Smart Data Collective

JUNE 4, 2021

Learning the various categories of machine learning, associated algorithms, and their performance parameters is the first step of machine learning. Machine learning is broadly classified into three types – Supervised. In supervised learning, a variable is predicted. Semi-Supervised Learning.

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Dataconomy

MAY 29, 2023

The applications of AI span diverse domains, including natural language processing, computer vision, robotics, expert systems, and machine learning. Some of the methods used in ML include supervised learning, unsupervised learning, reinforcement learning, and deep learning.

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Data Science Dojo

JUNE 11, 2024

These include image recognition, natural language processing, autonomous vehicles, financial services, healthcare, recommender systems, gaming and entertainment, and speech recognition. Recurrent Neural Networks (RNNs) RNNs belong to the category of deep neural networks.

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IBM Journey to AI blog

JULY 6, 2023

While artificial intelligence (AI), machine learning (ML), deep learning and neural networks are related technologies, the terms are often used interchangeably, which frequently leads to confusion about their differences. How do artificial intelligence, machine learning, deep learning and neural networks relate to each other?

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Pickl AI

AUGUST 1, 2024

Summary : Deep Learning engineers specialise in designing, developing, and implementing neural networks to solve complex problems. Introduction Deep Learning engineers are specialised professionals who design, develop, and implement Deep Learning models and algorithms.

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Pickl AI

FEBRUARY 7, 2025

Summary: Machine Learning and Deep Learning are AI subsets with distinct applications. ML works with structured data, while DL processes complex, unstructured data. Introduction In todays world of AI, both Machine Learning (ML) and Deep Learning (DL) are transforming industries, yet many confuse the two.

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Heartbeat

SEPTEMBER 20, 2023

Pixabay: by Activedia Image captioning combines natural language processing and computer vision to generate image textual descriptions automatically. Deep learning-based models, especially CNNs, have revolutionized feature extraction in image captioning.

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Heartbeat

OCTOBER 11, 2023

This process is known as machine learning or deep learning. Two of the most well-known subfields of AI are machine learning and deep learning. Supervised, unsupervised, and reinforcement learning : Machine learning can be categorized into different types based on the learning approach.

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Pickl AI

SEPTEMBER 16, 2024

Summary : Deep Belief Networks (DBNs) are Deep Learning models that use Restricted Boltzmann Machines and feedforward networks to learn hierarchical features and model complex data distributions. What is a Deep Belief Network (DBN)? They are effective in image recognition, NLP, and speech recognition.

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Heartbeat

AUGUST 29, 2023

Deep learning is a branch of machine learning that makes use of neural networks with numerous layers to discover intricate data patterns. Deep learning models use artificial neural networks to learn from data. Semi-Supervised Learning : Training is done using both labeled and unlabeled data.

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Dataconomy

APRIL 18, 2023

Understanding the basics of artificial intelligence Artificial intelligence is an interdisciplinary field of study that involves creating intelligent machines that can perform tasks that typically require human-like cognitive abilities such as learning, reasoning, and problem-solving.

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Dataversity

DECEMBER 16, 2022

2022 was a big year for AI, and we’ve seen significant advancements in various areas – including natural language processing (NLP), machine learning (ML), and deep learning. Unsupervised and self-supervised learning are making ML more accessible by lowering the training data requirements.

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Mlearning.ai

DECEMBER 13, 2023

Mastering Deep Learning and AI Interview Questions: What You Need to Know Image created by the author on Canva Knowledge is power, but enthusiasm pulls the switch.” Ever wondered what it takes to excel in deep learning interviews? Explain how you would implement transfer learning in a deep learning model.

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Mlearning.ai

JULY 11, 2023

Deep learning has transformed artificial intelligence, allowing machines to learn and make smart decisions. If you’re interested in exploring deep learning, this step-by-step guide will help you learn the basics and develop the necessary skills. Also, learn about common algorithms used in machine learning.

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Becoming Human

MAY 11, 2023

Empowering Startups and Entrepreneurs | InvestBegin.com | investbegin The success of ChatGPT can be attributed to several key factors, including advancements in machine learning, natural language processing, and big data. Another key component of the development of ChatGPT is deep learning.

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Pickl AI

NOVEMBER 28, 2024

Summary: Generative Adversarial Network (GANs) in Deep Learning generate realistic synthetic data through a competitive framework between two networks: the Generator and the Discriminator. In answering the question, “What is a Generative Adversarial Network (GAN) in Deep Learning?”

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Towards AI

JULY 23, 2023

In the first part of the series, we talked about how Transformer ended the sequence-to-sequence modeling era of Natural Language Processing and understanding. Semi-Supervised Sequence Learning As we all know, supervised learning has a drawback, as it requires a huge labeled dataset to train.

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IBM Journey to AI blog

DECEMBER 20, 2023

And retailers frequently leverage data from chatbots and virtual assistants, in concert with ML and natural language processing (NLP) technology, to automate users’ shopping experiences. Semi-supervised learning The fifth type of machine learning technique offers a combination between supervised and unsupervised learning.

