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Related lectures (32)

Page 2 of 4

Big Data Ecosystems: Technologies and Challenges

Covers the fundamentals of big data ecosystems, focusing on technologies, challenges, and practical exercises with Hadoop's HDFS.

Implementation Research: Needs Assessment

Emphasizes the importance of needs assessment in implementation research using various methods and data sources.

Bulky Hamiltonian isotopies

Explores the concept of bulky Hamiltonian isotopies of Lagrangian tori.

Distributed Computing: Challenges and Solutions

Explores challenges in distributed computing, data growth, and data types, emphasizing the battle against the three Vs in big data.

Deep Learning Accelerators: Optimization Strategies

Explores optimization strategies for deep learning accelerators, emphasizing data movement reduction through batching, dataflow optimizations, and compression.

Model Database Resolution

Covers model database resolution, hydrology, GIS, connections, transitions, and validation using various datasets.

Data Ingestion Layer: SmartDataLake

Explores the data ingestion process in SmartDataLake, including datasets and the RAW platform.

Unsupervised Learning: Clustering & Dimensionality Reduction

Introduces unsupervised learning through clustering with K-means and dimensionality reduction using PCA, along with practical examples.

River Hydraulics and Modeling: Semi-Distributed Approach

Explores river hydraulics, modeling, and calibration using a semi-distributed approach for accurate forecasting and water resource management.

Maximum Likelihood Inference

Explores maximum likelihood inference, comparing models based on likelihood ratios and demonstrating with a coin example.