[1] Machine Learning System Design Interview. https://www.aliaminian.com/books.
[2] Support Vector Machines. https://scikit-learn.org/stable/modules/svm.html.
[3] Bayes’ theorem. https://en.wikipedia.org/wiki/Bayes%27_theorem.
[4] Gaussian mixture models. https://scikit-learn.org/1.5/modules/mixture.html.
[5] Hidden Markov model. https://en.wikipedia.org/wiki/Hidden_Markov_model.
[6] Boltzmann machine. https://en.wikipedia.org/wiki/Boltzmann_machine.
[7] OpenAI’s ChatGPT. https://openai.com/index/chatgpt/.
[8] Economic Potential of Generative AI. https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier.
[9] The Llama 3 Herd of Models. https://arxiv.org/abs/2407.21783.
[10] Flamingo: a Visual Language Model for Few-Shot Learning. https://arxiv.org/abs/2204.14198.
[11] PaLM: Scaling Language Modeling with Pathways. https://arxiv.org/abs/2204.02311.
[12] Language Models are Few-Shot Learners. https://arxiv.org/abs/2005.14165.
[13] Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding. https://arxiv.org/abs/2205.11487.
[14] PaLM2 Technical Report. https://arxiv.org/abs/2305.10403.
[15] H100 Tensor Core GPU. https://www.nvidia.com/en-us/data-center/h100/.
[16] GPT-4 training cost. www.wired.com/story/openai-ceo-sam-altman-the-age-of-giant-ai-models-is-already-over/.
[17] Scaling Laws for Neural Language Models. https://arxiv.org/abs/2001.08361.
[18] Training Compute-Optimal Large Language Models. https://arxiv.org/abs/2203.15556.
[19] Introducing OpenAI o1. https://openai.com/index/introducing-openai-o1-preview/.
[20] Large Language Monkeys: Scaling Inference Compute with Repeated Sampling. https://arxiv.org/abs/2407.21787.
[21] ETL. https://aws.amazon.com/what-is/etl/.
[22] Tecton. https://www.tecton.ai/feature-store/.
[23] Amazon SageMaker. https://aws.amazon.com/sagemaker/.
[24] ML System Design Interview. https://www.amazon.com/gp/product/1736049127/.
[25] Comprehensive Exploration of Synthetic Data Generation: A Survey. https://arxiv.org/abs/2401.02524.
[26] HDFS Architecture Guide. https://hadoop.apache.org/docs/r1.2.1/hdfs_design.html.
[27] Amazon S3. https://aws.amazon.com/s3/.
[28] Apache Parquet. https://parquet.apache.org/.
[29] Apache ORC. https://orc.apache.org/docs/.
[30] Apache Lucene. https://lucene.apache.org/.
[31] Elasticsearch. https://www.elastic.co/elasticsearch.
[32] Attention Is All You Need. https://arxiv.org/abs/1706.03762.
[33] BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. https://arxiv.org/abs/1810.04805.
[34] An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. https://arxiv.org/abs/2010.11929.
[35] Learning Transferable Visual Models From Natural Language Supervision. https://arxiv.org/abs/2103.00020.
[36] Zero-Shot Text-to-Image Generation. https://arxiv.org/abs/2102.12092.
[37] Neural Machine Translation by Jointly Learning to Align and Translate. https://arxiv.org/abs/1409.0473.
[38] Common Crawl. https://commoncrawl.org/.
[39] Data Parallelism. https://en.wikipedia.org/wiki/Data_parallelism.
[40] Model Parallelism. https://huggingface.co/docs/transformers/v4.15.0/en/parallelism.
[41] Pipeline Parallelism. https://pytorch.org/docs/stable/distributed.pipelining.html.
[42] Mixed Precision Training. https://arxiv.org/abs/1710.03740.
[43] High-Resolution Image Synthesis with Latent Diffusion Models. https://arxiv.org/abs/2112.10752.
[44] Training Deep Nets with Sublinear Memory Cost. https://arxiv.org/abs/1604.06174.
[45] Automatic Mixed Precision. https://pytorch.org/tutorials/recipes/recipes/amp_recipe.html.
[46] Model parallelism. https://huggingface.co/docs/transformers/v4.17.0/en/parallelism.
[47] Paradigms of Parallelism. https://colossalai.org/docs/concepts/paradigms_of_parallelism/.
[48] Tensor Parallelism tutorial. https://pytorch.org/tutorials/intermediate/TP_tutorial.html.
[49] ZeRO: Memory Optimizations Toward Training Trillion Parameter Models. https://arxiv.org/abs/1910.02054.
[50] Introducing PyTorch Fully Sharded Data Parallel (FSDP) API. https://pytorch.org/blog/introducing-pytorch-fully-sharded-data-parallel-api/.
[51] Beam search. https://en.wikipedia.org/wiki/Beam_search.
[52] Top-k sampling. https://docs.cohere.com/docs/controlling-generation-with-top-k-top-p.
[53] Model monitoring for ML in production. https://www.evidentlyai.com/ml-in-production/model-monitoring.

[1] Gmail’s Smart Compose Feature. https://research.google/pubs/gmail‐smart‐compose‐real‐time‐assisted‐writing/.
[2] Fundamentals of Recurrent Neural Network. https://arxiv.org/abs/1808.03314.
[3] Attention Is All You Need. https://arxiv.org/abs/1706.03762.
