Heartbeat

AUGUST 21, 2023

In this guide, we’ll talk about Convolutional Neural Networks, how to train a CNN, what applications CNNs can be used for, and best practices for using CNNs. What Are Convolutional Neural Networks CNN? CNNs are artificial neural networks built to handle data having a grid-like architecture, such as photos or movies.

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Marktechpost

SEPTEMBER 30, 2024

Unlike large-scale models like ESM2 and ProGen2, AMPLIFY focuses on improving data quality rather than model size, achieving superior performance with 43 times fewer parameters. The team evaluated three strategies—data quality, quantity, and training steps—finding that improving data quality alone can create state-of-the-art models.

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Data Quality Neural Network Natural Language Processing Large Language Models 61

Towards AI

FEBRUARY 11, 2025

Beyond Scale: Data Quality for AI Infrastructure The trajectory of AI over the past decade has been driven largely by the scale of data available for training and the ability to process it with increasingly powerful compute & experimental models. Scale matters, but quality matters more. The key insight?

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Towards AI

OCTOBER 3, 2024

Liquid Neural Networks: Research focuses on developing networks that can adapt continuously to changing data environments without catastrophic forgetting. These networks excel at processing time series data, making them suitable for applications like financial forecasting and climate modeling.

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Marktechpost

JUNE 27, 2023

Since the discovery of the Transformer design, the art of training massive artificial neural networks has advanced enormously, but the science underlying this accomplishment is still in its infancy. In this paper, they investigate how the data quality might be improved along a different axis.

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IBM Journey to AI blog

NOVEMBER 29, 2023

It automatically identifies vulnerable individual data points and introduces “noise” to obscure their specific information. Although adding noise slightly reduces output accuracy (this is the “cost” of differential privacy), it does not compromise utility or data quality compared to traditional data masking techniques.

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Towards AI

JUNE 12, 2024

Photo by david clarke on Unsplash The most recent breakthroughs in language models have been the use of neural network architectures to represent text. 2013 Word2Vec is a neural network model that uses n-grams by training on context windows of words. The more hidden layers an architecture has, the deeper the network.)

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Marktechpost

OCTOBER 11, 2024

Existing methods to address cold start in recommendation systems depend on heuristics to boost item rankings or use additional information to compensate for the lack of interaction data. Next, non-stationary distribution shifts are managed through periodic model retraining, which is costly and unstable due to varying data quality.

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Marktechpost

OCTOBER 16, 2024

One of the core areas of development within machine learning is neural networks, which are especially critical for tasks such as image recognition, language processing, and autonomous decision-making. These models are governed by scaling laws, suggesting that increasing model size and the amount of training data enhances performance.

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Marktechpost

MAY 8, 2024

In this framework, an agent, like a self-driving car, navigates an environment based on observed sensory data, taking actions to maximize cumulative future rewards. DRL models, such as Deep Q-Networks (DQN), estimate optimal action policies by training neural networks to approximate the maximum expected future rewards.

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Marktechpost

JANUARY 14, 2024

The researchers present a categorization system that uses backbone networks to organize these methods. Most picture deblurring methods use paired images to train their neural networks. The initial step is using a neural network to estimate the blur kernel. Two steps comprised the process of deblurring images.

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Marktechpost

APRIL 21, 2024

In contrast, the Inheritune, efficiently trains small base LMs by inheriting transformer blocks from larger models and training on a small subset of data, achieving comparable performance with significantly fewer computational resources. In the experiments, the researchers use a 1 billion token subset of the Redpajama v1 dataset to train a 1.5

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Marktechpost

JUNE 4, 2024

Traditional data pruning methods include simple rules-based filtering and basic classifiers to identify high-quality samples. Advanced techniques have emerged, utilizing neural network-based heuristics to assess data quality based on various metrics such as feature similarity or sample difficulty.

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Large Language Models Neural Network Machine Learning Data Quality 112

Marktechpost

OCTOBER 11, 2024

The core of Distilabel’s framework revolves around the GAN architecture, which includes two primary neural networks: a generator and a discriminator. The competitive dynamic between the two networks allows for continuous refinement of the synthetic data.

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Marktechpost

JULY 7, 2024

Reinforcement Learning Techniques: Deep Q Network (DQN): DQN combines Q-learning with deep neural networks to handle high-dimensional state spaces. The human-agent joint learning system provides a practical approach to reducing human workload while maintaining data quality, which is crucial for robot manipulation tasks.

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Marktechpost

OCTOBER 1, 2023

This is because, whereas the size of the convolutional kernel constrains convolutional neural networks (CNNs) and can only extract local information, self-attention can remove global information from the picture, delivering adequate and meaningful visual characteristics.

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The MLOps Blog

NOVEMBER 14, 2023

Example of a deep learning visualization: small convolutional neural network CNN, notice how the thickness of the colorful lines indicates the weight of the neural pathways | Source How is deep learning visualization different from traditional ML visualization? Let’s take a computer vision model as an example.

