Machine learning (ML) often mimics how human brains operate by attaching virtual neurons with virtual synapses. Deep learning (DL) is a subset of ML putting steroids into the virtual brain and growing it orders of magnitude larger. This neuron count has skyrocketed hand-in-hand with the advances in computational power. Most headlines about ML solving hard problems like self-driving cars or facial recognition use DL, but the steroids come with a cost. Global warming is arguably the most critical problem our generation has to solve in the following years.

Topics and features: introduces dynamic programming and other fundamental algorithm design techniques, and investigates a wide selection of graph algorithms; compatible with the IOI Syllabus, yet also covering more advanced topics, such as maximum flows, Nim theory, and suffix structures; surveys specialized algorithms for trees, and discusses the mathematical topics that are relevant in competitive programming; reviews the features of the C programming language, and describes how to create efficient algorithms that can quickly process large data sets; discusses sorting algorithms and binary search, and examines a selection of data structures of the C standard library; covers such advanced algorithm design topics as bit-parallelism and amortized analysis, and presents a focus on efficiently processing array range queries; describes a selection of more advanced topics, including square-root algorithms and dynamic programming optimization.

Machine learning is a race. Those companies that can put machine learning models into production, on a large scale, first, will gain a huge advantage over their competitors and billions in potential revenue. But, there is a huge issue with the usability of machine learning -- there is a significant challenge around putting machine learning models into production at scale. Organisations can create incredibly complex machine learning models, but it's problematic to take huge datasets, apply them to different iterations of ML models and then deploy those successful iterations into production. Machine learning is becoming the phrase that data scientists hide from CVs, putting a data science model into production is the biggest data challenge, and companies are still not getting it.