Mlearning.ai

MARCH 9, 2023

Automated algorithms for image segmentation have been developed based on various techniques, including clustering, thresholding, and machine learning (Arbeláez et al., Understanding the robustness of image segmentation algorithms to adversarial attacks is critical for ensuring their reliability and security in practical applications.

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IBM Journey to AI blog

AUGUST 1, 2023

Foundation models: The driving force behind generative AI Also known as a transformer, a foundation model is an AI algorithm trained on vast amounts of broad data. An open-source model, Google created BERT in 2018. BERT (Bi-directional Encoder Representations from Transformers) is one of the earliest LLM foundation models developed.

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AI Impacts

FEBRUARY 1, 2023

A faulty brake line on a car is not much of a concern to the public until the car is on public roads, and the facebook feed algorithm cannot be a threat to society until it is used to control what large numbers of people see on their screens. But this model, on its own, is inadequate for AI, for reasons I will explain in the next section.

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Unite.AI

OCTOBER 1, 2024

What motivated you to launch SiLC, and what challenges did you set out to address when founding the company in 2018? For mobility, our technology detects objects hundreds of meters away while using motion to enable prediction-based algorithms for early reactions with immunity to multi-user interference.

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NVIDIA

AUGUST 29, 2024

Soon after those papers were published in 2017 and 2018, Kiela and his team of AI researchers at Facebook, where he worked at that time, realized LLMs would face profound data freshness issues. This type of optimization opens up edge use cases with smaller computers that can perform at significantly higher-than-expected levels.

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The Stanford AI Lab Blog

FEBRUARY 24, 2022

In this first post, we’ll analyze self-training , which is a very impactful algorithmic paradigm for semi-supervised learning and domain adaptation. In Part 2, we will use related theoretical ideas to analyze self-supervised contrastive learning algorithms, which have been very effective for unsupervised representation learning.

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AWS Machine Learning Blog

OCTOBER 5, 2023

Xin Huang is a Senior Applied Scientist for Amazon SageMaker JumpStart and Amazon SageMaker built-in algorithms. He focuses on developing scalable machine learning algorithms. From 2015–2018, he worked as a program director at the US NSF in charge of its big data program. He founded StylingAI Inc.,

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Pickl AI

JULY 24, 2024

Turing proposed the concept of a “universal machine,” capable of simulating any algorithmic process. The development of LISP by John McCarthy became the programming language of choice for AI research, enabling the creation of more sophisticated algorithms. Simon, demonstrated the ability to prove mathematical theorems.

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Flipboard

FEBRUARY 2, 2023

We then explain the details of the ML methodology and model training procedures. There are around 3,000 and 4,000 plays from four NFL seasons (2018–2021) for punt and kickoff plays, respectively. Models were trained and cross-validated on the 2018, 2019, and 2020 seasons and tested on the 2021 season.

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Machine Learning ML Big Data Data Scientist 123

AWS Machine Learning Blog

APRIL 3, 2023

Similarly, the nitrogen added as a fertilizer and the nitrogen leaching outcomes could be confounded as well, in the sense that a common cause can explain their association. In this crop yield study, the nitrogen added as fertilizer and the yield outcomes might be confounded. However, association is not causation.

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Heartbeat

MARCH 14, 2023

By incorporating computer vision methods and algorithms into robots, they are able to view and understand their environment. Object recognition and tracking algorithms include the CamShift algorithm , Kalman filter , and Particle filter , among others.

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Computer Vision Robotics Deep Learning Convolutional Neural Networks 97

Marek Rei

NOVEMBER 12, 2019

ArXiv 2018. EMNLP 2018. NAACL 2018. NAACL 2018. At the end, I also include the summaries for my own published papers since the last iteration (papers 61-74). Here we go. Improving Language Understanding by Generative Pre-Training Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever. Microsoft, Edinburgh.