[4] Gated Recurrent Unit. https://en.wikipedia.org/wiki/Gated_recurrent_unit.
[5] Long Short‐Term Memory. https://deeplearning.cs.cmu.edu/F23/document/readings/LSTM.pdf.
[6] RITA: Group Attention is All You Need for Timeseries Analytics. https://arxiv.org/abs/2306.01926.
[7] FlashAttention: Fast and Memory‐Efficient Exact Attention with IO‐Awareness. https://arxiv.org/abs/2205.14135.
[8] Language Identification. https://en.wikipedia.org/wiki/Language_identification.
[9] FastText Model for Language Identification. https://huggingface.co/facebook/fasttext-language-identification.
[10] Transformer‐XL. https://arxiv.org/abs/1901.02860.
[11] Byte‐Pair Encoding Tokenization. https://huggingface.co/learn/nlp-course/en/chapter6/5.
[12] SentencePiece Tokenization. https://arxiv.org/abs/1808.06226.
[13] Tiktoken Library. https://github.com/openai/tiktoken.
[14] Google’s Gemini. https://gemini.google.com/.
[15] SentencePiece Library. https://github.com/google/sentencepiece.
[16] Summary of Tokenizers. https://huggingface.co/docs/transformers/en/tokenizer_summary.
[17] OpenAI’s Tokenizers. https://tiktokenizer.vercel.app/?model=gpt‐4‐1106‐preview.
[18] BERT. https://arxiv.org/abs/1810.04805.
[19] OpenAI’s Models. https://platform.openai.com/docs/models.
[20] Meta’s LLaMA. https://llama.meta.com/.
[21] Introduction to Transformers by Andrej Karpathy. https://www.youtube.com/watch?v=XfpMkf4rD6E.
[22] Transformer Visualized. https://jalammar.github.io/illustrated-transformer/.
[23] Common Crawl. https://commoncrawl.org/.
[24] Cross‐Entropy. https://en.wikipedia.org/wiki/Cross-entropy.
[25] Prompt Engineering. https://platform.openai.com/docs/guides/prompt-engineering.
[26] Beam Search. https://en.wikipedia.org/wiki/Beam_search.
[27] Perplexity. https://en.wikipedia.org/wiki/Perplexity.
[28] Gmail Smart Compose: Real‐Time Assisted Writing. https://arxiv.org/abs/1906.00080.
[29] WordPiece Tokenization. https://huggingface.co/learn/nlp-course/en/chapter6/6.
[30] Better & Faster Large Language Models via Multi‐token Prediction. https://arxiv.org/abs/2404.19737.

[1] Google Translate Service. https://blog.google/products/translate/google-translate-new-languages-2024/.
[2] Neural Machine Translation by Jointly Learning to Align and Translate. https://arxiv.org/abs/1409.0473.
[3] BERT: Pre‐training of Deep Bidirectional Transformers for Language Understanding. https://arxiv.org/abs/1810.04805.
[4] GPT Models. https://platform.openai.com/docs/models.
[5] Claude Models. https://www.anthropic.com/claude.
[6] Bidirectional Long Short‐Term Memory (BLSTM) Neural Networks for Reconstruction of Top‐Quark Pair Decay Kinematics. https://arxiv.org/abs/1909.01144.
[7] BPE Tokenization. https://huggingface.co/learn/nlp-course/en/chapter6/5.
[8] C4 Dataset. https://www.tensorflow.org/datasets/catalog/c4.
[9] Wikipedia Dataset. https://www.tensorflow.org/datasets/catalog/wikipedia.
[10] Stack Exchange Dataset. https://huggingface.co/datasets/HuggingFaceH4/stack-exchange-preferences.
[11] How Transformers Work. https://huggingface.co/learn/nlp-course/en/chapter1/4.
[12] Exploring the Limits of Transfer Learning with a Unified Text‐to‐Text Transformer. https://arxiv.org/pdf/1910.10683.pdf.
[13] BART: Denoising Sequence‐to‐Sequence Pre‐training for Natural Language Generation, Translation, and Comprehension. https://arxiv.org/abs/1910.13461.
[14] mT5: A Massively Multilingual Pre‐trained Text‐to‐Text Transformer. https://arxiv.org/abs/2010.11934.
[15] Multilingual Denoising Pre‐training for Neural Machine Translation. https://arxiv.org/abs/2001.08210.
[16] BLEU Metric. https://en.wikipedia.org/wiki/BLEU.
[17] ROUGE Metric. https://en.wikipedia.org/wiki/ROUGE_(metric).
[18] METEOR Metric. https://www.cs.cmu.edu/~alavie/METEOR/pdf/Banerjee-Lavie-2005-METEOR.pdf.
[19] WordNet. https://wordnet.princeton.edu/.
[20] No Language Left Behind: Scaling Human‐Centered Machine Translation. https://research.facebook.com/publications/no-language-left-behind/.
[21] Decoder‐Only or Encoder‐Decoder? Interpreting Language Model as a Regularized Encoder‐Decoder. https://arxiv.org/abs/2304.04052.
[22] Towards Continual Learning for Multilingual Machine Translation via Vocabulary Substitution. https://arxiv.org/abs/2103.06799.
[23] Efficient Inference for Neural Machine Translation. https://arxiv.org/abs/2010.02416.
[24] Meta’s Multilingual Model. https://ai.meta.com/blog/nllb-200-high-quality-machine-translation/.
