Data Science Dojo

SEPTEMBER 11, 2023

From virtual assistants like Siri and Alexa to personalized recommendations on streaming platforms, chatbots, and language translation services, language models surely are the engines that power it all. First Generation: Early language models used simple statistical techniques like n-grams to predict words based on the previous ones.

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Becoming Human

MAY 11, 2023

Artificial intelligence (AI) has come a long way in recent years, and one of the most exciting developments in this field is the rise of language models like ChatGPT. In this article, we will explore these factors in more detail, and examine how they have contributed to the rise of ChatGPT and other language models.

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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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Dataconomy

MAY 25, 2023

By harnessing the power of AI in IoT, we can create intelligent ecosystems where devices seamlessly communicate, collaborate, and make intelligent choices to improve our lives. Let’s explore the fascinating intersection of these two technologies and understand how AI enhances the functionalities of IoT.

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Smart Data Collective

JUNE 4, 2021

Some of the applications of data science are driverless cars, gaming AI, movie recommendations, and shopping recommendations. Learning the various categories of machine learning, associated algorithms, and their performance parameters is the first step of machine learning. In supervised learning, a variable is predicted.

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IBM Journey to AI blog

JUNE 14, 2023

Sometimes the problem with artificial intelligence (AI) and automation is that they are too labor intensive. Traditional AI tools, especially deep learning-based ones, require huge amounts of effort to use. This is an open, hard problem for the entire field of AI applications.

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Dataconomy

MARCH 13, 2023

The creation of artificial intelligence (AI) has long been a dream of scientists, engineers, and innovators. With advances in machine learning, deep learning, and natural language processing, the possibilities of what we can create with AI are limitless.

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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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Data Science Blog

FEBRUARY 23, 2023

1, Data is the new oil, but labeled data might be closer to it Even though we have been in the 3rd AI boom and machine learning is showing concrete effectiveness at a commercial level, after the first two AI booms we are facing a problem: lack of labeled data or data themselves.

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Hacker News

MARCH 25, 2022

If you want to ride the next big wave in AI, grab a transformer. A transformer model is a neural network that learns context and thus meaning by tracking relationships in sequential data like the words in this sentence. They’re driving a wave of advances in machine learning some have dubbed transformer AI.

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IBM Journey to AI blog

JULY 6, 2023

What is machine learning? Machine learning (ML) is a subset of artificial intelligence (AI) that focuses on learning from what the data science comes up with. Machine learning can then “learn” from the data to create insights that improve performance or inform predictions.

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IBM Journey to AI blog

AUGUST 11, 2023

Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation. Machine learning engineers take massive datasets and use statistical methods to create algorithms that are trained to find patterns and uncover key insights in data mining projects.

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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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AWS Machine Learning Blog

AUGUST 16, 2023

Training machine learning (ML) models to interpret this data, however, is bottlenecked by costly and time-consuming human annotation efforts. One way to overcome this challenge is through self-supervised learning (SSL). Jeremy Anderson is a Director & Data Scientist at Travelers on the AI & Automation Accelerator team.

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Dataconomy

MARCH 27, 2023

RPA is often considered a form of artificial intelligence, but it is not a complete AI solution. AI, on the other hand, can learn from data and adapt to new situations without human intervention. Unsupervised learning:  This involves using unlabeled data to identify patterns and relationships within the data.

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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. Machine learning is a subset of AI. What is artificial intelligence (AI)?

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Pickl AI

AUGUST 1, 2023

Text mining is also known as text analytics or Natural Language Processing (NLP). It is the process of deriving valuable patterns, trends, and insights from unstructured textual data. Popular open-source libraries include NLTK (Natural Language Toolkit), spaCy, Gensim, sci-kit-learn, and TensorFlow.

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Dataconomy

MARCH 27, 2023

RPA is often considered a form of artificial intelligence, but it is not a complete AI solution. AI, on the other hand, can learn from data and adapt to new situations without human intervention. Unsupervised learning:  This involves using unlabeled data to identify patterns and relationships within the data.

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Heartbeat

NOVEMBER 16, 2023

An In-depth Look into Evaluating AI Outputs, Custom Criteria, and the Integration of Constitutional Principles Photo by Markus Winkler on Unsplash Introduction In the age of conversational AI, chatbots, and advanced natural language processing, the need for systematic evaluation of language models has never been more pronounced.