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Heartbeat

FEBRUARY 28, 2023

The past few years have witnessed exponential growth in medical image analysis using deep learning. In this article we will look into medical image segmentation and see how deep learning can be helpful in these cases. This can be further classified as supervised and unsupervised learning. Image by author.

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Dataconomy

MARCH 14, 2023

In recent years, natural language processing and conversational AI have gained significant attention as technologies that are transforming the way we interact with machines and each other. Moreover, the model training process is capable of adapting to new languages and data effectively.

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Hacker News

MAY 3, 2023

The core process is a general technique known as self-supervised learning , a learning paradigm that leverages the inherent structure of the data itself to generate labels for training. Fine-tuning may involve further training the pre-trained model on a smaller, task-specific labeled dataset, using supervised learning.

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Dataconomy

MAY 25, 2023

Here are some ways AI enhances IoT devices: Advanced data analysis AI algorithms can process and analyze vast volumes of IoT-generated data. By leveraging techniques like machine learning and deep learning, IoT devices can identify trends, anomalies, and patterns within the data.

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Becoming Human

MARCH 16, 2023

Since the advent of deep learning in the 2000s, AI applications in healthcare have expanded. Machine Learning Machine learning (ML) focuses on training computer algorithms to learn from data and improve their performance, without being explicitly programmed. A few AI technologies are empowering drug design.

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How to Learn Machine Learning

MAY 14, 2023

Acquiring Essential Machine Learning Knowledge Once you have a strong foundation in mathematics and programming, it’s time to dive into the world of machine learning. Additionally, you should familiarize yourself with essential machine learning concepts such as feature engineering, model evaluation, and hyperparameter tuning.

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Hacker News

MARCH 25, 2022

“Transformers made self-supervised learning possible, and AI jumped to warp speed,” said NVIDIA founder and CEO Jensen Huang in his keynote address this week at GTC. Transformers are in many cases replacing convolutional and recurrent neural networks (CNNs and RNNs), the most popular types of deep learning models just five years ago.

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DrivenData Labs

FEBRUARY 3, 2025

His research focuses on applying natural language processing techniques to extract information from unstructured clinical and medical texts, especially in low-resource settings. I love participating in various competitions involving deep learning, especially tasks involving natural language processing or LLMs.

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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. However, the process of creating AI can seem daunting to those who are unfamiliar with the technicalities involved. What is required to build an AI system?

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Pickl AI

JULY 12, 2024

Summary: This guide explores Artificial Intelligence Using Python, from essential libraries like NumPy and Pandas to advanced techniques in machine learning and deep learning. TensorFlow and Keras: TensorFlow is an open-source platform for machine learning.

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Pickl AI

AUGUST 14, 2024

They consist of interconnected nodes that learn complex patterns in data. Different types of neural networks, such as feedforward, convolutional, and recurrent networks, are designed for specific tasks like image recognition, Natural Language Processing, and sequence modelling.

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Pickl AI

SEPTEMBER 10, 2024

It processes multiple inputs, applies weights, and produces an output based on an activation function. Despite its limitations, the Perceptron laid the groundwork for more complex neural networks and Deep Learning advancements. Deep Learning The advent of Deep Learning has further advanced the capabilities of neural networks.

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Towards AI

FEBRUARY 19, 2025

Learning: Ability to improve performance over time using feedback loops. It perceives user input (text), decides on a response using natural language processing (NLP), executes the action (sending the reply), and learns from past interactions to enhance future responses. Learn More About Scikit-Learn 2.

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Pickl AI

MAY 29, 2024

Multi-Task Learning Deep Learning is a towering pillar in the vast landscape of artificial intelligence, revolutionising various domains with remarkable capabilities. Deep Learning algorithms have become integral to modern technology, from image recognition to Natural Language Processing.

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Dataconomy

SEPTEMBER 13, 2023

For example, they are relatively easy to train and require minimal computational resources compared to other types of deep learning models. As a result, diffusion models have become a popular tool in many fields of artificial intelligence, including computer vision, natural language processing, and audio synthesis.

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IBM Journey to AI blog

JUNE 14, 2023

Traditional AI tools, especially deep learning-based ones, require huge amounts of effort to use. With a foundation model, often using a kind of neural network called a “transformer” and leveraging a technique called self-supervised learning, you can create pre-trained models for a vast amount of unlabeled data.

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PyImageSearch

MARCH 30, 2023

If you want a gentle introduction to machine learning for computer vision, you’re in the right spot. Here at PyImageSearch we’ve been helping people just like you master deep learning for computer vision. Also, you might want to check out our computer vision for deep learning program before you go.

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Pickl AI

AUGUST 1, 2023

As technology continues to impact how machines operate, Machine Learning has emerged as a powerful tool enabling computers to learn and improve from experience without explicit programming. In this blog, we will delve into the fundamental concepts of data model for Machine Learning, exploring their types.

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Dataconomy

MARCH 27, 2023

Some of the ways in which ML can be used in process automation include the following: Predictive analytics:  ML algorithms can be used to predict future outcomes based on historical data, enabling organizations to make better decisions. Technology: Includes a range of technologies, including ML and deep learning.

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