Pickl AI

FEBRUARY 12, 2024

Differentiate between supervised and unsupervised learning algorithms. Supervised learning algorithms learn from labelled data, where each input is associated with a corresponding output label. What is the Central Limit Theorem, and why is it important in statistics?

Data Science 52

Data Science Cross Validation Machine Learning Machine Learning 52

AWS Machine Learning Blog

OCTOBER 30, 2024

It was distilled from a larger teacher model (approximately 5 billion parameters), which was pre-trained on a large amount of unlabeled ASIN data and pre-fine-tuned on a set of Amazon supervised learning tasks (multi-task pre-fine-tuning). Kara is passionate about innovation and continuous learning.

Machine Learning 97

Machine Learning Machine Learning AWS Data Scientist 97

Becoming Human

MAY 15, 2023

The two most common types of supervised learning are classification , where the algorithm predicts a categorical label, and regression , where the algorithm predicts a numerical value. Three of the most popular cloud platforms are Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure.

Data Science 75

Data Science Machine Learning Machine Learning Database 75

IBM Journey to AI blog

JULY 6, 2023

Subcategories of machine learning Some of the most commonly used machine learning algorithms include linear regression , logistic regression, decision tree , Support Vector Machine (SVM) algorithm, Naïve Bayes algorithm and KNN algorithm. These can be supervised learning, unsupervised learning or reinforced/reinforcement learning.

Machine Learning 70

Machine Learning Machine Learning Data Science Big Data 70

Towards AI

FEBRUARY 11, 2025

Others believe that innovations in reasoning models, reinforcement learning, and self-supervised learning will continue pushing the boundaries of AI capabilities. Some argue that while scaling has driven progress so far, we may eventually exhaust high-quality training data, leading to diminishing returns.

Data Quality 54

Data Quality Data Engineering Data Engineering Data Engineering 54

Pickl AI

AUGUST 1, 2024

Caffe: A Deep Learning framework focused on speed and modularity, often used for image processing tasks. MXNet: An efficient and flexible Deep Learning framework that supports multiple programming languages and is particularly well-suited for cloud computing.

Deep Learning 52

Deep Learning Deep Learning Machine Learning Machine Learning 52

Heartbeat

OCTOBER 25, 2023

Traditional computational infrastructure may not be sufficient to handle the vast amounts of data generated by high-throughput technologies. Developing scalable and efficient algorithms and leveraging cloud computing and parallel processing techniques are necessary to tackle significant data challenges in bioinformatics.

Machine Learning 52

Machine Learning Machine Learning Data Scientist Algorithm 52

Mlearning.ai

AUGUST 6, 2023

Towards the end of my studies, I incorporated basic supervised learning into my thesis and picked up Python programming at the same time. I also started on my data science journey by attending the Coursera specialization by Andrew Ng —  Deep Learning. I also learnt about cloud computing, specifically, AWS.

Data Science 52

Data Science Machine Learning Machine Learning ML 52

Pickl AI

SEPTEMBER 23, 2024

Machine Learning Supervised Learning includes algorithms like linear regression, decision trees, and support vector machines. Unsupervised Learning techniques such as clustering and dimensionality reduction to discover patterns in data.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Data Science Machine Learning 52