Towards AI

AUGUST 29, 2023

Introducing the backbone of Reinforcement Learning — The Markov Decision Process This member-only story is on us. Image by Ricardo Gomez Angel on Unsplash In most of my previous articles, I have mostly discussed Supervised Learning, with some sprinkling of elements of Unsupervised Learning.

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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

MAY 29, 2023

This article examines the important connection between QR codes and the domains of artificial intelligence (AI) and machine learning (ML), as well as how it affects the development of predictive analytics. Some of the methods used in ML include supervised learning, unsupervised learning, reinforcement learning, and deep learning.

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AssemblyAI

MAY 17, 2023

In this article, part of our Everything you need to know about Generative AI series, we will answer these questions and more. This article is designed to help all audiences understand these recent developments and how they find themselves situated in our lives. What is behind this recent wave of progress? Can we do better?

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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.

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Dataconomy

MAY 22, 2023

Distributed learning has emerged as a crucial technique for tackling complex problems and harnessing the power of large-scale data processing. But what exactly is distributed learning in machine learning? In this article, we will explore the concept of distributed learning and its significance in the realm of machine learning.

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Dataconomy

APRIL 18, 2023

In this article, we’ll explore some of the fundamental concepts in artificial intelligence, from supervised and unsupervised learning to bias and fairness in AI. Machine learning techniques can be broadly classified into three categories: supervised learning, unsupervised learning, and reinforcement learning.

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Becoming Human

MARCH 16, 2023

In this series, we will learn how to connect images and texts using a zero-shot classifier with hands-on examples. Here is part 2-Understanding zero-shot learning with clip model. How does Zero-Shot Learning work? Unseen Classes: These are the data classes on which the existing deep model needs to generalize.

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Data Science Dojo

SEPTEMBER 11, 2023

Evolution of language models: From inception to the present day These models have come a very long way since their birth, and their journey can be roughly divided into several generations, where some significant advancements were made in each generation.

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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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Hacker News

MAY 3, 2023

In this article we give a comprehensive overview of what’s really going on in the world of Language Models, building from the foundational ideas, all the way to the latest advancements. Fine-tuning may involve further training the pre-trained model on a smaller, task-specific labeled dataset, using supervised learning.

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Becoming Human

MARCH 16, 2023

In this article, we will explore how AI drug discovery is changing the industry. Since the advent of deep learning in the 2000s, AI applications in healthcare have expanded. The more layers of interconnected neurons a neural network has, the more “deep” it is. We will look at success stories, AI benefits, and limitations.

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Towards AI

JULY 23, 2023

In this article, we aim to focus on the development of one of the most powerful generative NLP tools, OpenAI’s GPT. Semi-Supervised Sequence Learning As we all know, supervised learning has a drawback, as it requires a huge labeled dataset to train. And we will also look at certain developments along the path.

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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. Machine Learning and Deep Learning One of the key components of the development of ChatGPT is machine learning.

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AssemblyAI

AUGUST 22, 2023

Using such data to train a model is called “supervised learning” On the other hand, pretraining requires no such human-labeled data. This process is called “self-supervised learning”, and is identical to supervised learning except for the fact that humans don’t have to create the labels.

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Dataconomy

MAY 25, 2023

By leveraging techniques like machine learning and deep learning, IoT devices can identify trends, anomalies, and patterns within the data. Here are some important machine learning techniques used in IoT: Supervised learning Supervised learning involves training machine learning models with labeled datasets.

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ODSC - Open Data Science

JULY 26, 2023

There are three main types of machine learning : supervised learning, unsupervised learning, and reinforcement learning. Supervised Learning In supervised learning, the algorithm is trained on a labelled dataset containing input-output pairs. predicting house prices).

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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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ODSC - Open Data Science

MAY 1, 2023

Given they’re built on deep learning models, LLMs require extraordinary amounts of data. MLOps can help organizations manage this plethora of data with ease, such as with data preparation (cleaning, transforming, and formatting), and data labeling, especially for supervised learning approaches.

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How to Learn Machine Learning

MAY 14, 2023

This is because machine learning has evolved into a driving force for various industries such as finance, healthcare, marketing, and many more. Machine learning engineers are in high demand, and the pursuit of a career in this field can be both personally and financially rewarding. The Machine Learning Engineer Career Path 1.

