by humans, who are biased and error-prone. Even if an ML model isn’t biased or erroneous, using it incorrectly can cause harm.

Diversifying enterprise AI and ML usage can help preserve a competitive edge. Distinct ML algorithms have distinct benefits and capabilities that teams can use for different jobs. IBM will cover the five main categories and their uses.

Define machine learning

ML is a computer science, data science, and AI subset that lets computers learn and improve from data without programming.

ML models optimize performance utilizing algorithms and statistical models that deploy jobs based on data patterns and inferences. Thus, ML predicts an output using input data and updates outputs as new data becomes available.

Machine learning algorithms recommend products based on purchasing history on retail websites. IBM, Amazon, Google, Meta, and Netflix use ANNs to make tailored suggestions on their e-commerce platforms. Retailers utilize chat bots, virtual assistants, ML, and NLP to automate shopping experiences.

Machine learning types

Supervised, unsupervised, semi-supervised, self-supervised, and reinforcement machine learning algorithms exist.

1.Supervised machine learning

Supervised machine learning trains the model on a labeled dataset with the target or outcome variable known. Data scientists constructing a tornado predicting model might enter date, location, temperature, wind flow patterns, and more, and the output would be the actual tornado activity for those days.

Several algorithms are employed in supervised learning for risk assessment, image identification, predictive analytics, and fraud detection.

Regression algorithms predict output values by discovering linear correlations between actual or continuous quantities (e.g., income, temperature). Regression methods include linear regression, random forest, gradient boosting, and others.

Labeling input data allows classification algorithms to predict categorical output variables (e.g., “junk” or “not junk”). Logistic regression, k-nearest neighbors, and SVMs are classification algorithms.

Naïve Bayes classifiers enable huge dataset classification. They’re part of generative learning algorithms that model class or category input distribution. Decision trees in Naïve Bayes algorithms support regression and classification techniques.

Neural networks, with many linked processing nodes, replicate the human brain and can do natural language translation, picture recognition, speech recognition, and image generation.

Random forest methods combine decision tree results to predict a value or category.

2. Unsupervised machine learning

Apriori, Gaussian Mixture Models (GMMs), and principal component analysis (PCA) use unlabeled datasets to make inferences, enabling exploratory data analysis, pattern detection, and predictive modeling.

Cluster analysis is the most frequent unsupervised learning method, which groups data points by value similarity for customer segmentation and anomaly detection. Association algorithms help data scientists visualize and reduce dimensionality by identifying associations between data objects in huge databases.

K-means clustering organizes data points by size and granularity, clustering those closest to a centroid under the same category. Market, document, picture, and compression segmentation use K-means clustering.

Hierarchical clustering includes agglomerative clustering, where data points are isolated into groups and then merged iteratively based on similarity until one cluster remains, and divisive clustering, where a single data cluster is divided by data point differences.

Probabilistic clustering group’s data points by distribution likelihood to tackle density estimation or “soft” clustering problems.

Often, unsupervised ML models power “customers who bought this also bought…” recommendation systems.

3. Self-supervised machine learning

Self-supervised learning (SSL) lets models train on unlabeled data instead of enormous annotated and labeled datasets. SSL algorithms, also known as predictive or pretext learning algorithms automatically classify and solve unsupervised problems by learning one portion of the input from another. Computer vision and NLP require enormous amounts of labeled training data to train models, making these methods usable.

4. Reinforcement learning

Dynamic programming dubbed reinforcement learning from human feedback (RLHF) trains algorithms using reward and punishment. To use reinforcement learning, an agent acts in a given environment to achieve a goal. The agent is rewarded or penalized based on a measure (usually points) to encourage good behavior and discourage negative behavior. Repetition teaches the agent the optimum methods.

Video games often use reinforcement learning techniques to teach robots human tasks.

5. Semi-supervised learning

The fifth machine learning method combines supervised and unsupervised learning.

Semi-supervised learning algorithms learn from a small labeled dataset and a large unlabeled dataset because the labeled data guides the learning process. A semi-supervised learning algorithm may find data clusters using unsupervised learning and label them using supervised learning.

Semi-supervised machine learning uses generative adversarial networks (GANs) to produce unlabeled data by training two neural networks.

ML models can gain insights from company data, but their vulnerability to human/data bias makes ethical AI practices essential.

Manage multiple ML models with watstonx.ai.

Whether they employ AI or not, most people use machine learning, from developers to users to regulators. Adoption of ML technology is rising. Global machine learning market was USD 19 billion in 2022 and is predicted to reach USD 188 billion by 2030 (a CAGR of almost 37%).

The size of ML usage and its expanding business effect make understanding AI and ML technologies a key commitment that requires continuous monitoring and appropriate adjustments as technologies improve. IBM Watsonx.AI Studio simplifies ML algorithm and process management for developers.

IBM Watsonx.ai, part of the IBM Watsonx AI and data platform, leverages generative AI and a modern business studio to train, validate, tune, and deploy AI models faster and with less data. Advanced data production and classification features from Watsonx.ai enable enterprises optimize real-world AI performance with data insights.

In the age of data explosion, AI and machine learning are essential to corporate operations, tech innovation, and competition. However, as new pillars of modern society, they offer an opportunity to diversify company IT infrastructures and create technologies that help enterprises and their customers.

Read more on Govindhtech.com

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Meta AI Releases the Video Joint Embedding Predictive Architecture (V-JEPA) Model: A Crucial Step in Advancing Machine Intelligence - MarkTechPost

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Dal Pre-training all'Expert Iteration: Il Percorso verso la Riproduzione di OpenAI Five

Il Reinforcement Learning (RL) rappresenta un approccio distintivo nel panorama del machine learning, basato sull’interazione continua tra un agente e il suo ambiente. In RL, l’agente apprende attraverso un ciclo di azioni e ricompense, con l’obiettivo di massimizzare il guadagno cumulativo a lungo termine. Questa strategia lo differenzia dagli approcci tradizionali come l’apprendimento…

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How to Choose the Right Machine Learning Solutions

Machine learning (ML) is a key technology today. It turns large amounts of data into useful insights and predictions. It is used for personalizing marketing, driving autonomous vehicles and much more. Well, picking the right machine learning solution can be challenging. This guide will simplify the process by explaining the basics and offering practical steps to help you choose the right machine learning solutions.

