AWS Machine Learning Blog

MARCH 28, 2024

Large language models (LLMs) are generally trained on large publicly available datasets that are domain agnostic. First, we observe that SEC filings contain noisy text due to the removal of tables and figures, so the authors remove short sentences that are deemed to be table or figure labels. billion words 5.1

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ODSC - Open Data Science

NOVEMBER 29, 2023

In late October, Google announced in a blog five new AI-powered features enhancing the popular map app in several different ways. Written by VP & General Manager, Geo Chris Phillips, the update lays out five new features that app users will likely enjoy. The first upgrade is the ability to see the world in 3D with Immersive View.

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Snorkel AI

OCTOBER 13, 2023

Cost optimization: Fine-tuned models achieve better results than generalized models. This blog is tailored for GPT-3.5-Turbo, Step 2: Labeling and fine-tuning After uploading data to Snorkel Flow, you can easily fine-tune a personalized version of GPT-3.5 Snorkel Flow users can export the labeled data and fine-tune GPT-3.5

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ODSC - Open Data Science

MARCH 8, 2024

Source ) In a blog post released today, OpenAI fired back at Elon Musk’s lawsuit and moved to dismiss his claims about the company’s motives. Source ) According to a report, Apple is hoping to push forward its efforts in generative AI in a bid to catch up with competitor Microsoft. Blogs or News to Share? MetaVoice-1B is a 1.2B

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AWS Machine Learning Blog

MARCH 30, 2023

In general, these requirements diverge substantially from the use cases of typical ML object detection models. Snapper consists of a SageMaker model backend as well as a front-end component that we integrate into the Ground Truth labeling UI. The use of Snapper in labeling tasks can significantly reduce cost and increase accuracy.

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AWS Machine Learning Blog

MAY 5, 2023

To create this app, they need a high-quality dataset containing clothing images, labeled with different categories. In this post, we show how to repurpose an existing dataset via data cleaning, preprocessing, and pre-labeling with a zero-shot classification model in FiftyOne , and adjusting these labels with Amazon SageMaker Ground Truth.

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Metadata Computer Vision Machine Learning Data Scientist 75

Flipboard

MAY 17, 2023

Best known as a robotics researcher, academic, and entrepreneur, Brooks is also an authority on AI: he directed the Computer Science and Artificial Intelligence Laboratory at MIT until 2007, and held faculty positions at Carnegie Mellon and Stanford before that. By the way, I recommend a long blog post by Stephen Wolfram. I’ve read it.

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Heartbeat

MARCH 5, 2024

Overview of Computer Vision and its Challenges Computer vision is a branch of computer science and artificial intelligence (AI) that focuses on giving computers the ability to comprehend and interpret visual data from pictures or movies. However, annotating vast datasets with accurate labels can be time-consuming and expensive.

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Google Research AI blog

FEBRUARY 7, 2023

In such a setting, a learner can sample examples one at a time, accessing both the context and true label, but in order to limit overhead costs, is only able to update its state (i.e., We provide a theoretical analysis, proving generalization and sampling rate bounds.

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AWS Machine Learning Blog

SEPTEMBER 1, 2023

Data science team – Data scientists need to focus on creating the best model based on predefined key performance indicators (KPIs) working in notebooks. Text-to-image – Labeled data, such as pairs of , has been used to train FMs, which are able to predict the best combination of pixels.

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Heartbeat

JANUARY 29, 2024

At its core, Meta-Learning equips algorithms with the aptitude to quickly grasp new tasks and domains based on past experiences, paving the way for unparalleled problem-solving skills and generalization abilities. The loss function quantifies the difference between the predicted outputs of our model and the ground truth labels.

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AWS Machine Learning Blog

AUGUST 16, 2023

This is a guest blog post co-written with Ben Veasey, Jeremy Anderson, Jordan Knight, and June Li from Travelers. This approach produces image representations that generalize well to unseen data and reduces the amount of labeled data required to build performant downstream models. The dataset has a size of about 109 GB.

