Technical notes

• What is a computer? (.md,computer_architecture)
• Binary addition (.md,computer_architecture)
• Overflow (.md,computer_architecture)
• Two's complement (.md,computer_architecture)
• A Tour of Computer Systems (.md,computer_architecture)
• Transistors (.md,computer_architecture)
• Flip-flops (.md,computer_architecture)
• Synchronous systems (.md,computer_architecture)
• Assembly (.md,computer_architecture)
• C memory management (.md,computer_architecture)
• Computer chip (.md,computer_architecture)
• Floating point (.md,computer_architecture)
• Flynn's taxonomy (.md,computer_architecture)
• Introduction to operating systems (.md,operating_systems)
• Concurrency (.md,operating_systems)
• Multithreading (.md,operating_systems)
• OSTEP: Concurrency and Threads (.md,operating_systems,concurrency)
• OSTEP: Locks (.md,operating_systems,concurrency)
• OSTEP: Condition Variables (.md,operating_systems,concurrency)
• OSTEP: Semaphores (.md,operating_systems,concurrency)
• OSTEP: Locked data structures (.md,operating_systems,concurrency)
• OSTEP: Common Concurrency Problems (.md,operating_systems,concurrency)
• Basic comparison sorting (.ipynb,data_structures_and_algorithms)
• Non-comparison sorting (.ipynb,data_structures_and_algorithms)
• Merge sort (.ipynb,data_structures_and_algorithms)
• Quicksort (.ipynb,data_structures_and_algorithms)
• Dynamic programming (.ipynb,data_structures_and_algorithms)
• Heap (.ipynb,data_structures_and_algorithms)
• Trie (.ipynb,data_structures_and_algorithms)
• Backtracking (.md,data_structures_and_algorithms)
• Sliding window (.md,data_structures_and_algorithms)
• 0-1 Knapsack (.ipynb,data_structures_and_algorithms)
• Correctness proof (.md,data_structures_and_algorithms)
• What is machine learning? (.md,machine_learning)
• Bias-Variance trade-off (.md,machine_learning)
• Naive bayes (.md,machine_learning)
• Linear discriminant analysis (.md,machine_learning)
• K-means clustering (.md,machine_learning)
• Support vector machines (.md,machine_learning)
• Kernel trick (.md,machine_learning)
• Linear regression (.ipynb,machine_learning)
• Logistic regression (.ipynb,machine_learning)
• Softmax regression (.ipynb,machine_learning)
• MLP (.ipynb,machine_learning)
• Gradient descent (.md,deep_learning)
• Automatic differentiation (.md,deep_learning)
• Attention (.md,deep_learning)
• Graph neural networks (.md,deep_learning)
• Data parallelism (.md,deep_learning)
• T5-style model parallelism (.md,deep_learning)
• Pipeline parallelism (.md,deep_learning)
• ZeRO-Offload (.md,deep_learning)
• ZeRO-DP (.md,deep_learning)
• Megatron-style model parallelism (.md,deep_learning)
• Mixture of Experts (.md,deep_learning)
• Reinforcement learning framework (.md,reinforcement_learning)
• Reinforcement learning objective (.md,reinforcement_learning)
• Imitation learning (.md,reinforcement_learning)
• Problems with imitation learning (.md,reinforcement_learning)
• Policy gradients (.md,reinforcement_learning)
• Policy gradients vs. imitation learning (.md,reinforcement_learning)
• 3Blue1Brown's Linear Algebra series (.md,linear_algebra)
• Eigendecomposition (.md,linear_algebra)
• Singular value decomposition (.md,linear_algebra)
• Moore-Penrose pseudoinverse (.md,linear_algebra)
• PCA (.md,linear_algebra)
• Critical points (.md,optimization)
• Overflow and Underflow (.md,optimization)
• Probablity basics (.md,probability)
• Random variables (.md,probability)
• Reliable, maintainable and scalable applications (.md,distributed_systems)
• Data models and query languages (.md,distributed_systems)
• Log segments with a hash index (.md,distributed_systems)
• SSTables (.md,distributed_systems)
• Data warehousing (.md,distributed_systems)
