[1] Data warehouse. https://un5necb5yb5rcmnrv6mj8.julianrbryant.com/learn/what-is-a-data-warehouse.
[2] Structured vs. unstructured data. https://un5ty71qpb5h6u5rxqywyjrjk0.julianrbryant.com/post/102gjab/machine-learning-libraries-for-tabular-data-problems.
[3] Bagging technique in ensemble learning. https://un5qgjbzw9dxcq3ecfxberhh.julianrbryant.com/wiki/Bootstrap_aggregating.
[4] Boosting technique in ensemble learning. https://un5mythmgjgh13x13w.julianrbryant.com/what-is/boosting/.
[5] Stacking technique in ensemble learning. https://un5ydfhhb3ev2qfr9ytw46zq.julianrbryant.com/stacking-ensemble-machine-learning-with-python/.
[6] Interpretability in Machine Mearning. https://un5h2c9ru75t0gpgzv9zy9j88c.julianrbryant.com/2020/08/31/6-interpretability/.
[7] Traditional machine learning algorithms. https://un5ydfhhb3ev2qfr9ytw46zq.julianrbryant.com/a-tour-of-machine-learning-algorithms/.
[8] Sampling strategies. https://un5gmtkzgjqu29q4q3t28.julianrbryant.com/methodology/sampling-methods/.
[9] Data splitting techniques. https://un5ydfhhb3ev2qfr9ytw46zq.julianrbryant.com/train-test-split-for-evaluating-machine-learning-algorithms/.
[10] Class-balanced loss. https://un5g9qc4gj7rc.julianrbryant.com/pdf/1901.05555.pdf.
[11] Focal loss paper. https://un5g9qc4gj7rc.julianrbryant.com/pdf/1708.02002.pdf.
[12] Focal loss. https://un5jf9grrxc0.julianrbryant.com/swlh/focal-loss-an-efficient-way-of-handling-class-imbalance-4855ae1db4cb.
[13] Data parallelism. https://un5gmtkzgkgem2vyhj5g.julianrbryant.com/2017/12/25/understanding-data-parallelism-in-machine-learning/.
[14] Model parallelism. https://un5n6892w35vj5dmhkx794rnk0.julianrbryant.com/sagemaker/latest/dg/model-parallel-intro.html.
[15] Cross entropy loss. https://un5qgjbzw9dxcq3ecfxberhh.julianrbryant.com/wiki/Cross_entropy.
[16] Mean squared error loss. https://un5qgjbzw9dxcq3ecfxberhh.julianrbryant.com/wiki/Mean_squared_error.
[17] Mean absolute error loss. https://un5qgjbzw9dxcq3ecfxberhh.julianrbryant.com/wiki/Mean_absolute_error.
[18] Huber loss. https://un5qgjbzw9dxcq3ecfxberhh.julianrbryant.com/wiki/Huber_loss.
[19] L1 and l2 regularization. https://un5gmtkzgjzya1zmztc9rx00k0.julianrbryant.com/blogs/l2-and-l1-regularization-machine-learning.
[20] Entropy regularization. https://un5qe8ze6ztwgfj3.julianrbryant.com/method/entropy-regularization.
[21] K-fold cross validation. https://un5qgjbzw9dxcq3ecfxberhh.julianrbryant.com/wiki/Cross-validation_(statistics).
[22] Dropout paper. https://uhm6mk8kzjmx6zm5.julianrbryant.com/papers/volume15/srivastava14a/srivastava14a.pdf.
[23] Overview of optimization algorithm. https://un5me0amwv5ju.julianrbryant.com/optimizing-gradient-descent/.
[24] Stochastic gradient descent. https://un5qgjbzw9dxcq3ecfxberhh.julianrbryant.com/wiki/Stochastic_gradient_descent.
[25] AdaGrad optimization algorithm. https://un5mu6vmryppd6n1hkh8a9kzd42g4hghjc.julianrbryant.com/index.php?title=AdaGrad.
[26] Momentum optimization algorithm. https://un5mu6vmryppd6n1hkh8a9kzd42g4hghjc.julianrbryant.com/index.php?title=Momentum.
[27] RMSProp optimization algorithm. https://un5mu6vmryppd6n1hkh8a9kzd42g4hghjc.julianrbryant.com/index.php?title=RMSProp.
[28] ELU activation function. https://un5pccfjefevwqnew3yveuf2cjb9rd2tvcx0.julianrbryant.com/en/latest/activation_functions.html#elu.
[29] ReLU activation function. https://un5pccfjefevwqnew3yveuf2cjb9rd2tvcx0.julianrbryant.com/en/latest/activation_functions.html#relu.
[30] Tanh activation function. https://un5pccfjefevwqnew3yveuf2cjb9rd2tvcx0.julianrbryant.com/en/latest/activation_functions.html#tanh.