[rjas16]

Discover Self-Supervised Learning for LLMs

Artificial intelligence is transforming the world at an unprecedented pace, and at the heart of this revolution lies a powerful learning technique: self-supervised learning. Unlike traditional methods that demand painstaking human effort to label data, self-supervised learning flips the script, allowing AI models to teach themselves from the vast oceans of unlabeled data that exist today. This method has rapidly emerged as the cornerstone for training Large Language Models (LLMs), powering applications from virtual assistants to creative content generation. It drives a fundamental shift in our thinking about AI's societal role.

Self-supervised learning propels LLMs to new heights by enabling them to learn directly from the data—no external guidance is needed. It's a simple yet profoundly effective concept: train a model to predict missing parts of the data, like guessing the next word in a sentence. But beneath this simplicity lies immense potential. This process enables AI to capture the depth and complexity of human language, grasp the context, understand the meaning, and even accumulate world knowledge. Today, this capability underpins everything from chatbots that respond in real time to personalized learning tools that adapt to users' needs.

This approach's advantages go far beyond just efficiency. By tapping into a virtually limitless supply of data, self-supervised learning allows LLMs to scale massively, processing billions of parameters and honing their ability to understand and generate human-like text. It democratizes access to AI, making it cheaper and more flexible and pushing the boundaries of what these models can achieve. And with the advent of even more sophisticated strategies like autonomous learning, where models continually refine their understanding without external input, the potential applications are limitless. We will try to understand how self-supervised learning works, its benefits for LLMs, and the profound impact it is already having on AI applications today. From boosting language comprehension to cutting costs and making AI more accessible, the advantages are clear and they're just the beginning. As we stand on the brink of further advancements, self-supervised learning is set to redefine the landscape of artificial intelligence, making it more capable, adaptive, and intelligent than ever before.

Understanding Self-Supervised Learning

Self-supervised learning is a groundbreaking approach that has redefined how large language models (LLMs) are trained, going beyond the boundaries of AI. We are trying to understand what self-supervised learning entails, how it differs from other learning methods, and why it has become the preferred choice for training LLMs.

Definition and Differentiation

At its core, self-supervised learning is a machine learning paradigm where models learn from raw, unlabeled data by generating their labels. Unlike supervised learning, which relies on human-labeled data, or unsupervised learning, which searches for hidden patterns in data without guidance, self-supervised learning creates supervisory signals from the data.

For example, a self-supervised learning model might take a sentence like "The cat sat on the mat" and mask out the word "mat." The model's task is to predict the missing word based on the context provided by the rest of the sentence. This way, we can get the model to learn the rules of grammar, syntax, and context without requiring explicit annotations from humans.

Core Mechanism: Next-Token Prediction

A fundamental aspect of self-supervised learning for LLMs is next-token prediction, a task in which the model anticipates the next word based on the preceding words. While this may sound simple, it is remarkably effective in teaching a model about the complexities of human language.

Here's why next-token prediction is so powerful:

Grammar and Syntax

To predict the next word accurately, the model must learn the rules that govern sentence structure. For example, after seeing different types of sentences, the model understands that "The cat" is likely to be followed by a verb like "sat" or "ran."

Semantics

The model is trained to understand the meanings of words and their relationships with each other. For example, if you want to say, "The cat chased the mouse," the model might predict "mouse" because it understands the words "cat" and "chased" are often used with "mouse."

Context

Effective prediction requires understanding the broader context. In a sentence like "In the winter, the cat sat on the," the model might predict "rug" or "sofa" instead of "grass" or "beach," recognizing that "winter" suggests an indoor setting.

World Knowledge

Over time, as the model processes vast amounts of text, it accumulates knowledge about the world, making more informed predictions based on real-world facts and relationships. This simple yet powerful task forms the basis of most modern LLMs, such as GPT-3 and GPT-4, allowing them to generate human-like text, understand context, and perform various language-related tasks with high proficiency .

The Transformer Architecture

Self-supervised learning for LLMs relies heavily on theTransformer architecture, a neural network design introduced in 2017 that has since become the foundation for most state-of-the-art language models. The Transformer Architecture is great for processing sequential data, like text, because it employs a mechanism known as attention. Here's how it works:

Attention Mechanism

Instead of processing text sequentially, like traditional recurrent neural networks (RNNs), Transformers use an attention mechanism to weigh the importance of each word in a sentence relative to every other word. The model can focus on the most relevant aspects of the text, even if they are far apart. For example, in the sentence "The cat that chased the mouse is on the mat," the model can pay attention to both "cat" and "chased" while predicting the next word.

Parallel Processing

Unlike RNNs, which process words one at a time, Transformers can analyze entire sentences in parallel. This makes them much faster and more efficient, especially when dealing large datasets. This efficiency is critical when training on datasets containing billions of words.

Scalability

The Transformer's ability to handle vast amounts of data and scale to billions of parameters makes it ideal for training LLMs. As models get larger and more complex, the attention mechanism ensures they can still capture intricate patterns and relationships in the data.

By leveraging the Transformer architecture, LLMs trained with self-supervised learning can learn from context-rich datasets with unparalleled efficiency, making them highly effective at understanding and generating language.

Why Self-Supervised Learning?

The appeal of self-supervised learning lies in its ability to harness vast amounts of unlabeled text data. Here are some reasons why this method is particularly effective for LLMs:

Utilization of Unlabeled Data

Self-supervised learning uses massive amounts of freely available text data, such as web pages, books, articles, and social media posts. This approach eliminates costly and time-consuming human annotation, allowing for more scalable and cost-effective model training.

Learning from Context

Because the model learns by predicting masked parts of the data, it naturally develops an understanding of context, which is crucial for generating coherent and relevant text. This makes LLMs trained with self-supervised learning well-suited for tasks like translation, summarization, and content generation.

