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0. (1Hack.Us) Premium Tutorials-Guides-Articles _ Community based Forum.url |
377B |
01. 01 HS Intro Dan And Cezanne V2-2K8KFEUxNbw.en.vtt |
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01. 01 HS Intro Dan And Cezanne V2-2K8KFEUxNbw.mp4 |
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01. 01 HS Intro Dan And Cezanne V2-2K8KFEUxNbw.zh-CN.vtt |
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01. 04 How Does Amazon Decide Which Features To Work On-KYG_LWDhg4I.en.vtt |
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01. 04 How Does Amazon Decide Which Features To Work On-KYG_LWDhg4I.mp4 |
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01. 04 How Does Amazon Decide Which Features To Work On-KYG_LWDhg4I.zh-CN.vtt |
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01. 05 Can You Explain The Idea Behind The GitHub Respository-Hk9ChDtv_nQ.en.vtt |
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01. 05 Can You Explain The Idea Behind The GitHub Respository-Hk9ChDtv_nQ.mp4 |
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01. 05 Can You Explain The Idea Behind The GitHub Respository-Hk9ChDtv_nQ.zh-CN.vtt |
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01. 06 Does Sagemaker Work With Certain Products Or Use Cases-9HSJp_i9LFw.en.vtt |
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01. 06 Does Sagemaker Work With Certain Products Or Use Cases-9HSJp_i9LFw.mp4 |
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01. 06 Does Sagemaker Work With Certain Products Or Use Cases-9HSJp_i9LFw.zh-CN.vtt |
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01. 1 SentimentRNN Intro V1-bQWUuaMc9ZI.en.vtt |
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01. 1 SentimentRNN Intro V1-bQWUuaMc9ZI.mp4 |
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01. 1 Weight Initialization V1-Ehc60si91Wg.en.vtt |
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01. Apresentando Alexis-38ExGpdyvJI.en.vtt |
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01. Arvato Final Project-qBR6A0IQXEE.en.vtt |
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01. Arvato Final Project-qBR6A0IQXEE.mp4 |
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01. Arvato Final Project-qBR6A0IQXEE.pt-BR.vtt |
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01. Arvato Final Project-qBR6A0IQXEE.zh-CN.vtt |
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01. Autoencoders.html |
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01. Autoencoders 01 Autoencoders V2 RENDER V2-a5zHMWOq0fc.mp4 |
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01. AWS Overview.html |
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01. Capstone project.html |
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01. Capstone Proposal.html |
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01. Congratulations!.html |
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01. Creating New Repositories - Intro-KT163BkqIeg.ar.vtt |
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01. Creating New Repositories - Intro-KT163BkqIeg.en.vtt |
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01. Deploying a Model in SageMaker.html |
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01. Deploying A Model With Sagemakerv2 RENDER V1 V2-nJCc4_9-iAQ.en.vtt |
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01. Deploying a Sentiment Analysis Model-LWcJtUKVkzo.en.vtt |
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01. Deploying a Sentiment Analysis Model-LWcJtUKVkzo.mp4 |
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01. Deployment Project.html |
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01. FAQ.html |
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01. Fraud Detection.html |
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01. Get Opportunities with LinkedIn.html |
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01. Gitfinal L1 01 Welcome-lbR82UD5F0c.ar.vtt |
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01. Implementing RNNs.html |
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01. Interview Segment Developing SageMaker.html |
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01. Intro.html |
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01. Intro.html |
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01. Intro.html |
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01. Introducing Alexis.html |
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01. Introducing Cezanne _ Dan.html |
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01. Introduction.html |
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01. Introduction.html |
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01. Introduction.html |
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01. Introduction.html |
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01. Introduction-5DfFaAl1Wmc.en.vtt |
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01. Introduction-5DfFaAl1Wmc.zh-CN.vtt |
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01. Introduction to Amazon SageMaker.html |
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01. Introduction to GPU Workspaces.html |
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01. Introduction To Software Engineering-7kphieW4yl4.en.vtt |
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01. Introduction To Software Engineering-7kphieW4yl4.mp4 |
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01. L2 01 Fraud Detection V1 RENDER V2-zDnyR5Tci5M.en.vtt |
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01. L4 00 Intro V2-ohVX3RUTghg.en.vtt |
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01. L5 00 Intro V2-7wI168JzBiU.zh-CN.vtt |
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01. M4L31 HSA Implementing RNNs V2 RENDERv1 V2-BHoiwB61ays.en.vtt |