[31] Sigmoid activation function. https://un5pccfjefevwqnew3yveuf2cjb9rd2tvcx0.julianrbryant.com/en/latest/activation_functions.html#softmax.
[32] FID score. https://un5qgjbzw9dxcq3ecfxberhh.julianrbryant.com/wiki/Fr%C3%A9chet_inception_distance.
[33] Inception score. https://un5qgjbzw9dxcq3ecfxberhh.julianrbryant.com/wiki/Inception_score.
[34] BLEU metrics. https://un5qgjbzw9dxcq3ecfxberhh.julianrbryant.com/wiki/BLEU.
[35] METEOR metrics. https://un5qgjbzw9dxcq3ecfxberhh.julianrbryant.com/wiki/METEOR.
[36] ROUGE score. https://un5qgjbzw9dxcq3ecfxberhh.julianrbryant.com/wiki/ROUGE_(metric).
[37] CIDEr score. https://un5g9qc4gj7rc.julianrbryant.com/pdf/1411.5726.pdf.
[38] SPICE score. https://un5g9qc4gj7rc.julianrbryant.com/pdf/1607.08822.pdf.
[39] Quantization-aware training. https://un5qex1awryd6zm5.julianrbryant.com/docs/stable/quantization.html.
[40] Model compression survey. https://un5g9qc4gj7rc.julianrbryant.com/pdf/1710.09282.pdf.
[41] Shadow deployment. https://un5x4nbkxjcwevxm3w.julianrbryant.com/machine%20learning/2019/03/30/deploying-machine-learning-applications-in-shadow-mode/.
[42] A/B testing. https://un5qgjbzw9dxcq3ecfxberhh.julianrbryant.com/wiki/A/B_testing.
[43] Canary release. https://un5h2c9ru75m974kq0pk2rk4ym.julianrbryant.com/cloud-native-patterns-canary-release-1cb8f82d371a.
[1] Visual search at pinterest. https://un5g9qc4gj7rc.julianrbryant.com/pdf/1505.07647.pdf.
[2] Visual embeddings for search at Pinterest. https://un5jf9grrxc0.julianrbryant.com/pinterest-engineering/unifying-visual-embeddings-for-visual-search-at-pinterest-74ea7ea103fo.
[3] Representation learning. https://un5qgjbzw9dxcq3ecfxberhh.julianrbryant.com/wiki/Feature_learning.
[4] ResNet paper. https://un5g9qc4gj7rc.julianrbryant.com/pdf/1512.03385.pdf.
[5] Transformer paper. https://un5g9qc4gj7rc.julianrbryant.com/pdf/1706.03762.pdf.
[6] Vision Transformer paper. https://un5g9qc4gj7rc.julianrbryant.com/pdf/2010.11929.pdf.
[7] SimCLR paper. https://un5g9qc4gj7rc.julianrbryant.com/pdf/2002.05709.pdf.
[8] MoCo paper. https://un5mvxtq0mphpenzzu9zbd8.julianrbryant.com/content_CVPR_2020/papers/He_Momentum_Contrast_for_Unsupervised_Visual_Representation_Learning_CVPR_2020_paper.pdf.
[9] Contrastive representation learning methods. https://un5xtc14ffrx6vwhy3c869mu.julianrbryant.com/posts/2019-11-10-self-supervised/.
[10] Dot product. https://un5qgjbzw9dxcq3ecfxberhh.julianrbryant.com/wiki/Dot_product.
[11] Cosine similarity. https://un5qgjbzw9dxcq3ecfxberhh.julianrbryant.com/wiki/Cosine_similarity.
[12] Euclidean distance. https://un5qgjbzw9dxcq3ecfxberhh.julianrbryant.com/wiki/Euclidean_distance.
[13] Curse of dimensionality. https://un5qgjbzw9dxcq3ecfxberhh.julianrbryant.com/wiki/Curse_of_dimensionality.
[14] Curse of dimensionality issues in ML. https://un5gmtkzgj4ewvwzrpmtcgack0.julianrbryant.com/blog/understanding-curse-of-dimensionality/.
[15] Cross-entropy loss. https://un5qgjbzw9dxcq3ecfxberhh.julianrbryant.com/wiki/Cross_entropy.
[16] Vector quantization. https://umn0mbagp3hxyydaxe89pvg.julianrbryant.com/fowler/fowler\%20personal\%20page/EE523_files/Ch_10_1\%20VQ\%20Description\%20(PPT).pdf.
[17] Product quantization. https://un5nzw9jyaquawwgzvveng7q.julianrbryant.com/product-quantization-for-similarity-search-2f1f67c5fddd.
[18] R-Trees. https://un5qgjbzw9dxcq3ecfxberhh.julianrbryant.com/wiki/R-tree.