[25] Machine Translation Evaluation. https://en.wikipedia.org/wiki/Evaluation_of_machine_translation.
[26] Word Error Rate (WER) Metric. https://en.wikipedia.org/wiki/Word_error_rate.
[27] Automatic Language Identification Using Deep Neural Networks. https://research.google.com/pubs/archive/42538.pdf.

[1] OpenAI’s ChatGPT. https://openai.com/index/chatgpt/.
[2] ChatGPT Wiki. https://en.wikipedia.org/wiki/ChatGPT.
[3] OpenAI’s Models. https://platform.openai.com/docs/models.
[4] Google’s Gemini. https://gemini.google.com/.
[5] Meta’s Llama. https://llama.meta.com/.
[6] Beautiful Soup. https://beautiful-soup-4.readthedocs.io/en/latest/.
[7] Lxml. https://lxml.de/.
[8] Document Object Model. https://en.wikipedia.org/wiki/Document_Object_Model.
[9] Boilerplate Removal Tool. https://github.com/miso-belica/jusText.
[10] fastText. https://fasttext.cc/.
[11] langid. https://github.com/saffsd/langid.py.
[12] RoFormer: Enhanced Transformer with Rotary Position Embedding. https://arxiv.org/abs/2104.09864.
[13] Llama 3 Human Evaluation. https://github.com/meta-llama/llama3/blob/main/eval_details.md.
[14] Exploring the Limits of Transfer Learning with a Unified Text‐to‐Text Transformer. https://arxiv.org/abs/1910.10683.
[15] DeBERTa: Decoding‐enhanced BERT with Disentangled Attention. https://arxiv.org/abs/2006.03654.
[16] Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention. https://arxiv.org/abs/2006.16236.
[17] Common Crawl. https://commoncrawl.org/.
[18] C4 Dataset. https://www.tensorflow.org/datasets/catalog/c4.
[19] Stack Exchange Dataset. https://github.com/EleutherAI/stackexchange-dataset.
[20] Training Language Models to Follow Instructions with Human Feedback. https://arxiv.org/abs/2203.02155.
[21] Alpaca. https://crfm.stanford.edu/2023/03/13/alpaca.html.
[22] Dolly‐15K. https://www.databricks.com/blog/2023/04/12/dolly-first-open-commercially-viable-instruction-tuned-llm.
[23] Introducing FLAN: More Generalizable Language Models with Instruction Fine‐Tuning. https://research.google/blog/introducing-flan-more-generalizable-language-models-with-instruction-fine-tuning/.
[24] Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback. https://arxiv.org/abs/2204.05862.
[25] Proximal Policy Optimization Algorithms. https://arxiv.org/abs/1707.06347.
[26] Direct Preference Optimization: Your Language Model is Secretly a Reward Model. https://arxiv.org/abs/2305.18290.
[27] Illustrating RLHF. https://huggingface.co/blog/rlhf.
[28] RLHF Progress and Challenges. https://www.youtube.com/watch?v=hhiLw5Q_UFg.
[29] State of GPT. https://www.youtube.com/watch?v=bZQun8Y4L2A.
[30] Different Sampling Methods. https://huggingface.co/blog/how-to-generate.
[31] The Curious Case of Neural Text Degeneration. https://arxiv.org/abs/1904.09751.
[32] OpenAI’s API Reference. https://platform.openai.com/docs/api-reference/chat/create.
[33] Cheat Sheet: Mastering Temperature and Top_p in ChatGPT API. https://community.openai.com/t/cheat‐sheet‐mastering‐temperature‐and‐top‐p‐in‐chatgpt‐api/172683.
[34] PIQA: Reasoning about Physical Commonsense in Natural Language. https://arxiv.org/abs/1911.11641.
[35] SocialIQA: Commonsense Reasoning about Social Interactions. https://arxiv.org/abs/1904.09728.
[36] HellaSwag: Can a Machine Really Finish Your Sentence? https://arxiv.org/abs/1905.07830.
[37] WinoGrande: An Adversarial Winograd Schema Challenge at Scale. https://arxiv.org/abs/1907.10641.
[38] Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering. https://arxiv.org/abs/1809.02789.
[39] CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge. https://arxiv.org/abs/1811.00937.
[40] TriviaQA: A Large Scale Dataset for Reading Comprehension and Question Answering. https://nlp.cs.washington.edu/triviaqa/.
[41] The Natural Questions Dataset. https://ai.google.com/research/NaturalQuestions.
[42] SQuAD: 100,000+ Questions for Machine Comprehension of Text. https://arxiv.org/abs/1606.05250.
[43] QuAC Dataset. https://quac.ai/.
[44] BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions. https://arxiv.org/abs/1905.10044.
[45] GSM8K Dataset. https://github.com/openai/grade-school-math.
[46] MATH Dataset. https://github.com/hendrycks/math/.
[47] HumanEval Dataset. https://github.com/openai/human-eval.
[48] MBPP Dataset. https://github.com/google-research/google-research/tree/master/mbpp.
[49] Measuring Massive Multitask Language Understanding. https://arxiv.org/abs/2009.03300.
[50] Measuring Massive Multitask Language Understanding. https://arxiv.org/abs/2009.03300.
[51] AGIEval: A Human‐Centric Benchmark for Evaluating Foundation Models. https://arxiv.org/abs/2304.06364.
[52] RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models. https://arxiv.org/abs/2009.11462.
[53] Perspective API. https://perspectiveapi.com/.