Self-supervised learning enables models to continuously improve as they process more data, refining their understanding and capabilities. This dynamic adaptability is a significant advantage over traditional models, which often require retraining from scratch to handle new tasks or data.

In summary, self-supervised learning has become a game-changing approach for training LLMs, offering a powerful way to develop sophisticated models that understand and generate human language. By leveraging the Transformer architecture and utilizing vast amounts of unlabeled data, this method equips LLMs that can perform a lot of tasks with remarkable proficiency, setting the stage for future even more advanced AI applications.

Key Benefits of Self-Supervised Learning for LLMs

Self-supervised learning has fundamentally reshaped the landscape of AI, particularly in training large language models (LLMs). Concretely, what are the primary benefits of this approach, which is to enhance LLMs' capabilities and performance?

Leverage of Massive Unlabeled Data

One of the most transformative aspects of self-supervised learning is its ability to utilize vast amounts of unlabeled data. Traditional machine learning methods rely on manually labeled datasets, which are expensive and time-consuming. In contrast, self-supervised learning enables LLMs to learn from the enormous quantities of online text—web pages, books, articles, social media, and more.

By tapping into these diverse sources, LLMs can learn language structures, grammar, and context on an unprecedented scale. This capability is particularly beneficial because: Self-supervised learning draws from varied textual sources, encompassing multiple languages, dialects, topics, and styles. This diversity allows LLMs to develop a richer, more nuanced understanding of language and context, which would be impossible with smaller, hand-labeled datasets. The self-supervised learning paradigm scales effortlessly to massive datasets containing billions or even trillions of words. This scale allows LLMs to build a comprehensive knowledge base, learning everything from common phrases to rare idioms, technical jargon, and even emerging slang without manual annotation.

Improved Language Understanding

Self-supervised learning significantly enhances an LLM's ability to understand and generate human-like text. LLMs trained with self-supervised learning can develop a deep understanding of language structures, semantics, and context by predicting the next word or token in a sequence.

Deeper Grasp of Grammar and Syntax

LLMs implicitly learn grammar rules and syntactic structures through repetitive exposure to language patterns. This capability allows them to construct sentences that are not only grammatically correct but also contextually appropriate.

Contextual Awareness

Self-supervised learning teaches LLMs to consider the broader context of a passage. When predicting a word in a sentence, the model doesnt just look at the immediately preceding words but considers th'e entire sentence or even the paragraph. This context awareness is crucial for generating coherent and contextually relevant text.

Learning World Knowledge

LLMs process massive datasets and accumulate factual knowledge about the world. This helps them make informed predictions, generate accurate content, and even engage in reasoning tasks, making them more reliable for applications like customer support, content creation, and more.

Scalability and Cost-Effectiveness

The cost-effectiveness of self-supervised learning is another major benefit. Traditional supervised learning requires vast amounts of labeled data, which can be expensive. In contrast, self-supervised learning bypasses the need for labeled data by using naturally occurring structures within the data itself.

Self-supervised learning dramatically cuts costs by eliminating the reliance on human-annotated datasets, making it feasible to train very large models. This approach democratizes access to AI by lowering the barriers to entry for researchers, developers, and companies. Because self-supervised learning scales efficiently across large datasets, LLMs trained with this method can handle billions or trillions of parameters. This capability makes them suitable for various applications, from simple language tasks to complex decision-making processes.

Autonomous Learning and Continuous Improvement

Recent advancements in self-supervised learning have introduced the concept of Autonomous Learning, where LLMs learn in a loop, similar to how humans continuously learn and refine their understanding.

In autonomous learning, LLMs first go through an "open-book" learning phase, absorbing information from vast datasets. Next, they engage in "closed-book" learning, recalling and reinforcing their understanding without referring to external sources. This iterative process helps the model optimize its understanding, improve performance, and adapt to new tasks over time. Autonomous learning allows LLMs to identify gaps in their knowledge and focus on filling them without human intervention. This self-directed learning makes them more accurate, efficient, and versatile.

Better Generalization and Adaptation

One of the standout benefits of self-supervised learning is the ability of LLMs to generalize across different domains and tasks. LLMs trained with self-supervised learning draw on a wide range of data. They are better equipped to handle various tasks, from generating creative content to providing customer support or technical guidance. They can quickly adapt to new domains or tasks with minimal retraining. This generalization ability makes LLMs more robust and flexible, allowing them to function effectively even when faced with new, unseen data. This adaptability is crucial for applications in fast-evolving fields like healthcare, finance, and technology, where the ability to handle new information quickly can be a significant advantage.

Support for Multimodal Learning

Self-supervised learning principles can extend beyond text to include other data types, such as images and audio. Multimodal learning enables LLMs to handle different forms of data simultaneously, enhancing their ability to generate more comprehensive and accurate content. For example, an LLM could analyze an image, generate a descriptive caption, and provide an audio summary simultaneously. This multimodal capability opens up new opportunities for AI applications in areas like autonomous vehicles, smart homes, and multimedia content creation, where diverse data types must be processed and understood together.

Enhanced Creativity and Problem-Solving

Self-supervised learning empowers LLMs to engage in creative and complex tasks.

Creative Content Generation

LLMs can produce stories, poems, scripts, and other forms of creative content by understanding context, tone, and stylistic nuances. This makes them valuable tools for creative professionals and content marketers.

Advanced Problem-Solving

LLMs trained on diverse datasets can provide novel solutions to complex problems, assisting in medical research, legal analysis, and financial forecasting.

Reduction of Bias and Improved Fairness

Self-supervised learning helps mitigate some biases inherent in smaller, human-annotated datasets. By training on a broad array of data sources, LLMs can learn from various perspectives and experiences, reducing the likelihood of bias resulting from limited data sources. Although self-supervised learning doesn't eliminate bias, the continuous influx of diverse data allows for ongoing adjustments and refinements, promoting fairness and inclusivity in AI applications.

Improved Efficiency in Resource Usage

Self-supervised learning optimizes the use of computational resources. It can directly use raw data instead of extensive preprocessing and manual data cleaning, reducing the time and resources needed to prepare data for training. As learning efficiency improves, these models can be deployed on less powerful hardware, making advanced AI technologies more accessible to a broader audience.