[19] KD-Tree. https://uhm6mk14xhdxcq6gt32g.julianrbryant.com/2020/08/05/find-nearest-neighbor-using-kd-tree/.
[20] Annoy. https://un5nzw9jyaquawwgzvveng7q.julianrbryant.com/comprehensive-guide-to-approximate-nearest-neighbors-algorithms-8b94f057d6b6.
[21] Locality-sensitive hashing. https://un5xmzagmyzzjk6gm3c0.julianrbryant.com/class/cs246/slides/03-1sh.pdf.
[22] Faiss library. https://github.com/facebookresearch/faiss/wiki.
[23] ScaNN library. https://github.com/google-research/google-research/tree/master/scann.
[24] Content moderation with ML. https://un5my6zewdc0.julianrbryant.com/blog/content-moderation/.
[25] Bias in
[26] Positional bias. https://un5mj0e7c7vafa8.julianrbryant.com/writing/position-bias/.
[27] Smart crop. https://un5h2c9ru75vw5chb81g.julianrbryant.com/engineering/en_us/topics/infrastructure/2018/Smart-Auto-Cropping-of-Images.
[28] Better search with gnns. https://un5g9qc4gj7rc.julianrbryant.com/pdf/2010.01666.pdf.
[29] Active learning. https://un5qgjbzw9dxcq3ecfxberhh.julianrbryant.com/wiki/Active_learning_(machine_learning).
[30] Human-in-the-loop ML. https://un5g9qc4gj7rc.julianrbryant.com/pdf/2108.00941.pdf.
[1] Google Street View. https://un5gmtkzgjfbpmm5pm1g.julianrbryant.com/streetview.
[2] DETR. https://github.com/facebookresearch/detr.
[3] RCNN family. https://un5xtc14ffrx6vwhy3c869mu.julianrbryant.com/posts/2017-12-31-object-recognition-part-3.
[4] Fast R-CNN paper. https://un5g9qc4gj7rc.julianrbryant.com/pdf/1504.08083.pdf.
[5] Faster R-CNN paper. https://un5g9qc4gj7rc.julianrbryant.com/pdf/1506.01497.pdf.
[6] YOLO family. https://un5qex6wxtee46t73w.julianrbryant.com/2022/04/04/introduction-to-the-yolo-family.
[7] SSD. https://uhm6mk91tq3v965crkvbe94b8ehpe.julianrbryant.com/ssd-object-detection-single-shot-multibox-detector-for-real-time-processing-9bd8deac0e06.
[8] Data augmentation techniques. https://un5gmtkzghdxcm45v6mj8.julianrbryant.com/getting-started/190280.
[9] CNN. https://un5qgjbzw9dxcq3ecfxberhh.julianrbryant.com/wiki/Convolutional_neural_network.
[10] Object detection details. https://un5n60am7amt3amdzvyvewt5eymc0hp3.julianrbryant.com/object/detection/2019/01/07/Mystery-of-Object-Detection.
[11] Forward pass and backward pass. https://un5gmtkzgkvecnwrqr1g.julianrbryant.com/watch?v=qzPQ8cEsVK8.
[12] MSE. https://un5qgjbzw9dxcq3ecfxberhh.julianrbryant.com/wiki/Mean_squared_error.
[13] Log loss. https://un5qgjbzw9dxcq3ecfxberhh.julianrbryant.com/wiki/Cross_entropy.
[14] Pascal VOC. https://umnf0ujgk7zp4qpgwg2bf9v48drf2.julianrbryant.com/pascal/VOC/voc2008/index.html.
[15] COCO dataset evaluation. https://un5kwmh6tp5vynygt32g.julianrbryant.com/\#detection-eval.
[16] Object detection evaluation. https://github.com/rafaelpadilla/Object-Detection-Metrics.
[17] NMS. https://un5qgjbzw9dxcq3ecfxberhh.julianrbryant.com/wiki/NMS.
[18] Pytorch implementation of NMS. https://un5hru49790x1a8.julianrbryant.com/non-maximum-suppression-theory-and-implementation-in-pytorch/.
[19] Recent object detection models. https://un5jc0hrgjgva.julianrbryant.com/deep-learning/object-detection/.
[20] Distributed training in Tensorflow. https://un5gmtkzgkg7uzmjykw9g9h0br.julianrbryant.com/guide/distributed_training.
[21] Distributed training in Pytorch. https://un5qex1awryd6zm5.julianrbryant.com/tutorials/beginner/dist_overview.html.
[22] GDPR and ML. https://un5gmtkzgj7vz92m3w.julianrbryant.com/radar/how-will-the-gdpr-impact-machine-learning.
[23] Bias and fairness in face detection. https://umn9yz85d2cvaekaza8dd6347y10.julianrbryant.com/col/sid.inpe.br/sibgrapi/2021/09.04.19.00/doc/103.pdf.
