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Related lectures (32)

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Landscape and Generalisation in Deep Learning

Explores the challenges and insights of deep learning, focusing on loss landscape, generalization, and feature learning.

Data Wrangling Techniques: HBase and Hive Integration

Covers data wrangling techniques using HBase and Hive, focusing on integration and practical applications.

Machine learning: Physics and Data

Delves into the intersection of physics and data in machine learning models, covering topics like atomic cluster expansion force fields and unsupervised learning.

Artificial Intelligence: The New Radical Addition to Work

Explores the convergence of IoT, Big Data, and Deep Learning in Artificial Intelligence applications across various industries.

Big Data Ecosystems: Technologies and Challenges

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

Data Science Essentials

Covers the essentials of data science, including data handling, visualization, and analysis, emphasizing practical skills and active engagement.

Deep Learning: Data, Models, and Challenges

Provides an overview of deep learning concepts, focusing on data, model architecture, and challenges in handling large datasets.

Introduction to Machine Learning: Course Overview and Basics

Introduces the course structure and fundamental concepts of machine learning, including supervised learning and linear regression.

Deep Learning: Principles and Applications

Covers the fundamentals of deep learning, including data, architecture, and ethical considerations in model deployment.

General Introduction to Big Data

Covers data science tools, Hadoop, Spark, data lake ecosystems, CAP theorem, batch vs. stream processing, HDFS, Hive, Parquet, ORC, and MapReduce architecture.