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Towards AI

JANUARY 10, 2024

How, the thing that we call Machine Learning, is different from today’s talk of the town, Generative AI? This article is about sharing some of those fundamental learnings. I will start with my understanding of two approaches to Machine Learning. They support Transfer Learning. What is Transfer Learning?

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How to Learn Machine Learning

MAY 2, 2025

Advanced Capabilities and Use Cases of Azure Machine Learning Handling Different Data Types Azure Machine Learning excels at working with various data types: Structured Data : Traditional tabular data can be processed using AutoML or custom models with frameworks like scikit-learn or XGBoost.

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ODSC - Open Data Science

MARCH 22, 2023

Cleanlab has been used to find millions of label errors in the most famous ML datasets: [link] In this Towards AI article , an XGBoost model was trained on a tabular dataset of student grades that had mislabeled examples, achieving 79% accuracy on a test set with validated labels.

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ODSC - Open Data Science

FEBRUARY 10, 2023

What worked best was an algorithm that combined two sub-fields of machine learning, supervised learning, and unsupervised learning. Originally posted on OpenDataScience.com Read more data science articles on OpenDataScience.com , including tutorials and guides from beginner to advanced levels!

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Becoming Human

MAY 15, 2023

You have to learn only those parts of technology that are useful in data science as well as help you land a job. Don’t worry; you have landed at the right place; in this article, I will give you a crystal clear roadmap to learning data science. Because this is the only effective way to learn Data Analysis.

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ODSC - Open Data Science

AUGUST 10, 2023

This perspective amalgamates language understanding and future prediction into a formidable self-supervised learning objective. You’ll get that at the ODSC West 2023 Deep Learning & Machine Learning Track. This strategy imparts the agent with a profound grasp of language semantics.

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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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AssemblyAI

SEPTEMBER 12, 2023

This article aims to answer the question: What is Automatic Speech Recognition (ASR)?, In 2014, Baidu published the paper, Deep Speech: Scaling up end-to-end speech recognition. In this paper, the researchers demonstrated the strength of applying Deep Learning research to power state-of-the-art, accurate speech recognition models.

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Heartbeat

JULY 6, 2023

Object detection is typically achieved through the use of deep learning models, particularly Convolutional Neural Networks (CNNs). In this article, you will learn about object detection through the SWIN Transformer. In this article, you will learn about object detection through the SWIN Transformer.

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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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ODSC - Open Data Science

JULY 12, 2024

The fields of AI and data science are changing rapidly and ODSC West 2024 is evolving to ensure we keep you at the forefront of the industry with our all-new tracks, AI Agents , What’s Next in AI, and AI in Robotics , and our updated tracks NLP, NLU, and NLG , and Multimodal and Deep Learning , and LLMs and RAG.

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Dataconomy

MARCH 3, 2023

As machine learning algorithms continue to evolve, financial institutions will be able to develop even more accurate and sophisticated asset pricing models, giving them a competitive edge in the market. This will help financial institutions comply with regulations and improve trust in machine learning models.

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Pickl AI

JULY 10, 2024

In this fast-evolving field, continuous learning and upskilling are crucial for staying relevant and competitive. This article aims to guide readers in selecting the best AI and Machine Learning Courses to enhance their careers. Key Features: Core AI concepts: deep learning, Machine Learning, and neural networks.

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ODSC - Open Data Science

FEBRUARY 14, 2023

The model was fine-tuned to reduce false, harmful, or biased output using a combination of supervised learning in conjunction to what OpenAI calls Reinforcement Learning with Human Feedback (RLHF), where humans rank potential outputs and a reinforcement learning algorithm rewards the model for generating outputs like those that rank highly.

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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. In this article, we will explore the essential steps involved in creating AI and the tools and techniques required to build robust and reliable AI systems.

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Pickl AI

NOVEMBER 18, 2024

Summary: The blog provides a comprehensive overview of Machine Learning Models, emphasising their significance in modern technology. It covers types of Machine Learning, key concepts, and essential steps for building effective models. The global Machine Learning market was valued at USD 35.80 billion by 2031 at a CAGR of 34.20%.

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BAIR

APRIL 6, 2023

These robots use recent advances in deep learning to operate autonomously in unstructured environments. By pooling data from all robots in the fleet, the entire fleet can efficiently learn from the experience of each individual robot.

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