[24] AI fairness. https://un5gmtkzghdxcm45v6mj8.julianrbryant.com/code/alexisbcook/ai-fairness.
[25] Continual learning. https://un5nzw9jyaquawwgzvveng7q.julianrbryant.com/how-to-apply-continual-learning-to-your-machine-learning-models-4754adcd7f7f.
[26] Active learning. https://un5qgjbzw9dxcq3ecfxberhh.julianrbryant.com/wiki/Active_learning_(machine_learning).
[27] Human-in-the-loop ML. https://un5g9qc4gj7rc.julianrbryant.com/pdf/2108.00941.pdf.
[1] Elasticsearch. https://un5gmtkzgjk7jznpqbueakg5cttg.julianrbryant.com/elasticsearch/elasticsearch_query_dsl.htm.
[2] Preprocessing text data. https://un5nj085u7ht3exwhj5g.julianrbryant.com/docs/transformers/preprocessing.
[3] NFKD normalization. https://un5uq91rg35tevr.julianrbryant.com/reports/tr15/.
[4] What is Tokenization summary. https://un5nj085u7ht3exwhj5g.julianrbryant.com/docs/transformers/tokenizer_summary.
[5] Hash collision. https://un5qgjbzw9dxcq3ecfxberhh.julianrbryant.com/wiki/Hash_collision.
[6] Deep learning for NLP. https://umn5ebe1x6bryr56hjtw630j1f6br.julianrbryant.com/lecture_notes/notes1.pdf.
[7] TF-IDF. https://un5qgjbzw9dxcq3ecfxberhh.julianrbryant.com/wiki/Tf\%E2\%80\%93idf.
[8] Word2Vec models. https://un5gmtkzgkg7uzmjykw9g9h0br.julianrbryant.com/tutorials/text/word2vec.
[9] Continuous bag of words. https://un5gmtkzghdxck5qrk2j0jr0k0.julianrbryant.com/2018/04/implementing-deep-learning-methods-feature-engineering-text-data-cbow.html.
[10] Skip-gram model. https://umn6ceubwu43xapntzm28.julianrbryant.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/.
[11] BERT model. https://un5g9qc4gj7rc.julianrbryant.com/pdf/1810.04805.pdf.
[12] GPT3 model. https://un5g9qc4gj7rc.julianrbryant.com/pdf/2005.14165.pdf.
[13] BLOOM model. https://un5h2br5w2wvattphkpfywth4eh7aahxr0.julianrbryant.com/blog/bloom.
[14] Transformer implementation from scratch. https://un5tq7v4zjhvyyegxfm0.julianrbryant.com/blog/transformers.
[15] 3D convolutions. https://un5gmtkzghdxcm45v6mj8.julianrbryant.com/code/shivamb/3d-convolutions-understanding-use-case/notebook.
[16] Vision Transformer. https://un5g9qc4gj7rc.julianrbryant.com/pdf/2010.11929.pdf.
[17] Query understanding for search engines. https://un5gmtkzggtb9apnm3rj8.julianrbryant.com/pulse/ai-query-understanding-daniel-tunkelang/.
[18] Multimodal video representation learning. https://un5g9qc4gj7rc.julianrbryant.com/pdf/2012.04124.pdf.
[19] Multilingual language models. https://un5g9qc4gj7rc.julianrbryant.com/pdf/2107.00676.pdf.
[20] Near-duplicate video detection. https://un5g9qc4gj7rc.julianrbryant.com/pdf/2005.07356.pdf.
[21] Generalizable search relevance. https://un5xrj34xjhm6fyg9zvx03qq.julianrbryant.com/book/ai-powered-search/chapter-10/v-10/20.
[22] Freshness in search and recommendation systems. https://un5j2j18xhup0em5wkwe47zq.julianrbryant.com/machine-learning/recommendation/dnn/re-ranking.
[23] Semantic product search by Amazon. https://un5g9qc4gj7rc.julianrbryant.com/pdf/1907.00937.pdf.
[24] Ranking relevance in Yahoo search. https://un5gmtkzghdxck56hkae4.julianrbryant.com/kdd2016/papers/files/adf0361-yinA.pdf.
[25] Semantic product search in E-Commerce. https://un5g9qc4gj7rc.julianrbryant.com/pdf/2008.08180.pdf.
[1] Facebook’s inauthentic behavior. https://un5rgf9muv95j1ygtuj28.julianrbryant.com/policies/community-standards/inauthentic-behavior/.
[2] LinkedIn’s professional community policies. https://un5gmtkzggtb9apnm3rj8.julianrbryant.com/legal/professional-community-policies.
[3] Twitter’s civic integrity policy. https://un5npc82gjkfryr63w.julianrbryant.com/en/rules-and-policies/election-integrity-policy.
