Pickl AI

NOVEMBER 28, 2024

Without linear algebra, understanding the mechanics of Deep Learning and optimisation would be nearly impossible. These techniques span different types of learning and provide powerful tools to solve complex real-world problems. Neural networks are the foundation of Deep Learning techniques.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Mlearning.ai

JUNE 1, 2023

Using PyTorch Deep Learning Framework and CNN Architecture Photo by Andrew S on Unsplash Motivation Build a proof-of-concept for Audio Classification using a deep-learning neural network with PyTorch framework. This is inherently a supervised learning problem. Data Source here.

Deep Learning 52

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Heartbeat

MARCH 7, 2024

ScikitLLM is interesting because it seamlessly integrates LLMs into your traditional Scikit-learn (Sklearn) library. In this post, we’ll take a deep dive into ScikitLLM and explore how you can use it to build text summarization ML models and monitor them all in Comet. We will, however, make use of OpenAI.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Pickl AI

NOVEMBER 18, 2024

The main types are supervised, unsupervised, and reinforcement learning, each with its techniques and applications. Supervised Learning In Supervised Learning , the algorithm learns from labelled data, where the input data is paired with the correct output. spam email detection) and regression (e.g.,

Machine Learning 52

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Dataconomy

MARCH 13, 2023

With advances in machine learning, deep learning, and natural language processing, the possibilities of what we can create with AI are limitless. There are several types of AI algorithms, including supervised learning, unsupervised learning, and reinforcement learning.

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Pickl AI

APRIL 2, 2024

Machine Learning: Subset of AI that enables systems to learn from data without being explicitly programmed. Supervised Learning: Learning from labeled data to make predictions or decisions. Unsupervised Learning: Finding patterns or insights from unlabeled data.

Data Science 52

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DagsHub

NOVEMBER 11, 2024

These vector databases store complex data by transforming the original unstructured data into numerical embeddings; this is enabled through deep learning models. AI also plays an important role in this process because it uses deep learning methods to create embeddings that find all the key features of the original data.

AI 52

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Pickl AI

SEPTEMBER 18, 2024

offer specialised Machine Learning and Artificial Intelligence courses covering Deep Learning , Natural Language Processing, and Reinforcement Learning. Developing more sophisticated algorithms, such as transformers and self-supervised learning models, pushes the boundaries of what AI can achieve.

Machine Learning 52

Machine Learning Machine Learning Algorithm Natural Language Processing 52

Pickl AI

APRIL 26, 2024

Machine learning is a subset of artificial intelligence that enables computers to learn from data and improve over time without being explicitly programmed. Explain the difference between supervised and unsupervised learning. Have you worked with cloud-based data platforms like AWS, Google Cloud, or Azure?

Data Analyst 52

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DagsHub

NOVEMBER 18, 2024

However, the growth of deep learning concepts like transformers , GANs , etc. In supervised learning, image annotation plays a key role as it supplies the necessary labels to train the computer vision algorithms. where the model tries to learn and identify different features and objects based on the annotated data.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

The MLOps Blog

MARCH 21, 2023

Name Short Description Algorithmia Securely govern your machine learning operations with a healthy ML lifecycle. An end-to-end enterprise-grade platform for data scientists, data engineers, DevOps, and managers to manage the entire machine learning & deep learning product life-cycle. Allegro.io

Machine Learning 69

Machine Learning Machine Learning Data Scientist ML 69

Dataconomy

MARCH 18, 2025

How anomaly detection works Understanding how anomaly detection works involves exploring different machine learning approaches. Supervised machine learning Supervised learning uses labeled datasets to train models. Microsoft Azure Anomaly Detector: Offers cloud solutions for detecting anomalies in time series data.

Algorithm 91

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