ODSC - Open Data Science

SEPTEMBER 21, 2023

Secondly, to be a successful ML engineer in the real world, you cannot just understand the technology; you must understand the business. Machine learning is ideal for cases when you want to do a semi-routine task faster, with more accuracy, or at a far larger scale than is possible with other solutions.

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Heartbeat

MARCH 14, 2023

Some of the methods used for scene interpretation include Convolutional Neural Networks (CNNs) , a deep learning-based methodology, and more conventional computer vision-based techniques like SIFT and SURF. A combination of simulated and real-world data was used to train the system, enabling it to generalize to new objects and tasks.

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Flipboard

JULY 10, 2023

By using our mathematical notation, the entire training process of the autoencoder can be written as follows: Figure 2 demonstrates the basic architecture of an autoencoder: Figure 2: Architecture of Autoencoder (inspired by Hubens, “Deep Inside: Autoencoders,” Towards Data Science , 2018 ). Or requires a degree in computer science?

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Towards AI

JULY 20, 2023

It’s also an area that stands to benefit most from automated or semi-automated machine learning (ML) and natural language processing (NLP) techniques. Over the past several years, researchers have increasingly attempted to improve the data extraction process through various ML techniques. This study by Bui et al.

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Mlearning.ai

MARCH 9, 2023

Recent studies have demonstrated that deep learning-based image segmentation algorithms are vulnerable to adversarial attacks, where carefully crafted perturbations to the input image can cause significant misclassifications (Xie et al., 2018; Sitawarin et al., 2018; Papernot et al., 2018; Papernot et al.,

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

These activities cover disparate fields such as basic data processing, analytics, and machine learning (ML). ML is often associated with PBAs, so we start this post with an illustrative figure. The ML paradigm is learning followed by inference. The union of advances in hardware and ML has led us to the current day.

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Mlearning.ai

MAY 5, 2023

It wasn’t until the development of deep learning algorithms in the 2000s and 2010s that LLMs truly began to take shape. Deep learning algorithms are designed to mimic the structure and function of the human brain, allowing them to process vast amounts of data and learn from that data over time.

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Hacker News

OCTOBER 15, 2024

Grand Teton is designed with compute capacity to support the demands of memory-bandwidth-bound workloads, such as Meta’s deep learning recommendation models ( DLRMs ), as well as compute-bound workloads like content understanding.

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Mlearning.ai

APRIL 8, 2023

In this article, we’ll look at the evolution of these state-of-the-art (SOTA) models and algorithms, the ML techniques behind them, the people who envisioned them, and the papers that introduced them. 2017) “ BERT: Pre-training of deep bidirectional transformers for language understanding ” by Devlin et al.

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Towards AI

JULY 4, 2024

If you are good with Python, AI, ML, APIs, py-cord, or setting up a machine/server, connect with him in the Discord thread! Introduced in 2018, BERT has been a topic of interest for many, with many articles and YouTube videos attempting to break it down. Want to Learn Quantization in The Large Language Model?

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Mlearning.ai

JANUARY 17, 2024

The foundations for today’s generative language applications were elaborated in the 1990s ( Hochreiter , Schmidhuber ), and the whole field took off around 2018 ( Radford , Devlin , et al.). Complex ML problems can only be solved in neural networks with many layers. Deep learning neural network. No, no, no!

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Heartbeat

MARCH 15, 2023

An open-source machine learning model called BERT was developed by Google in 2018 for NLP, but this model had some limitations, and due to this, a modified BERT model called RoBERTa (Robustly Optimized BERT Pre-Training Approach) was developed by the team at Facebook in the year 2019. The library can be installed via pip.

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Heartbeat

MARCH 22, 2023

ML models use loss functions to help choose the model that is creating the best model fit for a given set of data (actual values are the most like the estimated values). Train Your Own YoloV7 Object Detection Model — by Gourav Bais Object detection is one of the most important concepts in the deep learning space.

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Heartbeat

DECEMBER 8, 2023

He focused on generative AI trained on large language models, The strength of the deep learning era of artificial intelligence has lead to something of a renaissance in corporate R&D in information technology, according to Yann LeCun, chief AI. Hinton is viewed as a leading figure in the deep learning community.

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AWS Machine Learning Blog

NOVEMBER 8, 2023

The MONAI AI models and applications can be hosted on Amazon SageMaker , which is a fully managed service to deploy machine learning (ML) models at scale. When there are no requests to process, this deployment option can downscale the instance count to zero for cost savings, which is ideal for medical imaging ML inference workloads.

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Mlearning.ai

FEBRUARY 10, 2023

For example, if you are using regularization such as L2 regularization or dropout with your deep learning model that performs well on your hold-out-cross-validation set, then increasing the model size won’t hurt performance, it will stay the same or improve. In the big data and deep learning era, now you have much more flexibility.

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AWS Machine Learning Blog

APRIL 6, 2023

& AWS Machine Learning Solutions Lab (MLSL) Machine learning (ML) is being used across a wide range of industries to extract actionable insights from data to streamline processes and improve revenue generation. We trained three models using data from 2011–2018 and predicted the sales values until 2021.

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Heartbeat

FEBRUARY 6, 2023

During training, the model is presented with an image with its own real labels and learns to predict the class and position of each object in the image, as well as the corresponding mask. When you’re working on an enterprise scale, managing your ML models can be tricky. YOLOv3 is a newer version of YOLO and was released in 2018.

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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). The types of land cover in each image, such as pastures or forests, are annotated according to 19 labels.