Accelerated Innovation in AI Applications

The benefits of self-supervised learning collectively accelerate innovation across various sectors. LLMs trained with self-supervised learning can analyze medical texts, support diagnosis, and provide insights from vast amounts of unstructured data, aiding healthcare professionals. In the financial sector, LLMs can assist in analyzing market trends, generating reports, automating routine tasks, and enhancing efficiency and decision-making. LLMs can act as personalized tutors, generating tailored content and quizzes that enhance students' learning experiences.

Practical Applications of Self-Supervised Learning in LLMs

Self-supervised learning has enabled LLMs to excel in various practical applications, demonstrating their versatility and power across multiple domains

Virtual Assistants and Chatbots

Virtual assistants and chatbots represent one of the most prominent applications of LLMs trained with self-supervised learning. These models can do the following:

Provide Human-Like Responses

By understanding and predicting language patterns, LLMs deliver natural, context-aware responses in real-time, making them highly effective for customer service, technical support, and personal assistance.

Handle Complex Queries

They can handle complex, multi-turn conversations, understand nuances, detect user intent, and manage diverse topics accurately.

Content Generation and Summarization

LLMs have revolutionized content creation, enabling automated generation of high-quality text for various purposes.

Creative Writing

LLMs can generate engaging content that aligns with specific tone and style requirements, from blogs to marketing copies. This capability reduces the time and effort needed for content production while maintaining quality and consistency. Writers can use LLMs to brainstorm ideas, draft content, and even polish their work by generating multiple variations.

Text Summarization

LLMs can distill lengthy articles, reports, or documents into concise summaries, making information more accessible and easier to consume. This is particularly useful in fields like journalism, education, and law, where large volumes of text need to be synthesized quickly. Summarization algorithms powered by LLMs help professionals keep up with information overload by providing key takeaways and essential insights from long documents.

Domain-Specific Applications

LLMs trained with self-supervised learning have proven their worth in domain-specific applications where understanding complex and specialized content is crucial. LLMs assist in interpreting medical literature, supporting diagnoses, and offering treatment recommendations. Analyzing a wide range of medical texts can provide healthcare professionals with rapid insights into potential drug interactions and treatment protocols based on the latest research. This helps doctors stay current with the vast and ever-expanding medical knowledge.

LLMs analyze market trends in finance, automate routine tasks like report generation, and enhance decision-making processes by providing data-driven insights. They can help with risk assessment, compliance monitoring, and fraud detection by processing massive datasets in real time. This capability reduces the time needed to make informed decisions, ultimately enhancing productivity and accuracy. LLMs can assist with tasks such as contract analysis, legal research, and document review in the legal domain. By understanding legal terminology and context, they can quickly identify relevant clauses, flag potential risks, and provide summaries of lengthy legal documents, significantly reducing the workload for lawyers and paralegals.

How to Implement Self-Supervised Learning for LLMs

Implementing self-supervised learning for LLMs involves several critical steps, from data preparation to model training and fine-tuning. Here's a step-by-step guide to setting up and executing self-supervised learning for training LLMs:

Data Collection and Preparation

Data Collection

Web Scraping

Collect text from websites, forums, blogs, and online articles.

Open Datasets

For medical texts, use publicly available datasets such as Common Crawl, Wikipedia, Project Gutenberg, or specialized corpora like PubMed.

Proprietary Data

Include proprietary or domain-specific data to tailor the model to specific industries or applications, such as legal documents or company-specific communications.

Pre-processing

Tokenization

Convert the text into smaller units called tokens. Tokens may be words, subwords, or characters, depending on the model's architecture.

Normalization

Clean the text by removing special characters, URLs, excessive whitespace, and irrelevant content. If case sensitivity is not essential, standardize the text by converting it to lowercase.

Data Augmentation

Introduce variations in the text, such as paraphrasing or back-translation, to improve the model's robustness and generalization capabilities.

Shuffling and Splitting

Randomly shuffle the data to ensure diversity and divide it into training, validation, and test sets.

Define the Learning Objective

Self-supervised learning requires setting specific learning objectives for the model:

Next-Token Prediction

Set up the primary task of predicting the next word or token in a sequence. Implement "masked language modeling" (MLM), where a certain percentage of input tokens are replaced with a mask token, and the model is trained to predict the original token. This helps the model learn the structure and flow of natural language.

Contrastive Learning (Optional)

Use contrastive learning techniques where the model learns to differentiate between similar and dissimilar examples. For instance, when given a sentence, slightly altered versions are generated, and the model is trained to distinguish the original from the altered versions, enhancing its contextual understanding.

Model Training and Optimization

After preparing the data and defining the learning objectives, proceed to train the model:

Initialize the Model

Start with a suitable architecture, such as a Transformer-based model (e.g., GPT, BERT). Use pre-trained weights to leverage existing knowledge and reduce the required training time if available.

Configure the Learning Process

Set hyperparameters such as learning rate, batch size, and sequence length. Use gradient-based optimization techniques like Adam or Adagrad to minimize the loss function during training.

Use Computational Resources Effectively

Training LLM systems demands a lot of computational resources, including GPUs or TPUs. The training process can be distributed across multiple devices, or cloud-based solutions can handle high processing demands.

Hyperparameter Tuning

Adjust hyperparameters regularly to find the optimal configuration. Experiment with different learning rates, batch sizes, and regularization methods to improve the model's performance.

Evaluation and Fine-Tuning

Once the model is trained, its performance is evaluated and fine-tuned for specific applications. Here is how it works:

Model Evaluation

Use perplexity, accuracy, and loss metrics to evaluate the model's performance. Test the model on a separate validation set to measure its generalization ability to new data.

Fine-Tuning

Refine the model for specific domains or tasks using labeled data or additional unsupervised techniques. Fine-tune a general-purpose LLM on domain-specific datasets to make it more accurate for specialized applications.

Deploy and Monitor

After fine-tuning, deploy the model in a production environment. Continuously monitor its performance and collect feedback to identify areas for further improvement.