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01. M4L31 HSA Implementing RNNs V2 RENDERv1 V2-BHoiwB61ays.mp4 |
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01. Natural Language Processing-UQBxJzoCp-I.en.vtt |
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01. NLP and Pipelines.html |
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01. Pre-Notebook Custom Models _ Moon Data.html |
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01. Project Overview.html |
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01. Project Overview.html |
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01. Prove Your Skills With GitHub.html |
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01. Sentiment RNN, Introduction.html |
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01. Tagging, Branching, And Merging - Intro-sMf_r4_z-Ls.mp4 |
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01. Time-Series Forecasting.html |
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01. Transfer Learning.html |
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01. Transfer Learning-yfPEROi3SPU.en.vtt |
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01. Transfer Learning-yfPEROi3SPU.mp4 |
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01. Transfer Learning-yfPEROi3SPU.pt-BR.vtt |
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01. Transfer Learning-yfPEROi3SPU.zh-CN.vtt |
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01. Updating a Model.html |
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01. Weight Initialization.html |
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01. Welcome!.html |
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01. Welcome.html |
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01. Welcome To Deployment-jQ2IZzga8Nw.en.vtt |
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01. Welcome To Deployment-jQ2IZzga8Nw.mp4 |
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01. Welcome To Deployment-jQ2IZzga8Nw.zh-CN.vtt |
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01. Welcome to the Machine Learning Engineer Program _ Projects.html |
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01. What is Version Control.html |
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01. Why Network-exjEm9Paszk.ar.vtt |
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01. Why Network-exjEm9Paszk.en.vtt |
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01. Why Network-exjEm9Paszk.mp4 |
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01. Why Network-exjEm9Paszk.pt-BR.vtt |
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02. 01 Time Series Notebook V2-OZJu6or8Fl0.en.vtt |
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02. 01 Time Series Notebook V2-OZJu6or8Fl0.mp4 |
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02. 01 What Is Amazon Sagemaker-JWRtWcd92E4.en.vtt |
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02. 01 What Is Amazon Sagemaker-JWRtWcd92E4.mp4 |
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02. 02 What Applications Are Enabled By Amazon-iXN30g70PJ0.en.vtt |
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02. 02 What Applications Are Enabled By Amazon-iXN30g70PJ0.mp4 |
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02. 03 Why Should Students Gain Skills In Sagemaker And Cloud Services-Hp6qTdiqU3g.en.vtt |
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02. 03 Why Should Students Gain Skills In Sagemaker And Cloud Services-Hp6qTdiqU3g.mp4 |
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02. 07 How Do You Label Data At Scale-G_E5N6k2knA.en.vtt |
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02. 07 How Do You Label Data At Scale-G_E5N6k2knA.mp4 |
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02. 07 How Do You Label Data At Scale-G_E5N6k2knA.zh-CN.vtt |
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02. 08 What_S Your Prediction Of What Sagemaker Will Prioritize In The Next 1-2 Years-git73JsQC1Y.en.vtt |
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02. 08 What_S Your Prediction Of What Sagemaker Will Prioritize In The Next 1-2 Years-git73JsQC1Y.mp4 |
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02. 08 What_S Your Prediction Of What Sagemaker Will Prioritize In The Next 1-2 Years-git73JsQC1Y.zh-CN.vtt |
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02. 18 Moon Data Custom Model V1-vb5ojq8Jw7k.en.vtt |
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02. 18 Moon Data Custom Model V1-vb5ojq8Jw7k.mp4 |
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02. 18 Moon Data Custom Model V1-vb5ojq8Jw7k.zh-CN.vtt |
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02. 2 Constant Weights V1-zR4fECgeZ7Y.en.vtt |
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02. 2 Simple Autoencoder V2-KbmfyDNxL5U.en.vtt |
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02. 2 Simple Autoencoder V2-KbmfyDNxL5U.mp4 |
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02. 2 Simple Autoencoder V2-KbmfyDNxL5U.pt-BR.vtt |
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02. Applications of CNNs.html |
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02. AWS Setup Instructions for Regular account.html |
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02. AWS Setup Instructions for Regular account.html |
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02. Boston Housing Example - Deploying the Model.html |
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02. Building a Sentiment Analysis Model (XGBoost).html |
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02. Clean and Modular Code.html |
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02. Constant Weights.html |
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02. Containment.html |
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02. Course Overview.html |
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02. Create A Repo From Scratch.html |
