Covers the basics of parallelism, including physical examples, historical context, multicore era, and parallel collections in Scala.
Covers the basics of parallel programming, including exploiting parallelism in algorithms and the importance of avoiding race conditions.
Explores futures as a structured approach to concurrency, simplifying parallel tasks and input-output operations.
Covers the basics of parallel programming, including concurrency, forms of parallelism, synchronization, and programming models like PThreads and OpenMP.
Covers the concept of deadlock in concurrent programming and provides solutions to prevent them.
Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.
