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https://rust-unofficial.github.io/patterns/
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https://smallcultfollowing.com/babysteps/
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https://lucumr.pocoo.org/
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https://www.lpalmieri.com/
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https://blog.yoshuawuyts.com/
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https://www.i-programmer.info/babbages-bag/
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https://without.boats/blog/
- tokio
- impl riker actor for each socket connection
- libp2p pub/sub stack based on tokio tcp and udp with borsh and serde
- zmq pub/sub socket for async I/O event streaming like audio and video
- rpc capnp
- hyper
- juniper gql
- mailbox to receive async messages from other actors or other functions inside different part of the app
- pub/sub channels for broadcasting, executing and scheduling async tasks using tokio cron scheduler
- tokio worker green threadpool to run task in other threads using
tokio::spawn(async move{}) - rpc capnp based communication with outside world actors to call each other methods directly
- tokio message queue channles like mpsc and oneshot for sharing Arc<Mutex>: Send + Sync + 'static between threads and different parts of the app
- tokio event loop using
tokio::select!{}to select an async I/O event task
- distributed (replication and sharding) and decentralized concepts:
- search, db and routing engines like elastic, cassandra and p2p kademlia:
- create best objective function to find the most rewarded (less cost actions) path in the network graph env (route planning) greedily using:
- hybrid tech algorithms like NN, GA 🧬 and neurofuzzy(ANFIS)
- gradient optimization methods like stochastic gradient descent
- none gradient optimization methods like GA and FA
- graph theory and heuristic search algorithms like DAG, dijkstras, floyd, bellman, DFS, BFS and A*
- reinforcement learning algorithms like qlearning using mdp and bellman equation with off and on policy methods based on markov decision process and markov chain
- other algorithms using greedy, dynamic programming, backtracking, divide and conquer, recursive and brute forcing methods
- hashmap based algos like hash tables (DHT) to find closest peers to a specific range of key inside a replication like cassandra db and p2p nodes
- rpc capnp for actor method based communication on two different machines like calling between smart contract actors
- tokio tcp and udp and jobq channels for sharing Arc<Mutex>: Send + Sync + 'static between actor threads in a same machine
- actor tokio worker green threadpool and message queue for task and method broadcasting and scheduling to the pub/sub channels (tokio jobq, socket or rpc)
- pub/sub and other zmq socket actors to broadcast from publishers to subscribers (m2m) using sockets
- in libp2p peers can find each other using either mDNS (over LAN) or kademlia (over WAN)
- in libp2p peers can communicate with each other based on pub/sub floodsub or gossipsub protocols on top of rpc capnp, tokio udp and tcp, websocket, webrtc, zmq like sockets (req/res, cli/srv or pub/sub) actors
- proxy, firewall, vpns, packet sniffer and load balancer like pingora, HAproxy, v2ray and wireshark for all layers concepts:
- v2ray and tor protocols
- decompress encoded packet
- cpu task scheduling,
- weighted round robin dns,
- vector clock,
- event loop
- iptables
- zmq pub/sub
- simd divide and conquer based vectorization
- compiler, vm using llvm and os
- streaming of async I/O events compression and ram (buffer) algos like deflate, lz4 and snappy
- data serialization codec and protocols like borsh, bson, serde, capnp and gql
- runtime
- video and audio codec, compressor and streamer like ffmpeg
- cryptography
- bridge between blockchains
- language binding like writing rust code on top of apis of the compiled (.so) code in c like rust bindings to libsodium
- health improvement based on a pattern mining approach and semantic attributes like
- predicting missing part of the unseen input based on the exact style of the input
- reconstructing and labeling the unseen input based on the exact style of the input
- feature or representation learning and extracting from the data itself
- feature or representation selection using dimensionality reduction algorithms like GA, TSNE, PCA & VAE
- extracting semantic attributes from the input for 0/1/few/n-shot learning
- autoregressive based sequence modeling to predict the future based on past events and behaviors
- anomaly detection, 0/1/few/n-shot and meta learning
- solving catastrophic forgetting problem using MemTransformer instead of MANN with NTM architecture LSTM based
- FF algorithm as an alternative to gradient descent
- Transformers (https://arxiv.org/abs/2101.12037)
- SEER (https://arxiv.org/pdf/2103.01988)
- VAE & GAN
- BART (https://arxiv.org/pdf/1910.13461)
- VISSL (https://vissl.readthedocs.io/en/v0.1.5/)
- GNN (Graph Neural Network)
- MemTransformer (https://arxiv.org/pdf/2006.11527.pdf)
- Decision Transformer (https://arxiv.org/pdf/2106.01345v1.pdf)
- TransZero (https://arxiv.org/abs/2112.08643)
- DALLE-2 (https://arxiv.org/abs/2204.06125)
- CLIP (https://arxiv.org/abs/2103.00020)
- DayDreamer (https://arxiv.org/abs/2206.14176)
- Algorithm Distillation (https://arxiv.org/abs/2210.14215)
- You Only Live Once (https://arxiv.org/abs/2210.08863)
- Forward-Forward Algorithm (https://www.cs.toronto.edu/~hinton/FFA13.pdf)
- 🛠️ preprocessing
- tokenization
- Lexicon Normalization(stemming & lemmatization)
- lowercasing
- stopwwords removal
- padding
- 📄 vocabulary building, word embedding, vectorization, feature extraction and semantic analysis
- BOW(ngram based with TF-IDF normalization)
- Word2Vec(skip-gram & CBOW based)
- GloVe
- GPT
- BERT
- BART
- 📈 models and statistical results
- ML models like SVM, Naive Bayesian, Random Forest and Logistic Regression
- DL models like LSTM(MLP+CNN), Transformers and Attentions
- classification confusion matrix