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Nlp Engineer

A comprehensive guide for Nlp Engineer

""1PREREQUISITES AND FUNDAMENTALS2TEXT PROCESSING AND TRADITIONAL NLP3DEEP LEARNING FOR NLP4MODERN NLP (TRANSFORMERS AND LLMS)5ADVANCED NLP TASKS, APPLICATIONS, AND EVALUATION6LLMOPS AND DEPLOYMENT FOR NLPNlp Engineer1.1 Programming LanguagesPythonAdvanced conceptsOOPgeneratorsdecoratorsmemory managementasynchronousprogrammingC++Optionalforperformance-criticalsystemscustom CUDA/Tritonkernelsand inference enginesRustOptionalincreasingly used forfast tokenizerstoolingand safe backendsystemsSQLEssential for dataextraction and dataengineering tasks1.2 Mathematics and StatisticsLinear AlgebraVectorsMatricesTensorsEigenvalues/EigenvectorsSVDMatrix MultiplicationCalculusDerivativesPartial DerivativesChain RuleGradientsJacobiansHessiansVector CalculusProbability andStatisticsProbabilityDistributionsBayes' TheoremHypothesis TestingMarkov ChainsInformation TheoryEntropyCross-EntropyKL DivergenceMutual Information1.3 Machine Learning FundamentalsSupervised LearningRegressionClassificationSVMsDecision TreesRandom ForestsXGBoost/LightGBMUnsupervised LearningClusteringDimensionalityReduction like PCAt-SNEUMAPModel EvaluationCross-validationPrecisionRecallF1-ScoreROC/AUCOptimizationAlgorithmsGradient DescentSGDAdamAdamWRMSpropLearning RateSchedulers1.4 Software Engineering & Data Engineering (Crucial for Production)Version ControlGitGitHubGitLabCI/CD PipelinesGitHub ActionsJenkinsGitLab CIAPI DevelopmentFastAPIFlaskgRPCWebSocketsData Structures &AlgorithmsTestingPytestUnit TestingIntegration TestingMockingData ProcessingPipelinesApache SparkAirflowPandasPolarsdbt2.1 Text PreprocessingTokenizationWordSentenceSubword: BPEWordPieceUnigramByte-level BPESentencePiecetiktokenStemming andLemmatizationStop Word RemovalText NormalizationLowercasingPunctuation removalSpell checkingUnicode normalizationRegular Expressions(Regex) and StringManipulationString SimilarityMetricsLevenshtein/EditDistanceJaccard SimilarityCosine Similarity2.2 Feature Extraction (Vectorization)Bag of WordsBoWTF-IDFTerm Frequency-InverseDocument FrequencyBM25Best Matching 25 -crucial for modernlexical retrievalN-Grams2.3 Traditional NLP TasksSequence LabelingHidden Markov Models -HMMsConditional RandomFields - CRFsViterbi AlgorithmPart-of-Speech (POS)TaggingNamed EntityRecognitionNERDependency Parsing andConstituency ParsingChunkingShallow ParsingText ClassificationNaive BayesLogistic RegressionSVMTopic ModelingLDANMFBERTopic2.4 Core NLP LibrariesNLTKNatural LanguageToolkitspaCyIndustrial-strengthNLPGensimTopic modeling andvector space modelingScikit-learnStanzaStanford NLPTextBlob3.1 Neural Network FundamentalsFeedforward NeuralNetworksMLPBackpropagation, BPTT(BackpropagationThrough Time), andComputational GraphsActivation FunctionsReLUGELUSigmoidTanhSwishSiLULoss FunctionsCross-EntropyNegativeLog-LikelihoodContrastive LossFocal LossTriplet LossRegularizationDropoutWeight DecayLayer NormalizationBatch Normalization3.2 Word EmbeddingsWord2VecCBOWSkip-gramNegative SamplingGloVeGlobal Vectors forWord RepresentationFastTextSubword embeddingsContextualized WordEmbeddingsELMo3.3 Sequence Models & CNNs for TextConvolutional NeuralNetworks1D-CNNs for textclassificationRecurrent NeuralNetworksRNNsLong Short-Term MemoryLSTMGated Recurrent UnitsGRUBidirectional RNNsBiLSTMSeq2Seq ModelsEncoder-DecoderArchitectureAttention MechanismBahdanauLuongPointer Networks andCopy Mechanisms3.4 Deep Learning FrameworksPyTorchIndustry standard forresearch and modernNLPTensorFlow / KerasStill used in legacyenterprise systems andspecific productionenvironmentsJAX / FlaxGrowing in popularityfor large-scaledistributed trainingTritonOpenAI's language forGPU programming4.1 