Machine Learning Mastery’s Post

Hyperparameter tuning in machine learning is the process of finding the optimal configuration of hyperparameters to improve a model’s performance. Unlike model parameters, which are learned during training (like weights in neural networks), hyperparameters are set prior to the training process and control how the model learns. Examples include learning rate, batch size, number of layers, number of neurons, and regularization strength. #MachineLearning #HyperparameterTuning #ArtificialIntelligence

I’ve shared an in-depth exploration of how self-attention operates internally when applied to images, breaking down the mathematics behind it. This piece covers everything from calculating attention scores to applying the softmax function and how these concepts help neural networks focus on the most relevant parts of an image. What’s Inside: A step-by-step breakdown of how self-attention processes images. Mathematical insights into the calculation and scaling of attention scores. The role of multi-head attention in capturing complex image features. This article aims to clarify the intricate details of self-attention, making it easier to understand how this powerful mechanism enhances image-processing tasks. Machine Learning DeepLearning.AI Computer Vision Computer Vision Center

My blog article on computer vision, the ability of machines to interpret and understand visual information from their environment

Clone the Abilities of Powerful LLMs into Small Local Models Using Knowledge Distillation ... https://lnkd.in/exjXSaqe #AI #ML #Automation

A lecture on the score-matching perspective of diffusion models: https://lnkd.in/eJyiVxNY How come estimating the score function (gradient of the log likelihood) is easier than the actual likelihood function? how does it relate to the denoising objective of popular diffusion models?

Introduction to Adversarial Attack In Computer Vision

Why Use SVM for multiclass problem? In many real-world classification tasks, like image recognition or email categorization, we often encounter more than two classes. A great example is the Iris dataset, which has three flower types to classify (Setosa, Versicolor, Virginica). Since SVM inherently handles only binary classification, two main strategies are used to tackle multiclass problems effectively. Curious to know more? Check out my detailed article on #Medium! 👇 📬 Feel free to open an issue or reach out to me with your feedback or suggestions for improvements. #SVM #MachineLearning #Learning #ML #Classification #AI #DataScience

Object detection is one of the most important tasks in Computer Vision (CV). The YOLOv6 model localizes a section within an image and classifies the marked region within a predefined category. The output of the object detection is typically a bounding box and a label. Read more below 😃

Artificial Intelligence (AI) refers to the capability of computer systems to perform tasks that typically require human intelligence, such as understanding language, recognizing patterns, and making decisions. This field encompasses various technologies, including machine learning, deep learning, and natural language processing. Coursera The concept of AI dates back to the 1950s, with British logician Alan Turing's pioneering work laying the groundwork for the field. Encyclopedia Britannica Over the decades, AI has evolved significantly, leading to the development of systems that can learn from data and improve their performance over time.A pivotal advancement in AI is the development of transformer models, which have revolutionized natural language processing. These models utilize mechanisms that allow them to focus on different parts of input data, enabling them to understand and generate human-like text. This architecture has been instrumental in the creation of large language models like GPT-3 and GPT-4, which can perform a variety of language-related tasks with remarkable proficiency. The impact of AI is profound, influencing sectors such as healthcare, finance, and entertainment. In healthcare, AI assists in diagnosing diseases and personalizing treatment plans. In finance, it aids in fraud detection and algorithmic trading. In entertainment, AI contributes to content recommendation and even the creation of art and music. However, the rapid advancement of AI also raises ethical and societal concerns, including issues of bias, privacy, and the potential for job displacement. It is crucial to address these challenges to ensure that AI technologies are developed and deployed responsibly, maximizing their benefits while mitigating potential harms. For a deeper understanding of how transformer models function, you may find the following video insightful: https://lnkd.in/gSZGVgBx