[4] Facebook’s integrity survey. https://un5g9qc4gj7rc.julianrbryant.com/pdf/2009.10311.pdf.
[5] Pinterest’s violation detection system. https://un5jf9grrxc0.julianrbryant.com/pinterest-engineering/how-pinterest-fights-misinformation-hate-speech-and-self-harm-content-with-machine-learning-1806b73b40ef.
[6] Abusive detection at LinkedIn. https://un5qg71h07byjeh9xc0b42g5k0.julianrbryant.com/blog/2019/isolation-forest.
[7] WPIE method. https://un5mybugrt5by3nrwg0b5d8.julianrbryant.com/blog/community-standards-report/.
[8] BERT paper. https://un5g9qc4gj7rc.julianrbryant.com/pdf/1810.04805.pdf.
[9] Multilingual DistilBERT. https://un5nj085u7ht3exwhj5g.julianrbryant.com/distilbert-base-multilingual-cased.
[10] Multilingual language models. https://un5g9qc4gj7rc.julianrbryant.com/pdf/2107.00676.pdf.
[11] CLIP model. https://un5mvxtqxupm0.julianrbryant.com/blog/clip/.
[12] SimCLR paper. https://un5g9qc4gj7rc.julianrbryant.com/pdf/2002.05709.pdf.
[13] VideoMoCo paper. https://un5g9qc4gj7rc.julianrbryant.com/pdf/2103.05905.pdf.
[14] Hyperparameter tuning. https://un5necb5yb5rcmnrv6mj8.julianrbryant.com/ai-platform/training/docs/hyperparameter-tuning-overview.
[15] Overfitting. https://un5qgjbzw9dxcq3ecfxberhh.julianrbryant.com/wiki/Overfitting.
[16] Focal loss. https://un5mz2khr2gx6vwhy3c869mu.julianrbryant.com/2020/06/29/FocalLoss.html.
[17] Gradient blending in multimodal systems. https://un5g9qc4gj7rc.julianrbryant.com/pdf/1905.12681.pdf.
[18] ROC curve vs precision-recall curve. https://un5ydfhhb3ev2qfr9ytw46zq.julianrbryant.com/roc-curves-and-precision-recall-curves-for-classification-in-python/.
[19] Introduced bias by human labeling. https://un5w4yym6aytmm23.julianrbryant.com/articles/bias-in-machine-learning.
[20] Facebook’s approach to quickly tackling trending harmful content. https://un5mybugrt5by3nrwg0b5d8.julianrbryant.com/blog/harmful-content-can-evolve-quickly-our-new-ai-system-adapts-to-tackle-it/.
[21] Facebook’s TIES approach. https://un5g9qc4gj7rc.julianrbryant.com/pdf/2002.07917.pdf.
[22] Temporal interaction embedding. https://un5gmtkzgj4n4nq4wkw2e8zq.julianrbryant.com/atscaleevents/videos/730968530723238/.
[23] Building and scaling human review system. https://un5gmtkzgj4n4nq4wkw2e8zq.julianrbryant.com/atscaleevents/videos/1201751883328695/.
[24] Abusive account detection framework. https://un5gmtkzgkvecnwrqr1g.julianrbryant.com/watch?v=YeX4MdU0JNk.
[25] Borderline contents. https://un5rgf9muv95j1ygtuj28.julianrbryant.com/features/approach-to-ranking/content-distribution-guidelines/content-borderline-to-the-community-standards/.
[26] Efficient harmful content detection. https://un5myzb5x75t23j3.julianrbryant.com/news/2021/12/metas-new-ai-system-tackles-harmful-content/.
[27] Linear Transformer paper. https://un5g9qc4gj7rc.julianrbryant.com/pdf/2006.04768.pdf.
[28] Efficient AI models to detect hate speech. https://un5mybugrt5by3nrwg0b5d8.julianrbryant.com/blog/how-facebook-uses-super-efficient-ai-models-to-detect-hate-speech/.
[1] YouTube recommendation system. https://blog.youtube/inside-youtube/on-youtubes-recommendation-system.
[2] DNN for YouTube recommendation. https://un5gdu92gjfbpmm5pndc4fau0htg.julianrbryant.com/media/research.google.com/en//pubs/archive/45530.pdf.
[3] CBOW paper. https://un5g9qc4gj7rc.julianrbryant.com/pdf/1301.3781.pdf.
[4] BERT paper. https://un5g9qc4gj7rc.julianrbryant.com/pdf/1810.04805.pdf.
[5] Matrix factorization. https://un5j2j18xhup0em5wkwe47zq.julianrbryant.com/machine-learning/recommendation/collaborative/matrix.
[6] Stochastic gradient descent. https://un5qgjbzw9dxcq3ecfxberhh.julianrbryant.com/wiki/Stochastic_gradient_descent.