Transformer Architecture Deep-DiveSelf-AttentionMechanism & ScaledDot-Product AttentionMulti-Head Attention,Multi-Query Attention(MQA), Grouped-QueryAttentionGQAPositional EncodingAbsoluteRelativeRoPEALiBiNormalizationStrategiesPost-LNPre-LNRMSNormTransformer Encoderand Decoder BlocksKV Caching,PagedAttention, andChunked PrefillAdvanced ActivationsSwiGLUGeGLUFlashAttention (v1,v2, v3) andRingAttention4.2 Pre-trained Language Models (PLMs)Encoder-only ModelsBERTRoBERTaALBERTDeBERTaV3Decoder-only ModelsGPT seriesLlama 2/3MistralQwenClaudeGeminiEncoder-Decoder ModelsT5BARTFLAN-T5Mixture of Experts(MoE) ArchitectureMixtral 8x7BDeepSeekSmall Language Models(SLMs)Phi-3GemmaLlama-3-8BState Space Models(SSMs) & RNN RevivalsMambaJambaRWKV4.3 Fine-tuning and AlignmentTask-specificFine-tuning &Instruction TuningSupervised Fine-Tuning- SFTParameter-EfficientFine-Tuning (PEFT):LoRA, DoRA, PiSSA,QLoRA, Adapters,Prompt/Prefix TuningHigh-PerformanceFine-tuningUnslothAxolotlLlama-FactoryTorchtuneAlignment Techniques:RLHF, DPO, IterativeDPO, PPO, KTO, ORPO,CPO, Constitutional AIRLAIF4.4 Large Language Models (LLMs) Ecosystem & RAGPrompt EngineeringZero-shotFew-shotChain-of-ThoughtTree of ThoughtsReActAlgorithmic PromptOptimizationDSPyTextGradRetrieval-AugmentedGeneration (RAG)Architectures: NaiveRAG vs Advanced RAG(Modular RAG),GraphRAG, Self-RAG,FLARERAG Components:Chunking strategies,Embedding models(OpenAI, BGE, E5,Nomic, Jina),Re-ranking, QueryTransformationsVector DatabasesPineconeMilvusChromaFAISSQdrantWeaviatepgvectorOrchestrationFrameworksLangChainLlamaIndexHaystackSemantic KernelAgentic WorkflowsTool use/FunctionCallingAutoGPTLangGraphAutoGenCrewAI5.1 Core ApplicationsMachine TranslationText SummarizationExtractive andAbstractiveQuestion AnsweringQA - Extractive andGenerativeSentiment Analysis andAspect-based EmotionDetectionCode Generation andMath ReasoningInformation ExtractionRelation ExtractionCoreference ResolutionKnowledge GraphsText Style Transferand Grammatical ErrorCorrection5.2 Conversational AIChatbots and DialogueSystemsIntent Recognition andSlot FillingTask-oriented dialogsystems and StateTrackingFrameworksRasaVoiceflowBotpress5.3 Multimodal NLPVision-Language ModelsCLIPLLaVAQwen-VLFlamingoGPT-4V/4oGemini 1.5 ProText-to-ImageGenerationStable DiffusionDALL-E 3MidjourneyAudio-Text ModelsWhisperSeamlessM4TBarkVALL-EVideo-Language ModelsSora5.4 Model Evaluation and BenchmarkingLLM BenchmarksMMLUHumanEvalGSM8KHELMMT-BenchChatbot ArenaGPQARAG EvaluationRAGASARESTruLensLLM-as-a-Judge andReward ModelEvaluationTraditional NLPMetricsBLEUROUGEMETEORBERTScoreMoverScore6.1 Model Serving and Inference OptimizationPrecision & Data TypesFP16BF16FP8Microscaling FormatsInference EnginesvLLMText GenerationInference (TGI)TensorRT-LLMOllamaSGLangAphroditeQuantizationINT8INT4AWQGPTQGGUF/GGMLEXL2SmoothQuantModel FormatsSafetensorsONNXSpeculative Decodingand MedusaContinuous Batching,Chunked Prefill, andMulti-LoRA ServingLoRAXEdge and On-deviceDeploymentLlama.cppMLC-LLMWebGPUExecuTorch6.2 Cloud and InfrastructureCloud PlatformsAWS SageMakerGoogle Vertex AIAzure MLModalRunPodTogether AIBasetenHugging Face EcosystemHubInference EndpointsTransformersDatasetsTRLContainerization(Docker) andOrchestrationKubernetesKServeRay ServeDistributed TrainingDeepSpeedMegatron-LMFSDPRayHorovodGPU/TPU Utilizationand ProfilingNsight SystemsPyTorch Profiler6.3 Monitoring, Security, and MaintenanceModel MonitoringData DriftConcept DriftPerformancedegradationLatency/ThroughputmonitoringLogging,Observability, andPrompt TracingLangSmithPhoenixWeights & BiasesMLflowSecurity &VulnerabilitiesPrompt InjectionJailbreakingData LeakageData PoisoningResponsible AIBias detectionToxicity filteringGuardrailsNeMo GuardrailsLlama GuardDifferential Privacy