• Encoding and evolution (.md,distributed_systems)
• Single leader replication (.md,distributed_systems)
• Multi-leader replication (.md,distributed_systems)
• Leaderless replication (.md,distributed_systems)
• Partitioning (.md,distributed_systems)
• Batch processing (.md,distributed_systems)
• Client-centric consistency models (.md,distributed_systems)
• Hadoop Distributed File System (HDFS) (.md,distributed_systems)
• Kafka (.md,distributed_systems)
• Selective sequence prediction (.md,robustness_and_uncertainty_for_structured_prediction)
• Data synthesis for language reward models (.md,robustness_and_uncertainty_for_structured_prediction)
• Structured prediction (.md,robustness_and_uncertainty_for_structured_prediction)
• Ensemble methods (.md,robustness_and_uncertainty_for_structured_prediction)
• Calibration definitions (.md,robustness_and_uncertainty_for_structured_prediction)
• Post-hoc calibration (.md,robustness_and_uncertainty_for_structured_prediction)
• Decoding strategies (.md,robustness_and_uncertainty_for_structured_prediction)
• Maximum Softmax Probability (.md,robustness_and_uncertainty_for_structured_prediction)
• Selective prediction (.md,robustness_and_uncertainty_for_structured_prediction)
• Modeling model outputs (.md,robustness_and_uncertainty_for_structured_prediction)
• OOD detection (.md,robustness_and_uncertainty_for_structured_prediction)
• Calibration for structured prediction (.md,robustness_and_uncertainty_for_structured_prediction)
• Broadcasting (.md,miscellaneous)
• How to solve it (.md,miscellaneous)
• Modular exponentiation (.md,miscellaneous)
• James-Stein estimation (.md,miscellaneous)
• Entropy (.md,miscellaneous)
• dot_product_attention (.py,deep_learning)
• autograd (.py,deep_learning)
• batch_norm (.py,deep_learning)
• bellman_ford (.py,data_structures_and_algorithms)
• binary_search (.py,data_structures_and_algorithms)
• binary_search_tree (.py,data_structures_and_algorithms)
• bit_manipulation (.py,data_structures_and_algorithms)
• bounded_blocking_queue (.py,concurrency)
• breadth_first_search (.py,data_structures_and_algorithms)
• categorical_crossentropy (.py,deep_learning)
• custom_queue (.py,data_structures_and_algorithms)
• dag_relaxation (.py,data_structures_and_algorithms)
• deadlock (.py,concurrency)
• depth_first_search_iterative (.py,data_structures_and_algorithms)
• depth_first_search_recursive (.py,data_structures_and_algorithms)
• dijkstra (.py,data_structures_and_algorithms)
• dining_philosophers (.py,concurrency)
• hash_table (.py,data_structures_and_algorithms)
• heap_sort_in_place (.py,data_structures_and_algorithms)
• kth_largest_value (.py,data_structures_and_algorithms)
• linked_list (.py,data_structures_and_algorithms)
• lru_cache (.py,data_structures_and_algorithms)
• producer_consumer (.py,concurrency)
• sieve_of_eratosthenes (.py,data_structures_and_algorithms)
• softmax (.py,deep_learning)
• stack (.py,data_structures_and_algorithms)
• thread_error (.py,concurrency)
• thread_pool_executor (.py,concurrency)
• thread_pool_executor_from_scratch (.py,concurrency)
• as_completed (.py,concurrency)
• web_crawler (.py,concurrency)
• web_crawler_concurrent_with_queue (.py,concurrency)
• DataLoader (.ipynb,concurrency)
• readers_writers_balanced (.py,concurrency)
• k_means (.py,machine_learning)
• MinHashLSH (.md,data_structures_and_algorithms,machine_learning)
• Needleman-Wunsch algorithm (.md,data_structures_and_algorithms,strings)
• Smith-Waterman algorithm (.md,data_structures_and_algorithms,strings)
• String matching (.ipynb,data_structures_and_algorithms,strings)
• union_find (.py,data_structures_and_algorithms)
• fenwick_tree (.py,data_structures_and_algorithms)
• external_merge_sort (.py,data_structures_and_algorithms)