Linkedin Learning – NVIDIA Certified Associate Generative AI LLMs NCA GENL Cert Prep 2026 BOOKWARE-LERNSTUF | Apps-Tutorials | MKV,PDF | 1.76 GiB
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Linkedin Learning
NVIDIA Certified Associate Generative AI LLMs NCA GENL Cert Prep
2026
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Release Date : 2026-06-10
Publisher : Linkedin Learning
Title : NVIDIA Certified Associate Generative AI LLMs NCA GENL Cert Prep
Year : 2026
Month : 06
Category : Unknown
Language : English
Section : BOOKWARE
Files : 04 x 500 MB
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https://www.linkedin.com/learning/nvidia-certified-associate-generative-ai-llm
s-nca-genl-cert-prep
Generative AI and large language models (LLMs) are reshaping how organizations
build intelligent applications, and NVIDIAs tools are at the center of that
shift. In this course, explore the foundations of AI and machine learning,
transformer architecture, model selection, and customization techniques using
NVIDIAs AI stack, including TensorRT, RAPIDS, and NeMo. Learn how to train and
customize models for real-world use, discover ethical AI practices, and gain
hands-on experience with deep learning workflows. By the end of this course,
youTll be prepared to sit for the NCA-GENL certification exam and ready to
apply your skills to practical AI projects. This course was created by Packt
Publishing. We are pleased to host this training in our library.
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.. - - _//- ___\____---' [ R E C R U i T i N G ] . - _\_ ___--'-_ -- -_ .
iF YOU HAVE ANYTHiNG TO OFFER
CONTACT US ViA THE SCENE, OR EMAiL ADDRESS BELOW
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.. - - ___- _/ \____--' \ [ G R E E T i N G S ] _-------' \\___-- - // .
GREETiNGS TO ALL THE PAST AND PRESENT SCENE GROUPS AND SCENERS WHO MADE
STUF AVAiLABLE FOR THOSE THAT MAY NOT OTHERWISE HAD THE PRiViLEGE
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.. -_- -/_- __--\__ [ C O N T A C T D E T A i L S ] --- -___-- - \ - .
lernstuf [at] proton [dot] me
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32M 001-introduction.mkv 6.6M 002-why_companies_invest_in_generative_aiand_why_it_matters_to_you.mkv 4.6M 003-know_about_certification_tracks.mkv 6.4M 004-nca_genl_certification.mkv 3.4M 005-what_we_will_cover.mkv 8.8M 006-ai_and_ml_stack.mkv 4.6M 007-ai_infrastructure.mkv 15M 008-gpus_for_ai_and_ml.mkv 16M 009-gpu_vs_cpu.mkv 5.7M 010-gpu_architecture.mkv 17M 011-memory_pooling.mkv 6.7M 012-cloud_vs_on_premises.mkv 17M 013-network_infrastructure.mkv 12M 014-storage_infrastructure.mkv 4.6M 015-module_1_summary.mkv 21M 016-ai_and_ml.mkv 27M 017-deep_learning.mkv 15M 018-a_simple_use_case.mkv 16M 019-what_is_a_model.mkv 38M 020-training_a_model.mkv 4.7M 021-module_2_summary.mkv 17M 022-what_is_generative_ai.mkv 33M 023-foundation_model.mkv 19M 024-large_language_model.mkv 16M 025-road_to_generative_ai.mkv 16M 026-transformers.mkv 5.5M 027-module_3_summary.mkv 12M 028-transformers_the_engine_behind_modern_ai.mkv 15M 029-how_transformers_work.mkv 15M 030-tokenization.mkv 2.3M 031-encoding.mkv 5.0M 032-word_embedding.mkv 2.7M 033-decoding.mkv 2.0M 034-output.mkv 4.9M 035-positional_encoding.mkv 15M 036-transformer_explainer.mkv 42M 037-attention_mechanism.mkv 