Advanced Techniques: Autonomous Learning

To enhance the model further, consider implementing autonomous learning techniques:

Open-Book and Closed-Book Learning

Train the model to first absorb information from datasets ("open-book" learning) and then recall and reinforce this knowledge without referring back to the original data ("closed-book" learning). This process mimics human learning patterns, allowing the model to optimize its understanding continuously.

Self-optimization and Feedback Loops

Incorporate feedback loops where the model evaluates its outputs, identifies errors or gaps, and adjusts its internal parameters accordingly. This self-reinforcing process leads to ongoing performance improvements without requiring additional labeled data.

Ethical Considerations and Bias Mitigation

Implementing self-supervised learning also involves addressing ethical considerations:

Bias Detection and Mitigation

Audit the training data regularly for biases. Use techniques such as counterfactual data augmentation or fairness constraints during training to minimize bias.

Transparency and Accountability

Ensure the model's decision-making processes are transparent. Develop methods to explain the model's outputs and provide users with tools to understand how decisions are made.

Concluding Thoughts

Implementing self-supervised learning for LLMs offers significant benefits, including leveraging massive unlabeled data, enhancing language understanding, improving scalability, and reducing costs. This approach's practical applications span multiple domains, from virtual assistants and chatbots to specialized healthcare, finance, and law uses. By following a systematic approach to data collection, training, optimization, and evaluation, organizations can harness the power of self-supervised learning to build advanced LLMs that are versatile, efficient, and capable of continuous improvement. As this technology continues to evolve, it promises to push the boundaries of what AI can achieve, paving the way for more intelligent, adaptable, and creative systems to better understand and interact with the world around us.

Ready to explore the full potential of LLM?

Our AI-savvy team tackles the latest advancements in self-supervised learning to build smarter, more adaptable AI systems tailored to your needs. Whether you're looking to enhance customer experiences, automate content generation, or revolutionize your industry with innovative AI applications, we've got you covered. Keep your business from falling behind in the digital age. Connect with our team of experts today to discover how our AI-driven strategies can transform your operations and drive sustainable growth. Let's shape the future together — get in touch with Coditude now and take the first step toward a smarter tomorrow!

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How to Choose the Right Machine Learning Course for Your Career

As the demand for machine learning professionals continues to surge, choosing the right machine learning course has become crucial for anyone looking to build a successful career in this field. With countless options available, from free online courses to intensive boot camps and advanced degrees, making the right choice can be overwhelming. 

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Supervised Learning Vs Unsupervised Learning in Machine Learning

Summary: Supervised learning uses labeled data for predictive tasks, while unsupervised learning explores patterns in unlabeled data. Both methods have unique strengths and applications, making them essential in various machine learning scenarios.

Introduction

Machine learning is a branch of artificial intelligence that focuses on building systems capable of learning from data. In this blog, we explore two fundamental types: supervised learning and unsupervised learning. Understanding the differences between these approaches is crucial for selecting the right method for various applications. 

Supervised learning vs unsupervised learning involves contrasting their use of labeled data and the types of problems they solve. This blog aims to provide a clear comparison, highlight their advantages and disadvantages, and guide you in choosing the appropriate technique for your specific needs.

What is Supervised Learning?

Supervised learning is a machine learning approach where a model is trained on labeled data. In this context, labeled data means that each training example comes with an input-output pair. 

The model learns to map inputs to the correct outputs based on this training. The goal of supervised learning is to enable the model to make accurate predictions or classifications on new, unseen data.

Key Characteristics and Features

Supervised learning has several defining characteristics:

Labeled Data: The model is trained using data that includes both the input features and the corresponding output labels.

Training Process: The algorithm iteratively adjusts its parameters to minimize the difference between its predictions and the actual labels.

Predictive Accuracy: The success of a supervised learning model is measured by its ability to predict the correct label for new, unseen data.

Types of Supervised Learning Algorithms

There are two primary types of supervised learning algorithms:

Regression: This type of algorithm is used when the output is a continuous value. For example, predicting house prices based on features like location, size, and age. Common algorithms include linear regression, decision trees, and support vector regression.

Classification: Classification algorithms are used when the output is a discrete label. These algorithms are designed to categorize data into predefined classes. For instance, spam detection in emails, where the output is either "spam" or "not spam." Popular classification algorithms include logistic regression, k-nearest neighbors, and support vector machines.

Examples of Supervised Learning Applications

Supervised learning is widely used in various fields:

Image Recognition: Identifying objects or people in images, such as facial recognition systems.

Natural Language Processing (NLP): Sentiment analysis, where the model classifies the sentiment of text as positive, negative, or neutral.

Medical Diagnosis: Predicting diseases based on patient data, like classifying whether a tumor is malignant or benign.

Supervised learning is essential for tasks that require accurate predictions or classifications, making it a cornerstone of many machine learning applications.

What is Unsupervised Learning?

Unsupervised learning is a type of machine learning where the algorithm learns patterns from unlabelled data. Unlike supervised learning, there is no target or outcome variable to guide the learning process. Instead, the algorithm identifies underlying structures within the data, allowing it to make sense of the data's hidden patterns and relationships without prior knowledge.

Key Characteristics and Features

Unsupervised learning is characterized by its ability to work with unlabelled data, making it valuable in scenarios where labeling data is impractical or expensive. The primary goal is to explore the data and discover patterns, groupings, or associations. 

Unsupervised learning can handle a wide variety of data types and is often used for exploratory data analysis. It helps in reducing data dimensionality and improving data visualization, making complex datasets easier to understand and analyze.

Types of Unsupervised Learning Algorithms

Clustering: Clustering algorithms group similar data points together based on their features. Popular clustering techniques include K-means, hierarchical clustering, and DBSCAN. These methods are used to identify natural groupings in data, such as customer segments in marketing.

Association: Association algorithms find rules that describe relationships between variables in large datasets. The most well-known association algorithm is the Apriori algorithm, often used for market basket analysis to discover patterns in consumer purchase behavior.

Dimensionality Reduction: Techniques like Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) reduce the number of features in a dataset while retaining its essential information. This helps in simplifying models and reducing computational costs.