[54] ToxiGen: A Large‐Scale Machine‐Generated Dataset for Adversarial and Implicit Hate Speech Detection. https://arxiv.org/abs/2203.09509.
[55] HateCheck: Functional Tests for Hate Speech Detection Models. https://arxiv.org/abs/2012.15606.
[56] CrowS‐Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models. https://arxiv.org/abs/2010.00133.
[57] BBQ: A Hand‐Built Bias Benchmark for Question Answering. https://arxiv.org/abs/2110.08193.
[58] BOLD: Dataset and Metrics for Measuring Biases in Open‐Ended Language Generation. https://arxiv.org/abs/2101.11718.
[59] TruthfulQA: Measuring How Models Mimic Human Falsehoods. https://arxiv.org/abs/2109.07958.
[60] Question Answering for Privacy Policies: Combining Computational and Legal Perspectives. https://arxiv.org/abs/1911.00841.
[61] AdvGLUE Benchmark. https://adversarialglue.github.io/.
[62] Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment. https://arxiv.org/abs/1907.11932.
[63] AdvBench. https://github.com/llm-attacks/llm-attacks.
[64] Chatbot Arena Leaderboard. https://huggingface.co/spaces/lmarena-ai/chatbot-arena-leaderboard.
[65] A Survey on Recent Advances in LLM‐Based Multi‐Turn Dialogue Systems. https://arxiv.org/abs/2402.18013.
[66] Better & Faster Large Language Models via Multi‐Token Prediction. https://arxiv.org/abs/2404.19737.
[67] Gemini 1.5: Unlocking Multimodal Understanding Across Millions of Tokens of Context. https://arxiv.org/abs/2403.05530.
[68] HyperAttention: Long‐Context Attention in Near‐Linear Time. https://arxiv.org/abs/2310.05869.
[69] MM‐LLMs: Recent Advances in Multimodal Large Language Models. https://arxiv.org/abs/2401.13601.
[70] Multimodality and Large Multimodal Models. https://huyenchip.com/2023/10/10/multimodal.html.
[71] What is Retrieval‐Augmented Generation? https://cloud.google.com/use‐cases/retrieval‐augmented‐generation.
[72] How to Customize an LLM: A Deep Dive to Tailoring an LLM for Your Business. https://techcommunity.microsoft.com/blog/machinelearningblog/how-to-customize-an-llm-a-deep-dive-to-tailoring-an-llm-for-your-business/4110204.
[73] Llama 2: Open Foundation and Fine‐Tuned Chat Models. https://arxiv.org/abs/2307.09288.
[74] Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned. https://arxiv.org/abs/2209.07858.
[75] Introducing Superalignment. https://openai.com/index/introducing-superalignment/.
[76] Language Models are Few‐Shot Learners. https://arxiv.org/abs/2005.14165.
[77] GQA: Training Generalized Multi‐Query Transformer Models from Multi‐Head Checkpoints. https://arxiv.org/abs/2305.13245.
[78] Chain‐of‐Thought Prompting Elicits Reasoning in Large Language Models. https://arxiv.org/abs/2201.11903.
[79] Efficiently Scaling Transformer Inference. https://arxiv.org/abs/2211.05102.
[80] Prover‐Verifier Games Improve Legibility of Language Model Outputs. https://openai.com/index/prover-verifier-games-improve-legibility/.

[1] BLIP‐2: Bootstrapping Language‐Image Pre‐training with Frozen Image Encoders and Large Language Models. https://arxiv.org/abs/2301.12597.
[2] xGen‐MM (BLIP‐3): A Family of Open Large Multimodal Models. https://www.arxiv.org/abs/2408.08872.
[3] InternVL: Scaling Up Vision Foundation Models and Aligning for Generic Visual‐Linguistic Tasks. https://arxiv.org/abs/2312.14238.
[4] Meta’s Llama. https://llama.meta.com/.
[5] Byte‐Pair Encoding Tokenization. https://huggingface.co/learn/nlp-course/en/chapter6/5.
[6] LAION‐5B: An Open Large‐Scale Dataset for Training Next Generation Image‐Text Models. https://arxiv.org/abs/2210.08402.
[7] An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. https://arxiv.org/abs/2010.11929.
[8] Language Models are Unsupervised Multitask Learners. https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf.
[9] Learning Transferable Visual Models From Natural Language Supervision. https://arxiv.org/abs/2103.00020.
[10] Cross‐Entropy. https://en.wikipedia.org/wiki/Cross-entropy.
[11] CIDEr: Consensus‐Based Image Description Evaluation. https://arxiv.org/abs/1411.5726.
[12] TF‐IDF Introduction. https://web.stanford.edu/class/cs276/19handouts/lecture6-tfidf-1per.pdf.
[13] TF‐IDF. https://en.wikipedia.org/wiki/Tf%E2%80%93idf.
[14] Visual Question Answering Introduction. https://huggingface.co/tasks/visual-question-answering.
[15] Cross‐Domain Image Captioning with Discriminative Finetuning. https://arxiv.org/abs/2304.01662.
[16] Crossmodal‐3600 — Multilingual Reference Captions for Geographically Diverse Images. https://research.google/blog/crossmodal-3600-multilingual-reference-captions-for-geographically-diverse-images/.
[17] Efficient Image Captioning for Edge Devices. https://arxiv.org/abs/2212.08985.