16M 038-multi_head_attention.mkv 23M 039-encoder_decoder.mkv 8.7M 040-which_one_to_use.mkv 16M 041-prediction_strategy.mkv 7.9M 042-module_4_summary.mkv 20M 043-how_to_pick_the_right_ai_foundation_model.mkv 24M 044-model_selection_process.mkv 5.9M 045-slm_vs_llm.mkv 18M 046-model_evaluation.mkv 23M 047-metrics_that_matters.mkv 5.4M 048-metrics_comparison.mkv 23M 049-cross_validation.mkv 13M 050-a_b_testing.mkv 8.3M 051-selecting_best_model.mkv 6.8M 052-module_5_summary.mkv 8.3M 053-why_customize_a_model.mkv 23M 054-prompt_engineering.mkv 18M 055-system_prompt.mkv 29M 056-retrieval_augmented_generation_rag.mkv 15M 057-chunking.mkv 12M 058-transfer_learning.mkv 8.3M 059-transfer_learning_approaches.mkv 26M 060-transfer_learning_use_cases.mkv 12M 061-fine_tuning.mkv 14M 062-knowledge_distillation.mkv 8.5M 063-accuracy_on_a_validation_set.mkv 16M 064-module_6_summary.mkv 26M 065-model_training_analogy.mkv 9.2M 066-beer_or_wine.mkv 11M 067-order_of_steps.mkv 29M 068-data_collection.mkv 9.5M 069-data_processing_regex.mkv 19M 070-data_processing_eda.mkv 17M 071-eda_techniques.mkv 7.6M 072-model_training.mkv 16M 073-model_training_phases.mkv 9.4M 074-model_evaluation-2.mkv 9.7M 075-model_deployment.mkv 15M 076-options_for_deployment.mkv 8.5M 077-onnx_format.mkv 12M 078-quantization.mkv 6.1M 079-post_deployment.mkv 15M 080-module_7_summary.mkv 26M 081-nvidia_ecosystem.mkv 24M 082-nvidia_gpus.mkv 28M 083-cuda.mkv 9.7M 084-nvidia_smi.mkv 17M 085-nvidia_rapids.mkv 15M 086-how_rapids_work.mkv 22M 087-nvidia_tensorrt.mkv 9.1M 088-tensorrt_vs_quantization_vs_onnx.mkv 31M 089-nvidia_nemo.mkv 17M 090-nvidia_triton.mkv 18M 091-dynamic_batching.mkv 23M 092-ngc_catalog.mkv 16M 093-other_integration.mkv 17M 094-module_8_summary.mkv 8.7M 095-ethical_ai.mkv 13M 096-building_trustworthy_ai.mkv 9.4M 097-confidential_computing.mkv 11M 098-nvidia_nemo_guardrails.mkv 27M 099-embedding_classifier.mkv 5.4M 100-data_lineage_tracking.mkv 5.2M 101-red_teaming.mkv 12M 102-data_augmentation.mkv 6.5M 103-accountability.mkv 3.4M 104-explainable_ai.mkv 15M 105-module_9_summary.mkv 29M 106-forward_and_back_propagation.mkv 16M 107-vanishing_gradients.mkv 9.9M 108-challenges_of_vanishing_gradients.mkv 21M 109-neuron.mkv 22M 110-inside_a_neuron.mkv 47M 111-is_it_a_cat.mkv 19M 112-relu_vs_sigmoid.mkv 26M 113-layer_normalization.mkv 15M 114-named_entity_relationship.mkv 8.1M 115-diffusion_algorithms.mkv 17M 116-forward_and_reverse_diffusion.mkv 20M 117-forward_diffusion.mkv 16M 118-reverse_diffusion.mkv 13M 119-rapid_application_development_rad.mkv 9.3M 120-hugging_face_transformers.mkv 14M 121-module_10_summary.mkv 13M 122-prepare_for_the_certification_exam.mkv 292K Exercises 272K Exercises/9781807786076.pdf 12K Exercises/GitHub Codespaces.pdf 4.0K Exercises/source_code_link.txt 652K LERNSTUF 648K LERNSTUF/lernstuf_introduction_2025.rar 1.8G total
File: 001-introduction.mkv Size: 33278563 bytes (31.74 MiB), duration: 00:02:24, avg.bitrate: 1849 kb/s Audio: aac, 44100 Hz, stereo Video: h264, yuv420p, 1920x1080, 30.00 fps(r) (eng) Subtitles: eng
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Keywords: Linkedin, Learning, 8211, NVIDIA, Certified, Associate, Generative, LLMs, NCA, GENL, Cert, Prep, 2026, BOOKWARE, LERNSTUFDownload from Nitroflare
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