[7] WALS optimization. https://un5pe8tpwvva5nxwhk2zcphc7zg0m.julianrbryant.com/Learn-about-collaborative-filtering-and-weighted-alternating-least-square-with-tensorflow.html.
[8] Instagram multi-stage recommendation system. https://un5mybugrt5by3nrwg0b5d8.julianrbryant.com/blog/powered-by-ai-instagrams-explore-recommender-system/.
[9] Exploration and exploitation trade-offs. https://un5qgjbzw9dxcq3ecfxberhh.julianrbryant.com/wiki/Multi-armed_bandit.
[10] Bias in AI and recommendation systems. https://un5gmtkzgkxf5kcdv7rxqdk0qp3w5n8.julianrbryant.com/biases-search-recommender-systems/339319/#close.
[11] Ethical concerns in recommendation systems. https://un5xrbp0g75veu6cpvjj8.julianrbryant.com/article/10.1007/s00146-020-00950-y.
[12] Seasonality in recommendation systems. https://un5gmtkz2ycqjkygt32g.julianrbryant.com/csdl/proceedings-article/big-data/2019/09005954/1hJsfgT0qL6.
[13] A multitask ranking system. https://un5n68tpnddxcem5tqpfy4k4ym.julianrbryant.com/assets/youtube-multitask.pdf.
[14] Benefit from a negative feedback. https://un5g9qc4gj7rc.julianrbryant.com/abs/1607.04228?context=cs.
[1] Learning to rank methods. https://un5xrj34xjhm6fyg9zvx03qq.julianrbryant.com/book/practical-recommender-systems/chapter-13/53.
[2] RankNet paper. https://un5wg2h8gjwu2.julianrbryant.com/2015/wp-content/uploads/2015/06/icml_ranking.pdf.
[3] LambdaRank paper. https://un5gmtkzgj43w9rdtvyj8.julianrbryant.com/en-us/research/wp-content/uploads/2016/02/lambdarank.pdf.
[4] LambdaMART paper. https://un5gmtkzgj43w9rdtvyj8.julianrbryant.com/en-us/research/wp-content/uploads/2016/02/MSR-TR-2010-82.pdf.
[5] SoftRank paper. https://un5gmtkzgj43w9rdtvyj8.julianrbryant.com/en-us/research/wp-content/uploads/2016/02/SoftRankWsdm08Submitted.pdf.
[6] ListNet paper. https://un5gmtkzgj43w9rdtvyj8.julianrbryant.com/en-us/research/wp-content/uploads/2016/02/tr-2007-40.pdf.
[7] AdaRank paper. https://un5n6cag0p4d6zm5.julianrbryant.com/doi/10.1145/1277741.1277809.
[8] Batch processing vs stream processing. https://un5gmtkzgk8b8y58rjfvfp0.julianrbryant.com/learn/batch-vs-real-time-data-processing/#:~:text=Batch%20processing%20is%20when%20the,data%20flows%20through%20a%20system.
[9] Leveraging location data in ML systems. https://un5nzw9jyaquawwgzvveng7q.julianrbryant.com/leveraging-geolocation-data-for-machine-learning-essential-techniques-192ce3a969bc#:~:text=Location%20data%20is%20an%20important,based%20on%20your%20customer%20data.
[10] Logistic regression. https://un5gmtkzgkvecnwrqr1g.julianrbryant.com/watch?v=yIYKR4sgzI8.
[11] Decision tree. https://un5nem156t2uap7d3w.julianrbryant.com/en/blog/data-analytics/what-is-a-decision-tree/.
[12] Random forests. https://un5qgjbzw9dxcq3ecfxberhh.julianrbryant.com/wiki/Random_forest.
[13] Bias/variance trade-off. https://umn0mtkzgjwveepbxtum29j88c.julianrbryant.com/courses/cs578/2005fa/CS578.bagging.boosting.lecture.pdf.
[14] AdaBoost. https://un5qgjbzw9dxcq3ecfxberhh.julianrbryant.com/wiki/AdaBoost.
[15] XGBoost. https://un5v2734xjhz0emz1bzw2kgpdzg0m.julianrbryant.com/en/stable/.
[16] Gradient boosting. https://un5ydfhhb3ev2qfr9ytw46zq.julianrbryant.com/gentle-introduction-gradient-boosting-algorithm-machine-learning/.
[17] XGBoost in Kaggle competitions. https://un5gmtkzghdxcm45v6mj8.julianrbryant.com/getting-started/145362.
[18] GBDT. https://un5h2c9ru75r2m3e7z13cjqq.julianrbryant.com/gradient-boosting-for-classification/
[19] An introduction to GBDT. https://un5gmtkzgg428prckbtd03hpecn6e.julianrbryant.com/machine-learning/an-introduction-to-gradient-boosting-decision-trees/.