[18] Ensemble Model Using an Image Captioning and Ranking Example. https://cloud.google.com/dataflow/docs/notebooks/run_inference_multi_model.

[1] Perplexity. https://www.perplexity.ai/.
[2] ChatPDF. https://www.chatpdf.com/.
[3] LoRA: Low‐Rank Adaptation of Large Language Models. https://arxiv.org/abs/2106.09685.
[4] Optical Character Recognition. https://en.wikipedia.org/wiki/Optical_character_recognition.
[5] Dedoc GitHub Repository. https://github.com/ispras/dedoc.
[6] LayoutParser: A Unified Toolkit for Deep Learning Based Document Image Analysis. https://arxiv.org/abs/2103.15348.
[7] Google Cloud document parser API. https://cloud.google.com/document-ai/docs/layout-parse-chunk.
[8] PDF.CO document parser API. https://developer.pdf.co/api/document-parser/index.html.
[9] Character text splitter in LangChain. https://python.langchain.com/v0.1/docs/modules/data_connection/document_transformers/character_text_splitter/.
[10] Elasticsearch. https://www.elastic.co/elasticsearch.
[11] A Survey on Knowledge Graphs: Representation, Acquisition, and Applications. https://ieeexplore.ieee.org/document/9416312.
[12] Christopher D. Manning. Introduction to Information Retrieval.2008.
[13] Modern Information Retrieval: A Brief Overview. http://singhal.info/ieee2001.pdf.
[14] Learning Transferable Visual Models From Natural Language Supervision. https://arxiv.org/abs/2103.00020.
[15] OpenAI finetuning documentation. https://platform.openai.com/docs/guides/fine-tuning.
[16] Anthropic finetuning. https://www.anthropic.com/news/fine-tune-claude-3-haiku.
[17] RAFT: Adapting Language Model to Domain Specific RAG. https://arxiv.org/abs/2403.10131.
[18] Euclidean Distance. https://en.wikipedia.org/wiki/Euclidean_distance.
[19] Cosine Similarity. https://en.wikipedia.org/wiki/Cosine_similarity.
[20] Multidimensional binary search trees used for associative searching. https://dl.acm.org/doi/10.1145/361002.361007.
[21] R‐trees: A dynamic index structure for spatial searching. https://dl.acm.org/doi/10.1145/971697.602266.
[22] Annoy Library. https://github.com/spotify/annoy.
[23] Similarity search in high dimensions via hashing. https://www.cs.princeton.edu/courses/archive/spring13/cos598C/Gionis.pdf.
[24] Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs. https://arxiv.org/abs/1603.09320.
[25] Faiss Documentation. https://faiss.ai/.
[26] ScaNN. https://research.google/blog/announcing-scann-efficient-vector-similarity-search/.
[27] Developer Playground. https://docs.cohere.com/v2/docs/playground-overview.
[28] Chain‐of‐Thought Prompting Elicits Reasoning in Large Language Models. https://arxiv.org/abs/2201.11903.
[29] Tree of Thoughts: Deliberate Problem Solving with Large Language Models. https://arxiv.org/abs/2305.10601.
[30] OpenAI o1. https://openai.com/index/learning-to-reason-with-llms/.
[31] Scaling LLM Test‐Time Compute Optimally can be More Effective than Scaling Model Parameters. https://arxiv.org/abs/2408.03314.
[32] Language Models are Few‐Shot Learners. https://arxiv.org/abs/2005.14165.
[33] Machine Learning System Design Interview. https://www.aliaminian.com/books.
[34] Evaluation Measure for Information Retrieval. https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval).
[35] Ragas. https://docs.ragas.io/en/stable/.
[36] ARES: An Automated Evaluation Framework for Retrieval‐Augmented Generation Systems. https://arxiv.org/abs/2311.09476.
[37] Query2doc: Query Expansion with Large Language Models. https://arxiv.org/abs/2303.07678.
[38] TableNet: Deep Learning model for end‐to‐end Table detection and Tabular data extraction from Scanned Document Images. https://arxiv.org/abs/2001.01469.
[39] CascadeTabNet: An approach for end to end table detection and structure recognition from image‐based documents. https://arxiv.org/abs/2004.12629.
[40] Deepdesrt: Deep learning for detection and structure recognition of tables in document images. https://ieeexplore.ieee.org/document/8270123.
[41] Active Retrieval Augmented Generation. https://arxiv.org/abs/2305.06983.
[42] Self‐RAG: Learning to Retrieve, Generate, and Critique through Self‐Reflection. https://arxiv.org/abs/2310.11511.
[43] Precise Zero‐Shot Dense Retrieval without Relevance Labels. https://arxiv.org/abs/2212.10496.

[1] StyleGAN2. https://arxiv.org/abs/1912.04958.
[2] Auto‐Encoding Variational Bayes. https://arxiv.org/abs/1312.6114.
[3] Generative Adversarial Networks. https://arxiv.org/abs/1406.2661.
[4] Combating Mode Collapse in GAN Training: An Empirical Analysis Using Hessian Eigenvalues. https://arxiv.org/abs/2012.09673.
[5] Google’s GAN Course. https://developers.google.com/machine-learning/gan/training.
[6] StackGAN: Text to Photo‐Realistic Image Synthesis with Stacked Generative Adversarial Networks. https://arxiv.org/abs/1612.03242.
[7] Zero‐Shot Text‐to‐Image Generation. https://arxiv.org/abs/2102.12092.