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Metadata Data Scientist Generative AI Natural Language Processing 71

AWS Machine Learning Blog

APRIL 11, 2023

Streamlit is an open-source Python library that makes it easy to create and share web apps for ML and data science. As an example, we use a custom Amazon Rekognition demo, which will annotate and label an uploaded image. This will serve as a starting point, and it can be generalized to demo any custom ML model.

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ML Engineer ML Data Scientist Machine Learning 73

Pickl AI

AUGUST 1, 2023

In this blog, we will delve into the fundamental concepts of data model for Machine Learning, exploring their types. It is trained on a dataset comprising input features and corresponding target outputs, to generalize its knowledge to unseen data. Read Blog: Supervised learning vs Unsupervised learning 2.

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AWS Machine Learning Blog

FEBRUARY 6, 2024

We address this skew with generative AI models (Falcon-7B and Falcon-40B), which were prompted to generate event samples based on five examples from the training set to increase the semantic diversity and increase the sample size of labeled adverse events. Another column for the label class.

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Large Language Models BERT NLP Data Scientist 99

Heartbeat

JUNE 28, 2023

Cross-layer parameter sharing: ALBERT shares parameters across all layers of the model, allowing for more efficient use of parameters and better generalization. The idea is to use these soft targets instead of the hard labels that are typically used to train models. to(device) labels = batch[2].to(device)

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Heartbeat

FEBRUARY 21, 2024

However, using too many epochs can lead to overfitting, i.e., the model fits the training data very well, but the ability to generalize to new data is reduced. For example, when a neural network model tries to predict the label of an image, the loss function measures the accuracy of the model's predictions.

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Pickl AI

MARCH 10, 2023

Data Science is the process in which collecting, analysing and interpreting large volumes of data helps solve complex business problems. Effectively, Data Science job roles are increasing and have become one of the most critical career fields. Additionally, they must keep a log of all the activities to prevent duplication.

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AWS Machine Learning Blog

AUGUST 30, 2023

This solution involves fine-tuning the FLAN-T5 XL model, which is an enhanced version of T5 (Text-to-Text Transfer Transformer) general-purpose LLMs. T5 reframes natural language processing (NLP) tasks into a unified text-to-text-format, in contrast to BERT -style models that can only output either a class label or a span of the input.

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IBM Journey to AI blog

FEBRUARY 14, 2024

Using historical data of production, generative AI can predict or locate equipment failures in real time—and then suggest equipment adjustments, repair options or needed spare parts. Explore IBM® watsonx.ai™ The post The most valuable AI use cases for business appeared first on IBM Blog.

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Heartbeat

NOVEMBER 27, 2023

Training and Learning from Data Deep learning models are trained on large labeled datasets of medical images to learn the relationships between input images and their corresponding annotations. The limited availability of labeled medical images can hinder the development and generalizability of deep learning models.

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Heartbeat

OCTOBER 11, 2023

They can identify complex patterns that may not be apparent to humans, generating insights and discoveries. Generalization : Machine learning algorithms make predictions on fresh, untainted data by generalizing from the data they were trained on.

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Deep Learning Machine Learning Neural Network Natural Language Processing 52

Explosion

JANUARY 29, 2023

We’ve published several technical blog posts and reports, and created a bunch of new videos covering many tips and tricks to get the most out of our developer tools. Edi, Lj and team have written a comprehensive blog post covering full details of the spancat implementation as well as an architecture case study on nested NER.

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AWS Machine Learning Blog

OCTOBER 10, 2023

Trained on 680 thousand hours of labelled data, Whisper models demonstrate a strong ability to generalize to many datasets and domains without the need for fine-tuning. It was trained on 680 thousand hours of labelled speech data annotated using large-scale weak supervision. Model WER (percent) whisper-tiny 7.54 whisper-base 5.08

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Machine Learning OpenAI ML Algorithm 92

Heartbeat

NOVEMBER 15, 2023

To predict spam in new, unseen emails, the model must learn from labeled emails (some are marked as spam and others as non-spam). It is expressed as: P(A|B) = (P(B|A) * P(A)) / P(B) The Naive Bayes model can effectively classify or predict the class labels of new data instances by applying this principle.