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AWS Machine Learning Blog

FEBRUARY 10, 2023

Through a collaboration between the Next Gen Stats team and the Amazon ML Solutions Lab , we have developed the machine learning (ML)-powered stat of coverage classification that accurately identifies the defense coverage scheme based on the player tracking data. Each season consists of around 17,000 plays.

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PyImageSearch

JULY 3, 2023

Figure 3: Netflix personalized home page view (source: “NETFLIX System Design,” Medium , 2018 ). Machine Learning for Page Generation A good utility function that checks the relevance of a row is the core of building a personalized home page. Each row has a title (e.g., New releases, Because you watched X, Continue watching, etc.)

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Mlearning.ai

JUNE 16, 2023

The common practice for developing deep learning models for image-related tasks leveraged the “transfer learning” approach with ImageNet. ML practitioners, believing they had to match the sheer size of ImageNet, refrained from pre-training with much smaller available medical image datasets, let alone developing new ones.

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AWS Machine Learning Blog

APRIL 3, 2023

The accomplishments of deep learning are essentially just a type of curve fitting, whereas causality could be used to uncover interactions between the systems of the world under various constraints without testing hypotheses directly. Note that this solution is currently available in the US West (Oregon) Region only.

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Heartbeat

SEPTEMBER 13, 2023

For example, explainability is crucial if a healthcare professional uses a deep learning model for medical diagnoses. Captum allows users to explain both deep learning and traditional machine learning models. Explainability in Machine Learning || Seldon Blazek, P. Russell, C. & & Watcher, S.

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AWS Machine Learning Blog

JANUARY 13, 2023

To mitigate these challenges, we propose a federated learning (FL) framework, based on open-source FedML on AWS, which enables analyzing sensitive HCLS data. It involves training a global machine learning (ML) model from distributed health data held locally at different sites. Import the data loader into the training script.

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AWS Machine Learning Blog

JULY 13, 2023

Recent years have shown amazing growth in deep learning neural networks (DNNs). International Conference on Machine Learning. PMLR, 2018. [2] On large-batch training for deep learning: Generalization gap and sharp minima.” Toward understanding the impact of staleness in distributed machine learning.”

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AWS Machine Learning Blog

SEPTEMBER 23, 2024

Since its launch in 2018, Just Walk Out technology by Amazon has transformed the shopping experience by allowing customers to enter a store, pick up items, and leave without standing in line to pay. Learn more about how to power your store or venue with Just Walk Out technology by Amazon on the Just Walk Out technology product page.

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Google Research AI blog

FEBRUARY 28, 2023

Adherence to such public health programs is a prevalent challenge, so researchers from Google Research and the Indian Institute of Technology, Madras worked with ARMMAN to design an ML system that alerts healthcare providers about participants at risk of dropping out of the health information program. certainty when used correctly.

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Mlearning.ai

MARCH 2, 2023

Conclusion: BERT as Trend-Setter in NLP and Deep Learning References I. Preliminaries: Transformers and Unsupervised Transfer Learning This section presents the most important theoretical background to understand BERT. BERT: Pre-training of deep bidirectional transformers for language understanding. Benchmark Results V.

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Mlearning.ai

MAY 8, 2023

A comprehensive step-by-step guide with data analysis, deep learning, and regularization techniques Introduction In this article, we will use different deep-learning TensorFlow neural networks to evaluate their performances in detecting whether cell nuclei mass from breast imaging is malignant or benign.

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Mlearning.ai

JULY 31, 2023

Things become more complex when we apply this information to Deep Learning (DL) models, where each data type presents unique challenges for capturing its inherent characteristics. Yin and Shen (2018) accompany their research with a code implementation on GitHub here. Likewise, sound and text have no meaning to a computer.

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Mlearning.ai

APRIL 27, 2023

Using Deep Learning Models to Detect Abusive Behavior in Video Games Abusive behaviors are a pervasive issue in numerous business settings, including credit card companies, video games, and promotional events. To address such abusive behaviors, there exist various deep learning models that can effectively mitigate this problem.

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Snorkel AI

MARCH 21, 2023

Together with David Harvey, an engagement manager focused on scaling deployments and applied R&D at that same firm, they presented the session “Trends in Enterprise ML and the potential impact of Foundation Models” at Snorkel AI’s 2023 Foundation Model Virtual Summit. Our ML protocols need updating in several ways.

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Mlearning.ai

FEBRUARY 19, 2023

It combines techniques from computational linguistics, probabilistic modeling, deep learning to make computers intelligent enough to grasp the context and the intent of the language. GPT-3 is a successor to the earlier GPT-2 (released in Feb 2019) and GPT-1 (released in June 2018) models .

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Heartbeat

JANUARY 29, 2024

Everything You Need to Know About Few-Shot Learning || Paperspace Blog Model-Agnostic Meta-Learning (MAML) Made Simple || InstaDeep Nichol, A. & 2018) Reptile: A scalable meta-learning algorithm || OpenAI.com Xavier L. GPU) support_set = support_set.to(device) device) query_set = query_set.to(device)

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Towards AI

JULY 21, 2023

It uses the 2 model architecture: sparse search via Elasticsearch and then a ranker ML model. chiahsuan156/ODSQA This repository contains dataset for the IEEE SLT 2018 paper: ODSQA: OPEN-DOMAIN SPOKEN QUESTION ANSWERING DATASET… github.com Every Sunday we do a weekly round-up of NLP news and code drops from researchers around the world.

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AWS Machine Learning Blog

MAY 2, 2023

JumpStart is a machine learning (ML) hub that can help you accelerate your ML journey. There are a few limitations of using off-the-shelf pre-trained LLMs: They’re usually trained offline, making the model agnostic to the latest information (for example, a chatbot trained from 2011–2018 has no information about COVID-19).

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