[8] Muse: Text‐To‐Image Generation via Masked Generative Transformers. https://arxiv.org/abs/2301.00704.
[9] DALL∙E 3. https://openai.com/index/dall-e-3/.
[10] Attribute‐Specific Control Units in StyleGAN for Fine‐Grained Image Manipulation. https://arxiv.org/abs/2111.13010.
[11] A Guide to Convolution Arithmetic for Deep Learning. https://arxiv.org/abs/1603.07285.
[12] Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. https://arxiv.org/abs/1502.03167.
[13] Layer Normalization. https://arxiv.org/abs/1607.06450.
[14] Instance Normalization: The Missing Ingredient for Fast Stylization. https://arxiv.org/abs/1607.08022.
[15] Group Normalization. https://arxiv.org/abs/1803.08494.
[16] Deep Learning using Rectified Linear Units (ReLU). https://arxiv.org/abs/1803.08375.
[17] PyTorch’s Tanh Layer. https://pytorch.org/docs/stable/generated/torch.nn.Tanh.html.
[18] A Style‐Based Generator Architecture for Generative Adversarial Networks. https://arxiv.org/abs/1812.04948.
[19] Minimax. https://en.wikipedia.org/wiki/Minimax.
[20] Loss Functions in GANs. https://developers.google.com/machine-learning/gan/loss.
[21] Towards Principled Methods for Training Generative Adversarial Networks. https://arxiv.org/abs/1701.04862.
[22] Unrolled Generative Adversarial Networks. https://arxiv.org/abs/1611.02163.
[23] Stabilizing Training of Generative Adversarial Networks through Regularization. https://arxiv.org/abs/1705.09367.
[24] Megapixel Size Image Creation using Generative Adversarial Networks. https://arxiv.org/abs/1706.00082v1.
[25] Inception Score. https://en.wikipedia.org/wiki/Inception_score.
[26] GANs Trained by a Two Time‐Scale Update Rule Converge to a Local Nash Equilibrium. https://arxiv.org/abs/1706.08500.
[27] Demystifying MMD GANs. https://arxiv.org/abs/1801.01401.
[28] Rethinking the Inception Architecture for Computer Vision. https://arxiv.org/abs/1512.00567.
[29] FID Calculation. https://en.wikipedia.org/wiki/Fr%C3%A9chet_inception_distance.
[30] The Role of ImageNet Classes in Fréchet Inception Distance. https://arxiv.org/abs/2203.06026.
[31] Hierarchical Text‐Conditional Image Generation with CLIP Latents. https://arxiv.org/abs/2204.06125.
[32] Alias‐Free Generative Adversarial Networks. https://arxiv.org/abs/2106.12423.
[33] StyleGAN3. https://nvlabs.github.io/stylegan3/.
[34] Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. https://arxiv.org/abs/1511.06434.
[35] Wasserstein GAN. https://arxiv.org/abs/1701.07875.
[36] Stabilizing Generative Adversarial Networks: A Survey. https://arxiv.org/abs/1910.00927.
[37] Conditional Generative Adversarial Nets. https://arxiv.org/abs/1411.1784.
[38] CLIPScore: A Reference‐free Evaluation Metric for Image Captioning. https://arxiv.org/abs/2104.08718.
[39] DreamBooth: Fine Tuning Text‐to‐Image Diffusion Models for Subject‐Driven Generation. https://arxiv.org/abs/2208.12242.

[1] Taming Transformers for High‐Resolution Image Synthesis. https://arxiv.org/abs/2012.09841.
[2] Neural Discrete Representation Learning. https://arxiv.org/abs/1711.00937.
[3] Deep Learning using Rectified Linear Units (ReLU). https://arxiv.org/abs/1803.08375.
[4] Euclidean Distance. https://en.wikipedia.org/wiki/Euclidean_distance.
[5] A Guide to Convolution Arithmetic for Deep Learning. https://arxiv.org/abs/1603.07285.
[6] Very Deep Convolutional Networks for Large‐Scale Image Recognition. https://arxiv.org/abs/1409.1556.
[7] Generative Adversarial Networks. https://arxiv.org/abs/1406.2661.
[8] Inception Score. https://en.wikipedia.org/wiki/Inception_score.
[9] FID Calculation. https://en.wikipedia.org/wiki/Fr%C3%A9chet_inception_distance.
[10] Image Super‐Resolution Using Very Deep Residual Channel Attention Networks. https://arxiv.org/abs/1807.02758.
[11] ESRGAN: Enhanced Super‐Resolution Generative Adversarial Networks. https://arxiv.org/abs/1809.00219.
[12] NTIRE 2024 Challenge on Image Super‐Resolution (×4): Methods and Results. https://arxiv.org/abs/2404.09790.
[13] Muse: Text‐To‐Image Generation via Masked Generative Transformers. https://arxiv.org/abs/2301.00704.
[14] VQGAN‐CLIP: Open Domain Image Generation and Editing with Natural Language Guidance. https://arxiv.org/abs/2204.08583.
[15] LAR‐SR: A Local Autoregressive Model for Image Super‐Resolution. https://openaccess.thecvf.com/content/CVPR2022/papers/Guo_LAR-SR_A_Local_Autoregressive_Model_for_Image_Super-Resolution_CVPR_2022_paper.pdf.
[16] Long Horizon Temperature Scaling. https://arxiv.org/abs/2302.03686.