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Towards AI

FEBRUARY 20, 2024

If you want an overview of the Machine Learning Process, it can be categorized into 3 wide buckets: Collection of Data: Collection of Relevant data is key for building a Machine learning model. It isn't easy to collect a good amount of quality data. You need to know two basic terminologies here, Features and Labels.

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Unite.AI

OCTOBER 16, 2023

The underpinnings of LLMs like OpenAI's GPT-3 or its successor GPT-4 lie in deep learning, a subset of AI, which leverages neural networks with three or more layers. Training Data : The essence of a language model lies in its training data.

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Marktechpost

JULY 7, 2024

It consists of two separate neural networks: one for encoding user features and the other for encoding item features. While these systems enhance user engagement and drive revenue, they also present challenges like data quality and privacy concerns.

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Marktechpost

AUGUST 7, 2024

Tools like layer-wise relevance propagation can help visualize the elements of a neural network that contribute to specific outcomes, providing the necessary transparency. This includes considering patient population, disease conditions, and scanning quality.

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AssemblyAI

AUGUST 3, 2023

While such studies are still missing a full view of the landscape, they suggest that focusing on the data quality might be way more beneficial than prioritizing scalability when fine-tuning LLMs. Unraveling the exact scaling laws that govern the balance between demonstration data and RLHF or similar techniques (e.g.

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O'Reilly Media

NOVEMBER 19, 2024

One more embellishment is to use a graph neural network (GNN) trained on the documents. A generalized, unbundled workflow A more accountable approach to GraphRAG is to unbundle the process of knowledge graph construction, paying special attention to data quality.

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Marktechpost

NOVEMBER 18, 2024

Despite these technologies, challenges still need to be addressed, including limited breakthroughs in identifying new drug targets and data quality issues. VirtuDockDL’s integration of ligand- and structure-based screening provides efficient and accurate virtual screening.

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The MLOps Blog

JUNE 27, 2023

Data quality control: Robust dataset labeling and annotation tools incorporate quality control mechanisms such as inter-annotator agreement analysis, review workflows, and data validation checks to ensure the accuracy and reliability of annotations. Data monitoring tools help monitor the quality of the data.

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Marktechpost

SEPTEMBER 6, 2024

One intriguing potential benefit of model interpolation is its potential to enhance researchers’ understanding of the features of neural networks’ mode connectivity. The method of choice for model fusing in deep neural networks is coordinate-based parameter averaging.

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Pickl AI

JULY 12, 2024

Scikit-learn: A simple and efficient tool for data mining and data analysis, particularly for building and evaluating machine learning models. At the same time, Keras is a high-level neural network API that runs on top of TensorFlow and simplifies the process of building and training deep learning models.

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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. Deep learning models use artificial neural networks to learn from data.

Software Development 52

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Unite.AI

APRIL 26, 2024

To augment the data quality, the Mini-Gemini framework collects and produces more data based on public resources, including task-oriented instructions, generation-related data, and high-resolution responses, with the increased amount and enhanced quality improving the overall performance and capabilities of the model.

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Pickl AI

AUGUST 1, 2024

Summary : Deep Learning engineers specialise in designing, developing, and implementing neural networks to solve complex problems. They work on complex problems that require advanced neural networks to analyse vast amounts of data.

Deep Learning 52

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Pickl AI

OCTOBER 9, 2023

Data Management and Preprocessing for Accurate Predictions Data Quality is Paramount: The foundation of robust ML in demand forecasting lies in high-quality data. Retailers must ensure data is clean, consistent, and free from anomalies. Consistently review and purify data to uphold its accuracy.

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Google Research AI blog

MARCH 30, 2023

Though high-quality training datasets are vital to continued advancement in the field of ML, much of the data on which the field relies today is nearly a decade old (e.g., Despite the importance of data, ML research to date has been dominated by a focus on models. LAION or The Pile ).

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ODSC - Open Data Science

DECEMBER 5, 2023

On Thursday, Chelsea Finn, PhD, Assistant Professor at Stanford University discussed how sometimes neural networks can hallucinate and be quite incorrect, the repercussions, and how can we address these issues.

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AssemblyAI

SEPTEMBER 18, 2023

While LLMs offer potential advantages in terms of scalability and cost-efficiency, they also present meaningful challenges, especially concerning data quality, biases, and ethical considerations. They use neural networks that are inspired by the structure and function of the human brain. How Do Large Language Models Work?

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Mlearning.ai

NOVEMBER 29, 2023

Legacy Methods of Time Series Forecasting Recurrent Neural Network (RNN) Recurrent Neural Networks process a time series step-by-step, maintaining an internal state from time step to time step. Neural networks are great in this application as they can learn the temporal dependence from the given data.

Machine Learning 52

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Pickl AI

OCTOBER 2, 2024

Summary: Artificial Intelligence (AI) is revolutionising Genomic Analysis by enhancing accuracy, efficiency, and data integration. Despite challenges like data quality and ethical concerns, AI’s potential in genomics continues to grow, shaping the future of healthcare.

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Marktechpost

SEPTEMBER 19, 2024

UAT shows that neural networks can approximate any continuous function. In Transformer models, this principle is applied dynamically based on input data. Limitations in LLM reasoning are attributed to model size, data quality, and architecture.

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