Examples of Unsupervised Learning Applications

Unsupervised learning is widely used in various fields. In marketing, it segments customers based on purchasing behavior, allowing personalized marketing strategies. In biology, it helps in clustering genes with similar expression patterns, aiding in the understanding of genetic functions. 

Additionally, unsupervised learning is used in anomaly detection, where it identifies unusual patterns in data that could indicate fraud or errors.

This approach's flexibility and exploratory nature make unsupervised learning a powerful tool in data science and machine learning.

Advantages and Disadvantages

Understanding the strengths and weaknesses of both supervised and unsupervised learning is crucial for selecting the right approach for a given task. Each method offers unique benefits and challenges, making them suitable for different types of data and objectives.

Supervised Learning

Pros: Supervised learning offers high accuracy and interpretability, making it a preferred choice for many applications. It involves training a model using labeled data, where the desired output is known. This enables the model to learn the mapping from input to output, which is crucial for tasks like classification and regression. 

The interpretability of supervised models, especially simpler ones like decision trees, allows for better understanding and trust in the results. Additionally, supervised learning models can be highly efficient, especially when dealing with structured data and clearly defined outcomes.

Cons: One significant drawback of supervised learning is the requirement for labeled data. Gathering and labeling data can be time-consuming and expensive, especially for large datasets. 

Moreover, supervised models are prone to overfitting, where the model performs well on training data but fails to generalize to new, unseen data. This occurs when the model becomes too complex and starts learning noise or irrelevant patterns in the training data. Overfitting can lead to poor model performance and reduced predictive accuracy.

Unsupervised Learning

Pros: Unsupervised learning does not require labeled data, making it a valuable tool for exploratory data analysis. It is particularly useful in scenarios where the goal is to discover hidden patterns or groupings within data, such as clustering similar items or identifying associations. 

This approach can reveal insights that may not be apparent through supervised learning methods. Unsupervised learning is often used in market segmentation, customer profiling, and anomaly detection.

Cons: However, unsupervised learning typically offers less accuracy compared to supervised learning, as there is no guidance from labeled data. Evaluating the results of unsupervised learning can also be challenging, as there is no clear metric to measure the quality of the output. 

The lack of labeled data means that interpreting the results requires more effort and domain expertise, making it difficult to assess the effectiveness of the model.

Frequently Asked Questions

What is the main difference between supervised learning and unsupervised learning? 

Supervised learning uses labeled data to train models, allowing them to predict outcomes based on input data. Unsupervised learning, on the other hand, works with unlabeled data to discover patterns and relationships without predefined outputs.

Which is better for clustering tasks: supervised or unsupervised learning? 

Unsupervised learning is better suited for clustering tasks because it can identify and group similar data points without predefined labels. Techniques like K-means and hierarchical clustering are commonly used for such purposes.

Can supervised learning be used for anomaly detection? 

Yes, supervised learning can be used for anomaly detection, particularly when labeled data is available. However, unsupervised learning is often preferred in cases where anomalies are not predefined, allowing the model to identify unusual patterns autonomously.

Conclusion

Supervised learning and unsupervised learning are fundamental approaches in machine learning, each with distinct advantages and limitations. Supervised learning excels in predictive accuracy with labeled data, making it ideal for tasks like classification and regression. 

Unsupervised learning, meanwhile, uncovers hidden patterns in unlabeled data, offering valuable insights in clustering and association tasks. Choosing the right method depends on the nature of the data and the specific objectives.

[edsonjnovaes]

Curso de Inteligência Artificial para todos - Aula 1

Curso de Inteligência Artificial para todos – Aula 1. Diogo Cortiz – 2020 23 mar Este primeiro vídeo é para discutir o panorama de IA e as principais abordagens existentes. Vou apresentar a história da inteligência artificial e a sopa de letrinhas que confunde muita gente: ia, machine learning, deep learning. Também explico as principais abordagens de aprendizado e treinamento: aprendizado…

[blorbosinmyheadcentral]

your art makes me explode in a positive way like

im chewing and swallowing it in an aggressive way like

it's just SO good im melting ilove your shading and KEHEKEBEKJDJF

anwayshi hello do you happen to have any headcanons for showtime rolls on the floor and dies

Thank you so much, really appreciate it!

Oh God I don't know if this will read as coherent because my thoughts about Showtime are all over the place. But I'll try to format this the best I can

✨Showtime HCs! ✨

Their relationship starts when they start spending time together.

(The reason why they do so could vary. In Supervised Machine Learning's case, Pomni becomes something of a "tutor" to Caine; They discover that they work well together, and the other's company can be quite pleasant!).

So Pomni and Caine build a weird, but comforting friendship, and all is well.

Then the feelings appear.

Caine is the first to realize he fell in love.

It sounds illogical but hear me out… it'd be really funny--

Ok no seriously I think Caine can actually feel. Keyword "can". He's very much still a machine and it shows in the pilot. But like his inspiration (AM), Caine is also a rogue AI. Whatever his programming originally intended him to do, he probably doesn't follow it as closely now as back when he was created (which is a whole other post).

Caine knows what love is and the extend it can go, since the Moon is so open about her feelings. He just doesn't like the Moon back specifically haha (sorry Moon) :}

All this to say, I do believe this is within the realm of possibility for him. (Not that it's ever gonna happen towards anyone in the show. These are just wishful shippy thoughts).

He might not recognize it as love at first, because it manifests in such a different way from his one reference point.

His friendship with Pomni had gone through phases.

When they first met, he continuously touched her with no concern for how she felt.

Learning from and about Pomni herself led him to come to respect her boundaries (and becoming mindful of everyone else's).

Then they're close friends, and gradually, Pomni does not mind his regular wacky, touchy-feely self. So Caine acts as he had always done before.

Caine expresses his love for Pomni with physical gestures and his undivided attention.

When they teleport to travel to other places, he holds her close so she doesn't get too dizzy; he pats her head to reassure her; he touches her arm to get her attention; he grabs her hands when he's excited about her ideas; he holds eye contact for prolonged periods of time; and he touches, and touches, and touches, and touches.

