Technical notes
147 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,system_design)
- Data models and query languages (.md,system_design)
- Log segments with a hash index (.md,system_design)
- SSTables (.md,system_design)
- Data warehousing (.md,system_design)
- Encoding and evolution (.md,system_design)
- Single leader replication (.md,system_design)
- Multi-leader replication (.md,system_design)
- Leaderless replication (.md,system_design)
- Partitioning (.md,system_design)
- Batch processing (.md,system_design)
- Client-centric consistency models (.md,system_design)
- Hadoop Distributed File System (HDFS) (.md,system_design)
- Kafka (.md,system_design)
- Rate limiting (.md,system_design)
- 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)
- floyd_warshall (.py,data_structures_and_algorithms)
- dining_philosophers (.py,concurrency)
- hash_table (.py,data_structures_and_algorithms)
- hash_map_with_linked_list (.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)
- floyd_cycle_detection (.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)