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Becoming Human

MARCH 16, 2023

AI drug discovery is exploding. Overhyped or not, investments in AI drug discovery jumped from $450 million in 2014 to a whopping $58 billion in 2021. AI has already helped identify promising candidate therapeutics, and it didn’t take years but months or even days. We will look at success stories, AI benefits, and limitations.

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IBM Journey to AI blog

DECEMBER 20, 2023

That’s why diversifying enterprise AI and ML usage can prove invaluable to maintaining a competitive edge. What is machine learning? ML is a computer science, data science and artificial intelligence (AI) subset that enables systems to learn and improve from data without additional programming interventions.

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Hacker News

MAY 3, 2023

In the grand tapestry of modern artificial intelligence, how do we ensure that the threads we weave when designing powerful AI systems align with the intricate patterns of human values? This question lies at the heart of AI alignment , a field that seeks to harmonize the actions of AI systems with our own goals and interests.

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The MLOps Blog

FEBRUARY 22, 2024

Important note: Continual learning aims to allow the model to effectively learn new concepts while ensuring it does not forget already acquired information. Plenty of CL techniques exist that are useful in various machine-learning scenarios. The memory can be a database, a local file system, or just an object in RAM.

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Towards AI

JULY 23, 2023

Last Updated on July 25, 2023 by Editorial Team Author(s): Abhijit Roy Originally published on Towards AI. 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. Let’s see it step by step.

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IBM Journey to AI blog

AUGUST 1, 2023

True to their name, generative AI models generate text, images, code , or other responses based on a user’s prompt. It’s the underlying engine that gives generative models the enhanced reasoning and deep learning capabilities that traditional machine learning models lack.

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Heartbeat

FEBRUARY 8, 2024

Cross-modal retrieval frameworks have been developed through research using AI and CV. Cross-modal retrieval is a branch of computer vision and natural language processing that links visual and verbal descriptions.

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Pickl AI

JULY 17, 2023

Machine Learning is a subset of artificial intelligence (AI) that focuses on developing models and algorithms that train the machine to think and work like a human. Unsupervised Learning Algorithms Unsupervised Learning Algorithms tend to perform more complex processing tasks in comparison to supervised learning.

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ODSC - Open Data Science

FEBRUARY 14, 2023

Trained with 570 GB of data from books and all the written text on the internet, ChatGPT is an impressive example of the training that goes into the creation of conversational AI. ChatGPT is a next-generation language model (referred to as GPT-3.5) ChatGPT is a next-generation language model (referred to as GPT-3.5)

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AssemblyAI

SEPTEMBER 18, 2023

Few technological advancements have captured the imagination, curiosity, and application of experts and businesses quite like artificial intelligence (AI). However, among all the modern-day AI innovations, one breakthrough has the potential to make the most impact: large language models (LLMs). How Do Large Language Models Work?

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Google Research AI blog

NOVEMBER 28, 2022

Sara Mahdavi , Rapha Gontijo Lopes , Tim Salimans , Jonathan Ho , David J Fleet , Mohammad Norouzi EXPO Day Workshops Graph Neural Networks in Tensorflow: A Practical Guide Workshop Organizers include: Bryan Perozzi , Sami Abu-el-Haija A Hands-On Introduction to Tensorflow and Jax Workshop Organizers include: Josh Gordon Affinity Workshops LatinX in (..)

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AWS Machine Learning Blog

MAY 30, 2023

Gradient boosting is a supervised learning algorithm that attempts to accurately predict a target variable by combining an ensemble of estimates from a set of simpler and weaker models. He is passionate about technology and enjoys building and experimenting in the analytics and AI/ML space.

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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

AUGUST 15, 2023

Artificial intelligence (AI) adoption is here. Organizations are no longer asking whether to add AI capabilities, but how they plan to use this quickly emerging technology. While 42% of companies say they are exploring AI technology, the failure rate is high; on average, 54% of AI projects make it from pilot to production.

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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.

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AWS Machine Learning Blog

OCTOBER 4, 2023

Train an ML model on the preprocessed images, using a supervised learning approach to teach the model to distinguish between different skin types. Jake Wen is a Solutions Architect at AWS, driven by a passion for Machine Learning, Natural Language Processing, and Deep Learning.

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The MLOps Blog

MARCH 21, 2023

” — Conor Murphy , Lead Data Scientist at Databricks, in “Survey of Production ML Tech Stacks” at the Data+AI Summit 2022 Your team should be motivated by MLOps to show everything that goes into making a machine learning model, from getting the data to deploying and monitoring the model. Responsible AI and explainability.

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