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Heartbeat

JULY 20, 2023

By fine-tuning the model, we can leverage the model’s pre-training on general object segmentation to develop a more precise model. Limited availability of labeled datasets: In some domains, there is a scarcity of datasets with fine-grained annotations, making it difficult to train segmentation networks using supervised learning algorithms.

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Computer Vision Machine Learning Deep Learning Algorithm 52

Heartbeat

MARCH 28, 2023

Let’s start with PyTorch: PyTorch PyTorch Features ( Image Source ) Two frameworks are generally used for deep learning: TensorFlow and PyTorch. To follow along with the code in this blog, you can access the notebook I used in this project here. The samples and their related labels are stored in Dataset.

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Heartbeat

JULY 17, 2023

for batch_idx, (inputs, labels) in tqdm(enumerate(dataloaders[phase])): inputs = inputs.to(device) device) labels = labels.to(device) Editor’s Note: Heartbeat is a contributor-driven online publication and community dedicated to providing premier educational resources for data science, machine learning, and deep learning practitioners.

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Heartbeat

JANUARY 15, 2024

This ballet of convolutions allows deep learning models to generalize across various objects and scenes. Deep Learning and Large Datasets Deep learning models thrive on extensive datasets, and the availability of vast labeled datasets has been instrumental in nurturing the capabilities of neural networks.

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Deep Learning Computer Vision Neural Network Convolutional Neural Networks 52

Heartbeat

JULY 19, 2023

While it is acknowledged that the capacity to fine-tune models is not exclusive to Hugging Face or transformers in general, Hugging Face Transformers provide a user-friendly environment and a diverse selection of pre-trained models that can be effectively customized. You can read more about the text() package here.

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AWS Machine Learning Blog

APRIL 27, 2023

The model is scored correctly if it predicts “no error” or “correct” in the generation if the annotators labeled the example as having no error, or if it predicts any of the error types in the generation (along with “incorrect” or “wrong”) when the annotators labeled it as having an error.

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Machine Learning Large Language Models Categorization Natural Language Processing 65

ODSC - Open Data Science

JANUARY 12, 2024

Open Data Science Blog Recap: Baidu’s Ernie Bot Has More than 100 Million Users According to CTO According to the chief technology officer of Baidu, its ChatGPT-like chatbot, Ernie Bot has gained over 100 million users. GitHub Copilot Cha t now generally available for organizations and individuals.

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Large Language Models Robotics Chatbots AI 52

Heartbeat

FEBRUARY 21, 2023

It is employed to ascertain a speaker’s or writer’s feelings, attitudes, and opinions with regard to a particular subject or general contextual polarity of a work. This is often done by training a classifier on a labeled dataset of text, where each piece of text has been manually annotated with a sentiment label.

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Heartbeat

NOVEMBER 16, 2023

Often a reference label is provided to define the ideal or correct response. The requires_input and requires_reference attributes indicate whether the evaluator needs an input prompt and reference label, respectively. Criteria can optionally use reference labels to enable checking for factual correctness.

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AWS Machine Learning Blog

SEPTEMBER 20, 2023

With SageMaker Studio, you can access purpose-built tools for every stage of the ML development lifecycle, from data preparation to building, training, and deploying your ML models, improving data science team productivity by up to ten times. For more details, refer to Create or Open an Amazon SageMaker Studio Notebook. image and Python 3.0

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ML Python Deep Learning Machine Learning 101

Heartbeat

FEBRUARY 15, 2024

While deep learning models thrive on large labeled datasets, HOG’s feature extraction approach allows it to generalize across different object types. Additionally, HOG’s simplicity and computational efficiency make it a valuable contender for real-time applications, especially in scenarios where resource constraints are a concern.

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Mlearning.ai

FEBRUARY 24, 2023

In this blog post, we will explain what PCA is and how it works and present five examples of PCA analysis. PCA is an unsupervised learning technique that does not require labels or target variables. Data science refers to the number of features in a dataset. What is PCA? How Does Dimensionality Affect Models?

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