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

Deep Learning Neural Network Machine Learning Natural Language Processing 52

AWS Machine Learning Blog

MARCH 9, 2023

As machine learning (ML) models have improved, data scientists, ML engineers and researchers have shifted more of their attention to defining and bettering data quality. This has led to the emergence of a data-centric approach to ML and various techniques to improve model performance by focusing on data requirements.

ML Engineer 98

ML Engineer Data Scientist Convolutional Neural Networks ML 98

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.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Python Neural Network 52

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

Machine Learning ML Neural Network Convolutional Neural Networks 52

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.

Neural Network 52

Neural Network Deep Learning Machine Learning Artificial Intelligence 52

Flipboard

JULY 31, 2023

Nowadays, with the advent of deep learning and convolutional neural networks, this process can be automated, allowing the model to learn the most relevant features directly from the data. Model Training: With the labeled data and identified features, the next step is to train a machine learning model.

Machine Learning 123

Machine Learning Computer Vision Deep Learning Convolutional Neural Networks 123

Heartbeat

OCTOBER 12, 2023

These models, powered by massive neural networks, have catalyzed groundbreaking advancements in natural language processing (NLP) and have reshaped the landscape of machine learning. They owe their success to many factors, including substantial computational resources, vast training data, and sophisticated architectures.

Large Language Models 59

Large Language Models Neural Network Convolutional Neural Networks Artificial Intelligence 59

NVIDIA

FEBRUARY 7, 2024

The Public Sector Drives Research, Delivers Improved Citizen Services Data is playing an increasingly important role in government services, including for public health and disease surveillance, scientific research, social security administration, and extreme-weather monitoring and management.

Robotics 118

Robotics Convolutional Neural Networks AI AI 118

Pickl AI

NOVEMBER 28, 2024

Linear Algebra Linear algebra is fundamental for Machine Learning, especially in understanding how models process data. For example, in neural networks, data is represented as matrices, and operations like matrix multiplication transform inputs through layers, adjusting weights during training.

Machine Learning 52

Machine Learning Neural Network ML Engineer Algorithm 52

Viso.ai

SEPTEMBER 2, 2024

Experience with classical computer vision tools, such as OpenCV , object detection, image segmentation, data annotation, etc. Verifying and validating annotations to maintain high data quality and reliability. Good understanding of spatial data, 2D and 3D geometry, and coordinate systems.

Computer Vision 52

Computer Vision Software Engineer Convolutional Neural Networks Neural Network 52

Viso.ai

MAY 14, 2024

Such image fusion will provide higher reliability, accuracy, and data quality. Convolutional Neural Network Zhang et al. In many computer vision applications (e.g. robot motion and medical imaging) there is a need to integrate relevant information from multiple images into a single image.

Computer Vision 52

Computer Vision Robotics Convolutional Neural Networks Neural Network 52

The MLOps Blog

APRIL 20, 2023

Regularization techniques: experiment with weight decay, dropout, and data augmentation to improve model generalization. Managing data quality and quantity : managing data quality and quantity is crucial for training reliable CV models.

Computer Vision 64

Computer Vision Auto-classification Neural Network Convolutional Neural Networks 64

Heartbeat

OCTOBER 25, 2023

The following are some critical challenges in the field: a) Data Integration: With the advent of high-throughput technologies, enormous volumes of biological data are being generated from diverse sources. Deep learning, a subset of machine learning, has revolutionized image analysis in bioinformatics.

Machine Learning 52

Machine Learning Data Scientist Convolutional Neural Networks Algorithm 52

Viso.ai

OCTOBER 10, 2024

Decision Trees and Random Forests: Segmentation of urban landscapes using satellite imagery and geospatial data is an important part of land use planning and resource allocation and these machine learning algorithms can handle it. Recurrent Neural Networks and LSTM: These ML algorithms analyze time-series data.

Computer Vision 52

Computer Vision Machine Learning Algorithm Convolutional Neural Networks 52

Heartbeat

SEPTEMBER 25, 2023

Convolutional Neural Networks (CNNs), a machine learning algorithm, has successfully identified and classified abnormalities, lesions, tumors, and other critical features in medical images such as X-rays, MRIs, CT scans, and mammograms.

Machine Learning 52

Machine Learning Algorithm Data Scientist Automation 52

Viso.ai

JUNE 24, 2024

These models usually use a classification algorithm like a Convolutional Neural Network (CNN) or a multimodal architecture. Data Collection and Annotation Deep learning models are highly dependent on data quality and volume.

Computer Vision 40

Computer Vision Convolutional Neural Networks AI AI 40

Viso.ai

JANUARY 4, 2024

At its core, Semantic Segmentation is driven by deep learning models , particularly Convolutional Neural Networks (CNNs) , acting as an encoder and decoder. This is seen with a higher overhead in terms of training data quality (and quantity), an increased complexity of implementation, and additional computational cost.

Computer Vision 52

Computer Vision Convolutional Neural Networks Deep Learning Neural Network 52