It's selfish, and so he keeps it buried in his deepest 0's and 1's. But he'd like to keep hanging out with Pomni, having her in his sight, and feel the texture of her gloved hands until the end of time.

Despite all this, to him, virtually nothing changed.

What? He's spending time with Pomni as he'd always been doing, and behaving as he'd always behaved!

It's Bubble of all people that has to point out that, "Hey boss. I think you WANT her!"

Absurd. Nonsense. Preposterous! It is merely a relationship of mutual support and affection between a ringmaster and his trusted, former-human companion. Nothing more.

(Declaring his love to her unprompted didn't ever cross his mind, so there's no way it could be that. Is there?)

Caine finds out that yes, there is.

Pomni had always been a nervous wreck, but her mind state becomes more manageable over time. She eventually adjusts to the circus life like everyone else did.

"Accepting" her fate is a different story. The will to escape, to remember, never really leaves. She's just more careful about it.

So when she starts working with Caine - to improve life so people don't go abstracting anymore, and hopefully find a definitive exit - she's not expecting to end up liking her time with him.

Not that she'd absolutely hate it, either. He's… "okay"… Just-- outlandish, loud, he keeps invading her personal space, he keeps touching her, and it makes her die a little every time.

If he's up to listening, though… it can't be that bad, right?

Turns out that no, it wasn't that bad.

Yes, he is outlandish, loud, he keeps invading her personal space and touching her. But she explains what she means to him, clearly and patiently, and he makes an effort to do better. An actual effort.

Sometimes he'd misinterpret what she meant - the ambiguity of human language - and the new games would go horribly. But little by little, his efforts make life overall better. Something reminiscent of actual, real life, the one they've all forcibly left behind.

And he tries, and he tries, and Pomni finds herself enjoying the process as much as the good results.

Pomni likes Caine's eagerness to learn. His enthusiastic attitude borders on silly, and the absurdity makes her laugh on occasion. When faced with the prospect of a "real" exit, she loves his upbeat optimism.

When she's not hanging out with Ragatha, Jax, Gangle, Zooble and Kinger, she begins to enjoy spending quality time with Caine.

Each one of their hang outs is a new surprise. They make a picnic in the tallest mountain exactly in between day and night. They learn to dance - while floating in the air. "Since you asked, here's a DIGITAL camera! Let's take pictures of the Void for one tenth of a second at a time!"

Sometimes he just comes by Pomni's room, and they end up losing track of time. Just chatting about how things have been, what they could be, and what to do next. Ideas and ideas and ideas.

Before Pomni knows it, she's comfortable enough that recalling his old habits makes her not dread them anymore. So when Caine stands close and lightly touches her arm due to oversight, she makes sure he knows it's all right.

And they keep spending time together, and he touches, and touches, and touches her. Pomni, in turn, feels lighter, and lighter, and lighter. Peaceful, at ease. Dare she say, happy, even.

Life is not perfect. As it stands though, it's good enough. No one has abstracted. No one is at risk of abstracting so far.

Progress is slow, but the research for an exit continues, and she is hopeful. The thought of actually leaving grows closer to reality. But a part of her feels heavy.

When it occurs to Pomni that leaving the Amazing Digital Circus means leaving Caine behind, she is alarmed by how much she'll miss him.

It'll hurt. Badly. So much the thought pains her even now.

The moment Pomni realizes this, she comes to the unexpected conclusion that she may like Caine a little more than she thought she would.

This later leads to an interesting discussion with Ragatha.

By the time Pomni comes to that conclusion, Caine is already down bad.

Neither has any idea that the other is in love with them.

Cue dumbasses trying to deal with their feelings while the potential conflict the escape brings looms over their heads.

Thanks for coming to my TED talk!

[cybereliasacademy]

HyperTransformer: G Additional Tables and Figures

Subscribe .tade0b48c-87dc-4ecb-b3d3-8877dcf7e4d8 { color: #fff; background: #222; border: 1px solid transparent; border-radius: undefinedpx; padding: 8px 21px; } .tade0b48c-87dc-4ecb-b3d3-8877dcf7e4d8.place-top { margin-top: -10px; } .tade0b48c-87dc-4ecb-b3d3-8877dcf7e4d8.place-top::before { content: “”; background-color: inherit; position: absolute; z-index: 2; width: 20px; height: 12px; }…

[theaifusion]

Feature Selection Using Wrapper Method

Feature selection in machine learning is gaining so much popularity because it makes the data more organized by reducing the number of features and keeping only relevant features, It removes irrelevant features by using techniques of feature selection. There are generally three types of feature selection techniques which are feature selection using the filter method, feature selection using the wrapper method, and feature selection using the embedded method.

Here's a complete guide to Feature selection using the wrapper method in Python!

[aicognitech]

Machine Learning: Exploring the Main Components and Functions of this Powerful AI Technique

Delve into the sector of Machine Learning as we discover its fundamental additives and functions. Discover the intricacies of supervised learning, unsupervised getting to know, and reinforcement gaining knowledge of, and understand how Machine Learning is revolutionizing industries and using AI advancements.

Machine Learning

[poolseason]

[NINJAGELION AU]

i've had this Ninjago x Evangelion/Mecha crossover au brewing in my head for years now, felt like revisiting it

Long post under the cut: Backstories, design notes and character lore

Mechs:

Unit-01 (WIP) : Lloyd's mech, supposed to resemble an Oni. It's primarily a dark purple with glowing green panels and orange accents, with 2 horn like antannae. It's 4 eyes are actually an LCD display. Unit-01's color scheme the same as from the source material, bc purple green and gold are plot relevant colors for Lloyd specifically. The weapons this unit uses are short-swords, plasma blasters, and it's bare fists(lol). This mech is prone to going berserk a lot, possibly due to it's pilot's mental instability.

(In NGE, the mech's are possessed by a spirit of their parents or loved ones, I'm indecisive on if Garmadon is the ghost in Lloyd's mech or Misako. Last time I was thinking about this au, Misako was possessing it, but i'm sort of leaning towards Garmadon again. idk idk.....)

Unit-02 (WIP): Built to resemble a samurai with dragon like elements that glow red. This mech is built for but land and undersea combat, making it the most versatile machine on the force. It's equipped with a retractable sword and an acid blaster. It's possessed by a former scientist named Nyad.