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AWS Machine Learning Blog

JULY 13, 2023

Another way can be to use an AllReduce algorithm. For example, in the ring-allreduce algorithm, each node communicates with only two of its neighboring nodes, thereby reducing the overall data transfers. Train a binary classification model using the SageMaker built-in XGBoost algorithm. alpha – L1 regularization term on weights.

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Heartbeat

OCTOBER 18, 2023

By leveraging the power of neural networks, deep learning techniques breathe new life into recommendation algorithms, empowering them to handle complex data and surpass the limitations of their predecessors. Netflix’s movies and TV shows are recommended based on user ratings, viewing history, and platform interactions.

Deep Learning 52

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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.). Generative AI models usually have millions of neurons and billions of synapses (aka „ parameters “).

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Mlearning.ai

MARCH 2, 2023

Moreover, the most important theoretical foundations for BERT are explained and additional graphics are provided for illustration purposes. In particular, the architecture of transformers and the attention mechanism, as well as the idea behind unsupervised transfer learning, especially with fine-tuning, are explained. 2] Vaswani, A.,

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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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Computer Vision Deep Learning Neural Network Machine Learning 132

AWS Machine Learning Blog

FEBRUARY 10, 2023

We describe how we designed an accurate, explainable ML model to make coverage classification from player tracking data, followed by our quantitative evaluation and model explanation results. Quantitative evaluation We utilize 2018–2020 season data for model training and validation, and 2021 season data for model evaluation.

Computer Vision 78

Computer Vision Machine Learning ML Convolutional Neural Networks 78

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. Reptile is a meta-learning algorithm that falls under model-agnostic meta-learning approaches.

Machine Learning 52

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ML @ CMU

DECEMBER 29, 2023

These factors introduce noise that can affect hyperparameter tuning algorithms and lead to suboptimal model selection. However, FL is still vulnerable to post-hoc attacks where the public output of the FL algorithm (e.g. that are fed into an FL training algorithm (more details in the next section).

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AWS Machine Learning Blog

MAY 2, 2023

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). If you have a large dataset, the SageMaker KNN algorithm may provide you with an effective semantic search.

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Heartbeat

AUGUST 23, 2023

Consider a scenario where legal practitioners are armed with clever algorithms capable of analyzing, comprehending, and extracting key insights from massive collections of legal papers. Algorithms can automatically detect and extract key items. But what if there was a technique to quickly and accurately solve this language puzzle?

NLP 52

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Mlearning.ai

JUNE 8, 2023

Use algorithm to determine closeness/similarity of points. Vector Embeddings for Developers: The Basics | Pinecone Used geometry concept to explain what is vector, and how raw data is transformed to embedding using embedding model. Pinecone Used a picture of phrase vector to explain vector embedding. What are Vector Embeddings?

Machine Learning 52

Machine Learning BERT Neural Network OpenAI 52

deepsense.ai

APRIL 16, 2023

First, we will explain the MLP block. Source: [ 5 ] It runs GEMM with query ((W^Q)), key ((W^K)), and value weights ((W^V)) according to the previously explained partitioning in parallel. 2018 ) bridges the gap between reducing memory usage during training and maintaining good accuracy. Figure 13: FlashAttention algorithm.

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Large Language Models Neural Network Deep Learning Explainability 52

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. We pay our contributors, and we don’t sell ads.

BERT 52

BERT NLP Deep Learning Neural Network 52

PyImageSearch

NOVEMBER 13, 2023

One of the most popular deep learning-based object detection algorithms is the family of R-CNN algorithms, originally introduced by Girshick et al. Since then, the R-CNN algorithm has gone through numerous iterations, improving the algorithm with each new publication and outperforming traditional object detection algorithms (e.g.,

Convolutional Neural Networks 119

Convolutional Neural Networks Neural Network Computer Vision Deep Learning 119

Unite.AI

AUGUST 1, 2023

OpenAI has been instrumental in developing revolutionary tools like the OpenAI Gym, designed for training reinforcement algorithms, and GPT-n models. The fundamental working of React can be explained through an instance from HotpotQA, a task requiring high-order reasoning. This demonstrates a classic case of ‘knowledge conflict'.