[17] Learning Rate Scheduling. https://d2l.ai/chapter_optimization/lr‐scheduler.html.
[18] Adversarial Training. https://adversarial-ml-tutorial.org/adversarial_training/.
[19] Progressive Growing of GANs for Improved Quality, Stability, and Variation. https://arxiv.org/abs/1710.10196.
[20] CogView2: Faster and Better Text‐to‐Image Generation via Hierarchical Transformers. https://arxiv.org/abs/2204.14217.

[1] OpenAI’s DALL‐E 3. https://openai.com/index/dall-e-3/.
[2] Imagen 3. https://arxiv.org/abs/2408.07009.
[3] Adobe’s Firefly. https://www.adobe.com/products/firefly.html.
[4] Introducing ChatGPT. https://openai.com/index/chatgpt/.
[5] Zero‐Shot Text‐to‐Image Generation. https://arxiv.org/abs/2102.12092.
[6] Muse: Text‐To‐Image Generation via Masked Generative Transformers. https://arxiv.org/abs/2301.00704.
[7] Generative Modeling by Estimating Gradients of the Data Distribution. https://arxiv.org/abs/1907.05600.
[8] Learning Transferable Visual Models From Natural Language Supervision. https://arxiv.org/abs/2103.00020.
[9] Exploring the Limits of Transfer Learning with a Unified Text‐to‐Text Transformer. https://arxiv.org/abs/1910.10683.
[10] Hierarchical Text‐Conditional Image Generation with CLIP Latents. https://arxiv.org/abs/2204.06125.
[11] High‐Resolution Image Synthesis with Latent Diffusion Models. https://arxiv.org/abs/2112.10752.
[12] On the De‐duplication of LAION‐2B. https://arxiv.org/abs/2303.12733.
[13] xGen‐MM (BLIP‐3): A Family of Open Large Multimodal Models. https://www.arxiv.org/abs/2408.08872.
[14] U‐Net: Convolutional Networks for Biomedical Image Segmentation. https://arxiv.org/abs/1505.04597.
[15] Scalable Diffusion Models with Transformers. https://arxiv.org/abs/2212.09748.
[16] An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. https://arxiv.org/abs/2010.11929.
[17] Photorealistic Text‐to‐Image Diffusion Models with Deep Language Understanding. https://arxiv.org/abs/2205.11487.
[18] Denoising Diffusion Probabilistic Models. https://arxiv.org/abs/2006.11239.
[19] Classifier‐Free Diffusion Guidance. https://arxiv.org/abs/2207.12598.
[20] Denoising Diffusion Implicit Models. https://arxiv.org/abs/2010.02502.
[21] Introduction to Diffusion Models. https://lilianweng.github.io/posts/2021-07-11-diffusion-models/.
[22] Mixed Precision Training. https://arxiv.org/abs/1710.03740.
[23] FSDP tutorial. https://pytorch.org/tutorials/intermediate/FSDP_tutorial.html.
[24] DeepSpeed. https://github.com/microsoft/DeepSpeed.
[25] Parallel Sampling of Diffusion Models. https://arxiv.org/abs/2305.16317.
[26] Consistency Models. https://arxiv.org/abs/2303.01469.
[27] Inception score. https://en.wikipedia.org/wiki/Inception_score.
[28] FID calculation. https://en.wikipedia.org/wiki/Fr%C3%A9chet_inception_distance.
[29] CLIPScore: A Reference‐free Evaluation Metric for Image Captioning. https://arxiv.org/abs/2104.08718.
[30] Sora overview. https://openai.com/index/video-generation-models-as-world-simulators/.
[31] Imagen Video: High Definition Video Generation with Diffusion Models. https://arxiv.org/abs/2210.02303.
[32] Finetune Stable Diffusion Models with DDPO via TRL. https://huggingface.co/blog/trl-ddpo.
[33] Kandinsky: an Improved Text‐to‐Image Synthesis with Image Prior and Latent Diffusion. https://arxiv.org/abs/2310.03502.
[34] On the Importance of Noise Scheduling for Diffusion Models. https://arxiv.org/abs/2301.10972.
[35] Patchn’Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution. https://arxiv.org/abs/2307.06304.
[36] InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual‐Linguistic Tasks. https://arxiv.org/abs/2312.14238.
[37] BLIP‐2: Bootstrapping Language‐Image Pre‐training with Frozen Image Encoders and Large Language Models. https://arxiv.org/abs/2301.12597.
[38] Adding Conditional Control to Text‐to‐Image Diffusion Models. https://arxiv.org/abs/2302.05543.
[39] StyleDrop: Text‐to‐image generation in any style. https://research.google/blog/styledrop-text-to-image-generation-in-any-style/.

[1] Imagine yourself: Tuning‐Free Personalized Image Generation. https://ai.meta.com/research/publications/imagine-yourself-tuning-free-personalized-image-generation/.
[2] MoA: Mixture‐of‐Attention for Subject‐Context Disentanglement in Personalized Image Generation. https://arxiv.org/abs/2404.11565.
[3] InstantID: Zero‐shot Identity‐Preserving Generation in Seconds. https://arxiv.org/abs/2401.07519.
[4] An Image is Worth One Word: Personalizing Text‐to‐Image Generation using Textual Inversion. https://textual-inversion.github.io/.
[5] DreamBooth: Fine Tuning Text‐to‐Image Diffusion Models for Subject‐Driven Generation. https://arxiv.org/abs/2208.12242.