[20] Introduction to neural networks. https://un5gmtkzgkvecnwrqr1g.julianrbryant.com/watch?v=0twSSFZN9Mc.
[21] Bias issues and solutions in recommendation systems. https://un5gmtkzgkvecnwrqr1g.julianrbryant.com/watch?v=pPq9iyGIZZ8.
[22] Feature crossing to encode non-linearity. https://un5j2j18xhup0em5wkwe47zq.julianrbryant.com/machine-learning/crash-course/feature-crosses/encoding-nonlinearity.
[23] Freshness and diversity in recommendation systems. https://un5j2j18xhup0em5wkwe47zq.julianrbryant.com/machine-learning/recommendation/dnn/re-ranking.
[24] Privacy and security in ML. https://un5gmtkzgj43w9rdtvyj8.julianrbryant.com/en-us/research/blog/privacy-preserving-machine-learning-maintaining-confidentiality-and-preserving-trust/.
[25] Two-sides marketplace unique challenges. https://un5gmtkzgj1y2p23.julianrbryant.com/blog/uber-eats-recommending-marketplace/.
[26] Data leakage. https://un5ydfhhb3ev2qfr9ytw46zq.julianrbryant.com/data-leakage-machine-learning/.
[27] Online training frequency. https://un5nj0bdv4ybau23.julianrbryant.com/2022/01/02/real-time-machine-learning-challenges-and-solutions.html#towards-continual-learning.
[1] Addressing delayed feedback. https://un5g9qc4gj7rc.julianrbryant.com/pdf/1907.06558.pdf.
[2] AdTech basics. https://un5u7g3m7vrx6m0k77gj8.julianrbryant.com/library/guides/what-is-adtech.
[3] SimCLR paper. https://un5g9qc4gj7rc.julianrbryant.com/pdf/2002.05709.pdf.
[4] Feature crossing. https://un5j2j18xhup0em5wkwe47zq.julianrbryant.com/machine-learning/crash-course/feature-crosses/video-lecture.
[5] Feature extraction with GBDT. https://un5nzw9jyaquawwgzvveng7q.julianrbryant.com/feature-generation-with-gradient-boosted-decision-trees-21d4946d6ab5.
[6] DCN paper. https://un5g9qc4gj7rc.julianrbryant.com/pdf/1708.05123.pdf.
[7] DCN V2 paper. https://un5g9qc4gj7rc.julianrbryant.com/pdf/2008.13535.pdf.
[8] Microsoft’s deep crossing network paper. https://un5gmtkzghdxck56hkae4.julianrbryant.com/kdd2016/papers/files/adf0975-shanA.pdf.
[9] Factorization Machines. https://un5gmtkzghdxenmj3jaxcjqq.julianrbryant.com/recsys/2017/03/27/factorization-machines/.
[10] Deep Factorization Machines. https://un5n7p8czk5vjq0.julianrbryant.com/chapter_recommender-systems/deepfm.html.
[11] Kaggle’s winning solution in ad click prediction. https://un5gmtkzgkvecnwrqr1g.julianrbryant.com/watch?v=4Go5crRVyuU.
[12] Data leakage in ML systems. https://un5ydfhhb3ev2qfr9ytw46zq.julianrbryant.com/data-leakage-machine-learning/.
[13] Time-based dataset splitting. https://un5gmtkzggtb9apnm3rj8.julianrbryant.com/pulse/time-based-splitting-determining-train-test-data-come-manraj-chalokia/?trk=public_profile_article_view.
[14] Model calibration. https://un5ydfhhb3ev2qfr9ytw46zq.julianrbryant.com/calibrated-classification-model-in-scikit-learn/.
[15] Field-aware Factorization Machines. https://un5gmtkzgjwpjnpgpbcbe2hc1e6xp.julianrbryant.com/~cjlin/papers/ffm.pdf.
[16] Catastrophic forgetting problem in continual learning. https://un5gmtkzgjwveemrc689pvg.julianrbryant.com/~liub/lifelong-learning/continual-learning.pdf.
[1] Instagram’s Explore recommender system. https://un5mybugrt5by3nrwg0b5d8.julianrbryant.com/blog/powered-by-ai-instagrams-explore-recommender-system.
[2] Listing embeddings in search ranking. https://un5jf9grrxc0.julianrbryant.com/airbnb-engineering/listing-embeddings-for-similar-listing-recommendations-and-real-time-personalization-in-search-601172f7603e.
[3] Word2vec. https://un5qgjbzw9dxcq3ecfxberhh.julianrbryant.com/wiki/Word2vec.
[4] Negative sampling technique. https://un5gmtkzgkzvktn60b2j8.julianrbryant.com/cs/nlps-word2vec-negative-sampling.