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Prompt Engineering Prompt Engineer ChatGPT Convolutional Neural Networks 246

Viso.ai

JUNE 4, 2024

Point Cloud Processing Methods Computer vision algorithms provide point cloud processing functionalities that include: point cloud registration, shape fitting to 3-D point clouds , and the ability to read, write, store, display, and compare point clouds. The reason for that is the constraints of the undefined discriminator.

Computer Vision 52

Computer Vision Convolutional Neural Networks Deep Learning Neural Network 52

Viso.ai

JULY 17, 2024

Output from Neural Style Transfer – source Neural Style Transfer Explained Neural Style Transfer follows a simple process that involves: Three images, the image from which the style is copied, the content image, and a starting image that is just random noise. 2018) StarGAN extends GANs to multi-domain image translation.

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Computer Vision Deep Learning 52

Topbots

JUNE 22, 2023

Also, since at least 2018, the American agency DARPA has delved into the significance of bringing explainability to AI decisions. Outstandingly, ChatPGT presents such a capacity: it can explain its decisions. Outperforming algorithmic trading reinforcement learning systems: A supervised approach to the cryptocurrency market.

Machine Learning 107

Machine Learning ChatGPT Natural Language Processing Categorization 107

AI Impacts

APRIL 7, 2023

Every algorithm for evaluating the solution has some numerical error. Most of the systems we care about are considerably messier than the simple examples we use to explain chaos. 2018) [link]. There are many ways small uncertainties can arise: Every measuring device has a finite precision. The tyranny of the god scenario.

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PyImageSearch

JANUARY 2, 2023

Health startups and tech companies aiming to integrate AI technologies account for a large proportion of AI-specific investments, accounting for up to $2 billion in 2018 ( Figure 1 ). Machine learning algorithms can also recognize patterns in DNA sequences and predict a patient’s probability of developing an illness.

Computer Vision 52

Computer Vision Deep Learning Neural Network Machine Learning 52

Mlearning.ai

JANUARY 26, 2023

The meaning of this term is explained below. 2018, July). In IGARSS 2018–2018 IEEE International Geoscience and Remote Sensing Symposium (pp. Video Presentation of the B3 Project’s Data Cube. View the remaining presentations and full discussion forum here. Remote Sensing, 12(24), 4033. Giuliani, G., Chatenoux, B.,

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Heartbeat

DECEMBER 8, 2023

This includes cleaning and transforming data, performing calculations, or applying machine learning algorithms. LeCun received the 2018 Turing Award (often referred to as the "Nobel Prize of Computing"), together with Yoshua Bengio and Geoffrey Hinton, for their work on deep learning.

Convolutional Neural Networks 52

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PyImageSearch

JULY 3, 2023

Figure 1: Netflix Recommendation System (source: “Netflix Film Recommendation Algorithm,” Pinterest ). Netflix recommendations are not just one algorithm but a collection of various state-of-the-art algorithms that serve different purposes to create the complete Netflix experience. Each row has a title (e.g.,

Computer Vision 52

Computer Vision Deep Learning Algorithm Machine Learning 52

Snorkel AI

JANUARY 18, 2023

A 2018 study from McKinsey estimated the total global unclaimed value potential from AI/ML at $10-15 trillion, and Foundation Models will enable valuable applications that the firm couldn’t conceive of four years ago. Arora said that the approach yielded a lift in accuracy on all of the popular LLMs they tested on.

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Viso.ai

APRIL 18, 2024

Panoptic image segmentation was first introduced by Alexander Kirillov and his team in 2018. The classification of objects into “things” and “stuff” is crucial for panoptic image segmentation as it allows the algorithm to apply different strategies for segmenting and classifying these two types of entities.

Computer Vision 52

Computer Vision Robotics Deep Learning Algorithm 52