[6] LoRA: Low‐Rank Adaptation of Large Language Models. https://arxiv.org/abs/2106.09685.
[7] Language Models are Few‐Shot Learners. https://arxiv.org/abs/2005.14165.
[8] Classifier‐Free Diffusion Guidance. https://arxiv.org/abs/2207.12598.
[9] CLIPScore: A Reference‐free Evaluation Metric for Image Captioning. https://arxiv.org/abs/2104.08718.
[10] FID calculation. https://en.wikipedia.org/wiki/Fr%C3%A9chet_inception_distance.
[11] Inception score. https://en.wikipedia.org/wiki/Inception_score.
[12] Learning Transferable Visual Models From Natural Language Supervision. https://arxiv.org/abs/2103.00020.
[13] Emerging Properties in Self‐Supervised Vision Transformers. https://arxiv.org/abs/2104.14294.
[14] Contrastive Representation Learning. https://lilianweng.github.io/posts/2021-05-31-contrastive/.
[15] DINOv2: Learning Robust Visual Features without Supervision. https://arxiv.org/abs/2304.07193.
[16] An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine‐tuning. https://arxiv.org/abs/2308.08747.
[17] SDXL: Improving Latent Diffusion Models for High‐Resolution Image Synthesis. https://arxiv.org/abs/2307.01952.
[18] Deepfakes, Misinformation, and Disinformation in the Era of Frontier AI, Generative AI, and Large AI Models. https://arxiv.org/abs/2311.17394.
[19] Privacy‐Preserving Personal Identifiable Information (PII) Label Detection Using Machine Learning. https://ieeexplore.ieee.org/document/10307924.
[20] Does fine‐tuning GPT‐3 with the OpenAI API leak personally‐identifiable information? https://arxiv.org/abs/2307.16382.

[1] Video generation models as world simulators. https://openai.com/index/video-generation-models-as-world-simulators/.
[2] H100 Tensor Core GPU. https://www.nvidia.com/en-us/data-center/h100/.
[3] High‐Resolution Image Synthesis with Latent Diffusion Models. https://arxiv.org/abs/2112.10752.
[4] Meta Movie Gen. https://ai.meta.com/research/movie-gen/.
[5] Auto‐Encoding Variational Bayes. https://arxiv.org/abs/1312.6114.
[6] The Illustrated Stable Diffusion. https://jalammar.github.io/illustrated-stable-diffusion/.
[7] On the De‐duplication of LAION‐2B. https://arxiv.org/abs/2303.12733.
[8] The Llama 3 Herd of Models. https://arxiv.org/abs/2407.21783.
[9] LLaVA‐NeXT: A Strong Zero‐shot Video Understanding Model. https://llava-vl.github.io/blog/2024-04-30-llava-next-video/.
[10] Lumiere: A Space‐Time Diffusion Model for Video Generation. https://arxiv.org/abs/2401.12945.
[11] OpenSora Technical Report. https://github.com/hpcaitech/Open-Sora/blob/main/docs/report_02.md.
[12] RoFormer: Enhanced Transformer with Rotary Position Embedding. https://arxiv.org/abs/2104.09864.
[13] Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets. https://arxiv.org/abs/2311.15127.
[14] Emu Video: Factorizing Text‐to‐Video Generation by Explicit Image Conditioning. https://arxiv.org/abs/2311.10709.
[15] Imagen Video: High Definition Video Generation with Diffusion Models. https://arxiv.org/abs/2210.02303.
[16] HyperAttention: Long‐context Attention in Near‐Linear Time. https://arxiv.org/abs/2310.05869.
[17] Mixture of Experts Explained. https://huggingface.co/blog/moe.
[18] VBench: Comprehensive Benchmark Suite for Video Generative Models. https://vchitect.github.io/VBench-project/.
[19] Movie Gen Bench. https://github.com/facebookresearch/MovieGenBench.
[20] FID calculation. https://en.wikipedia.org/wiki/Fr%C3%A9chet_inception_distance.
[21] Inception score. https://en.wikipedia.org/wiki/Inception_score.
[22] The Unreasonable Effectiveness of Deep Features as a Perceptual Metric. https://arxiv.org/abs/1801.03924.
[23] Demystifying MMD GANs. https://arxiv.org/abs/1801.01401.
[24] Towards Accurate Generative Models of Video: A New Metric & Challenges. https://arxiv.org/abs/1812.01717.
[25] Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset. https://arxiv.org/abs/1705.07750.
[26] Rethinking the Inception Architecture for Computer Vision. https://arxiv.org/abs/1512.00567.
[27] Moonshot: Towards Controllable Video Generation and Editing with Multimodal Conditions. https://arxiv.org/abs/2401.01827.
[28] Progressive Distillation for Fast Sampling of Diffusion Models. https://arxiv.org/abs/2202.00512.
[29] Schedulers. https://huggingface.co/docs/diffusers/v0.9.0/en/api/schedulers.
[30] Photorealistic Text‐to‐Image Diffusion Models with Deep Language Understanding. https://arxiv.org/abs/2205.11487.
[31] CustomVideo: Customizing Text‐to‐Video Generation with Multiple Subjects. https://arxiv.org/abs/2401.09962.
[32] Control‐A‐Video: Controllable Text‐to‐Video Generation with Diffusion Models. https://controlavideo.github.io/.
[33] Introducing Stable Cascade. https://stability.ai/news/introducing-stable-cascade.