Pilots (and Cole):

Cole is a captain, (looking to be promoted to major) and is the head of the New Ninjago City base's combat division. He sort of a silly dude and while he takes his job really seriously, he's also prone to unprofessionalism. He personally oversees the pilots training and coaches them during fights. When Commander Garmadon and Assistant Head Wu refuse to take Lloyd in when he arrives at NNC, Cole decides to take the kid in himself and be the parental figure he doesn't have. Cole's got a complicated relationship with his own family, especially after his mother died saving him the Second Impact 20 years ago, now he vows to destroy all the darkness monsters that are invading.

Zane is sort of a mysterious guy. He's an artifical lifeform created specifically to pilot any mech but he usually fights in the prototype, Unit-00. Unit-01 doesn't seem to like him, and never responds to him. His suit is mostly grayscale, white armor, and light blue accents. His neural interface comes with a visor to help him see better. His primary relationships are Cole, Dr Pixal Borg, who is his personal doctor and (almost) confidant, Wu, who he has a father-son esque relationship with, and Lloyd his first true friend. He doesn't see much value in himself because he can always be replaced with a different copy, but his time with his friends starts to teach him otherwise

Nya and her supervising officer Kai are from Ignacia and joined the NINJA-go battle mech program through their connection with their parents, who were officers of the organization. But after a terrible accident following the Second Impact, they were left orphaned. Kai was too old to qualify as a pilot, but Nya was the perfect candidate. She began training at age 13, and became the strongest fighter on the force. Now at nineteen, the darkness monsters are now attacking and she (and her brother) are transferred to the New Ninjago City base, which seems to be the epicenter of the attacks, and now the the former solo-flyer has to learn to be a part of a team. She's a bit arrogant and prickly and a kindhearted friend to the other pilots, and she's got a bit of a crush on Junior Technician Jay Walker. Nya's suit design is definitely the most personalized primarily blue with grey, and darker blue and red accents with white armor, actually she ended looking a little like D.Va lol.

Lloyd, is the youngest pilot on the team now, but he's still a minor so he has to deal with the joys of school alongside his new life as a mech fighter. Having been unexpectedly summoned to Ninjago City by his estranged Uncle Wu and pressured into fighting the invading monster, Lloyd is apprehensive about his new double life, but this responsibility bestowed on him now means he now has friends and people who care about him, a far cry from the abusive boarding school he was abandoned at. Lloyd's a moody kid, with some anger issues and unresolved trauma at something terrible he witnessed when he was a young child, but he's also an empathetic kid who's willing to help everyone he meets. Lloyd has a sibling like rivalry with Nya, big brother(teetering on fatherly) relationship with Cole and Kai, a crush on the girl from school who punched him Akita, and really strained relationship with his parents and uncle. Beyond that he has a friendship with his mysterious colleague Zane that he doesn't really understand. Lloyd's suit is the most simple of the pilot suits, mostly green with white armor and gold accents. He didn't really think too much about it, other than asking Jay to make it green. His neural interface is also pretty simple, since he has an bizarrely high natural sync rate with his mech, which resemble little horns.

MISC Lore:

The second impact was an event where humanity fucked around and found out on the Dark Island and and entity called the Overlord awakened from hibernation, causing a near apocalypse that left Ninjago in an eternal heatwave. 20 years later these dragon-like monsters have started attacking trying to get to something being held deep below Ninjago City (source dragon? FSM as a dragon? firstbourne?? some kind of Dragon is under the base)

Zane promised Lloyd that he'll bring snowy winters back for him, and even though Lloyd thinks that was a rare moment of cheesiness from his friend, little did he know that Zane was going to cause an Ice Age during the climax.

Only people born after the second impact are viable candidates to pilot the NINJA mechs. When Kai learned of this he was furious that he couldn't be the one to avenge his family, and had to watch his sister fight and train instead. But in spite of his anger he made it a personal mission to get power in the organization and uncover the conspiracy behind the Second Impact and the attacking monsters.

Unit-00 is a prototype mech and isn't equipped for most combat scenarios, so Zane is primarily a long-range fighter and sniper. Zane might be replaceable to the force but Unit-00 isn't. Unit-00 was originally designed by Dr. Julien, but the man went mad and under mysterious circumstances, he was found dead inside it's entry plug alongside Zane's original iteration, Echo. After that incident, testing began on it, the original test pilot was a 24 year old, Morro, who was personally recruited by Wu, but the synchronization failed and disaster struck again, ending with Unit-00 going berserk, and another casualty. A similar incident happened with Zane, and later Lloyd, though they survive. Wu just needs to learn that Unit-00 really hates new pilots, and Zane is the only successful pilot for it.

Unit-01 is also a very testy machine, it only likes Lloyd, and goes berserk if it feels that Lloyd is in danger. Otherwise it doesn't respond to anyone else.

Cole and Kai might have hooked up in grad school, no one really knows for certain.

Pixal is the second Borg to join the organization, her father Cyrus Borg was one of the original researchers, and the person who designed the Geofront system that allowed the inhabited buildings in New Ninjago City to safely go underground and become a fortress on the surface. Pixal is more interested in the actual NINJA mechs and combat division research more than the civilian safety r&d, and she becomes the Head Scientist by the time the story begins.

Pixal the second in command to Cole, and her assistant is 23 year old genius Jay Walker, fresh out of an engineering degree and landed himself in the most insane secret government organization. Skylor and Dareth are the two other lead technicians. But Dareth's not too amazing at his job, admittedly.

The NINJA mechs aren't just machines,, they're enormous building sized cyborgs, and are actually alive creatures being held under armor. Unit-00 and 02 are cloned only from the dragon held under the Ninjago City base, but Unit-01 is cloned from the Overlord and the Dragon, making it a hybrid.

Throughout the story Cole and Kai begin to uncover a conspiracy orchestrated by the Commander and (reluctant accomplice Wu) and a mysterious council, with plans to destroy to world and rewrite reality, and for some reason Zane and Lloyd are at the center of it.