[5] Positional bias. https://un5mj0e7c7vafa8.julianrbryant.com/writing/position-bias/.
[6] Random walk. https://un5qgjbzw9dxcq3ecfxberhh.julianrbryant.com/wiki/Random_walk.
[7] Random walk with restarts. https://un5gmtkzgkvecnwrqr1g.julianrbryant.com/watch?v=HbzQzUaJ_9I.
[8] Seasonality in recommendation systems. https://un5gmtkz2ycqjkygt32g.julianrbryant.com/csdl/proceedings-article/big-data/2019/09005954/1hJsfgT0qL6.
[1] News Feed ranking in Facebook. https://un5qg71h07byjemjq01g.julianrbryant.com/2021/01/26/ml-applications/news-feed-ranking/.
[2] Twitter’s news feed system. https://un5h2c9ru75vw5chb81g.julianrbryant.com/engineering/en_us/topics/insights/2017/using-deep-learning-at-scale-in-twitters-timelines.
[3] LinkedIn’s News Feed system LinkedIn. https://un5qg71h07byjeh9xc0b42g5k0.julianrbryant.com/blog/2020/understanding-feed-dwell-time.
[4] BERT paper. https://un5g9qc4gj7rc.julianrbryant.com/pdf/1810.04805.pdf.
[5] ResNet model. https://un5g9qc4gj7rc.julianrbryant.com/pdf/1512.03385.pdf.
[6] CLIP model. https://un5mvxtqxupm0.julianrbryant.com/blog/clip/.
[7] Viterbi algorithm. https://un5qgjbzw9dxcq3ecfxberhh.julianrbryant.com/wiki/Viterbi_algorithm.
[8] TF-IDF. https://un5qgjbzw9dxcq3ecfxberhh.julianrbryant.com/wiki/Tf%E2%80%93idf.
[9] Word2vec. https://un5qgjbzw9dxcq3ecfxberhh.julianrbryant.com/wiki/Word2vec.
[10] Serving a billion personalized news feed. https://un5gmtkzgkvecnwrqr1g.julianrbryant.com/watch?v=Xpx5RYNTQvg.
[11] Mean absolute error loss. https://un5qgjbzw9dxcq3ecfxberhh.julianrbryant.com/wiki/Mean_absolute_error.
[12] Means squared error loss. https://un5qgjbzw9dxcq3ecfxberhh.julianrbryant.com/wiki/Mean_squared_error.
[13] Huber loss. https://un5qgjbzw9dxcq3ecfxberhh.julianrbryant.com/wiki/Huber_loss.
[14] A news feed system design. https://un5xr0f5zbrk9nxcxe8e4tugce468gtx4m.julianrbryant.com/system-design/news-feed/design-a-news-feed-system.
[15] Predict viral tweets. https://un5nzw9jyaquawwgzvveng7q.julianrbryant.com/using-data-science-to-predict-viral-tweets-615b0acc2e1e.
[16] Cold start problem in recommendation systems. https://un5qgjbzw9dxcq3ecfxberhh.julianrbryant.com/wiki/Cold_start_(recommender_systems).
[17] Positional bias. https://un5mj0e7c7vafa8.julianrbryant.com/writing/position-bias/.
[18] Determine retraining frequency. https://un5nj0bdv4ybau23.julianrbryant.com/2022/01/02/real-time-machine-learning-challenges-and-solutions.html#towards-continual-learning.
[1] Clustering in ML. https://un5j2j18xhup0em5wkwe47zq.julianrbryant.com/machine-learning/clustering/overview.
[2] PYMK on Facebook. https://un5hgz1xtk5y2.julianrbryant.com/Xpx5RYNTQvg?t=1823.
[3] Graph convolutional neural networks. https://umn4hpanwact2em5tqpfy4k4ym.julianrbryant.com/graph-convolutional-networks/.
[4] GraphSage paper. https://un5nebagmyzzjk6gm3c0.julianrbryant.com/people/jure/pubs/graphsage-nips17.pdf.
[5] Graph attention networks. https://un5g9qc4gj7rc.julianrbryant.com/pdf/1710.10903.pdf.
[6] Graph isomorphism network. https://un5g9qc4gj7rc.julianrbryant.com/pdf/1810.00826.pdf.
[7] Graph neural networks. https://distill.pub/2021/gnn-intro/.
[8] Personalized random walk. https://un5gmtkzgkvecnwrqr1g.julianrbryant.com/watch?v=HbzQzUaJ_9I.
[9] LinkedIn’s PYMK system. https://un5qg71h07byjeh9xc0b42g5k0.julianrbryant.com/blog/2021/optimizing-pymk-for-equity-in-network-creation.
[10] Addressing delayed feedback. https://un5g9qc4gj7rc.julianrbryant.com/pdf/1907.06558.pdf