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02. Deployment L3 C1 V1-0PBsV-SzSlo.en.vtt |
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02. Displaying A Repository_s Commits.html |
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02. Forecasting Energy Consumption, Notebook.html |
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02. Git Add.html |
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02. How NLP Pipelines Work.html |
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02. Interview Segment New Features.html |
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02. Interview Segment What is SageMaker and Why Learn It.html |
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02. Introduction.html |
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02. Introduction to Hyperparameter Tuning.html |
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02. L4 Lesson Overview V2-9WQF-CCNdJ8.en.vtt |
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02. Meet Chris-0ccflD9x5WU.ar.vtt |
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02. Modifying The Last Commit.html |
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02. Moon Data _ Custom Models.html |
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02. Nd016 WebND Ud123 Gitcourse BETAMOJITO L1 11 Google Docs Revision History Walkthrough-GcvvbdKEchk.ar.vtt |
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02. Nd016 WebND Ud123 Gitcourse BETAMOJITO L3 11 Git Log Output Explained-xJfurQcVYfo.ar.vtt |
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02. NLP M1-L1 01 NLP Pipeline-vJx6oKlu_MM.en.vtt |
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02. NLP M1-L1 01 NLP Pipeline-vJx6oKlu_MM.zh-CN.vtt |
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02. Pre-Notebook Payment Fraud Detection.html |
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02. Pre-Notebook Sentiment RNN.html |
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02. Procedural vs. Object-Oriented Programming.html |
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02. Program Structure.html |
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02. Setting up a Notebook Instance.html |
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02. Software _ Data Requirements.html |
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02. Support.html |
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02. Tagging.html |
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02. Testing.html |
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02. Time-Series Prediction.html |
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02. Troubleshooting Possible Errors.html |
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02. Useful Layers.html |
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02. Useful Layers-kn4BN7z3UGQ.en.vtt |
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02. Useful Layers-kn4BN7z3UGQ.mp4 |
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02. Use Your Story to Stand Out.html |
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02. Version Control In Daily Use.html |
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02. What_s Ahead.html |
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02. Workspace Playground.html |
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02. Workspace Portfolio Exercise.html |
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03. 01 Transaction Data V1-bF65I3J6aqQ.en.vtt |
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03. 01 Transaction Data V1-bF65I3J6aqQ.mp4 |
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03. 03 Fine Tuning V1 RENDER V2-XOyb315xYbw.en.vtt |
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03. 03 Training Memory V1-sx7T_KP5v9I.en.vtt |
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03. 09 Do You Have Advice For Someone Who Wants To Learn More-Wgq4eukacqE.en.vtt |
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03. AWS SageMaker.html |
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03. Boston Housing Example - Tuning the Model.html |
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03. Boston Housing In-Depth - Deploying the Model.html |
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03. Branching.html |
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03. Building a Sentiment Analysis Model (Linear Learner).html |
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03. Changing How Git Log Displays Information.html |
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03. Class, Object, Method and Attribute.html |
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03. Clone An Existing Repo.html |
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03. ConNet 01 LessonOutline V1 V1-77LzWE1qQrc.en.vtt |
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03. Course Outline, Case Studies.html |
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03. Deployment L3 C2 V1-1lzWAzypJ9k.en.vtt |
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03. Exercise Payment Transaction Data.html |
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03. Fine-Tuning.html |
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03. Git Commit.html |
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03. GPU Workspace Playground.html |
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03. L1 03 Meet Andrew V1 V2-IPSwDqqk2Cc.en.vtt |
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03. L2 2 03 Testing Data Science V1 V4-AsnstNEMv1c.en.vtt |
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03. L3 03 Class Obj Methods Attributes V1 1 V2-yvVMJt09HuA.en.vtt |
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03. Lesson Outline.html |
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03. Machine Learning Workflow - Part 1 Introduction--ZtVV7RvGYY.en.vtt |
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03. Machine Learning Workflow - Part 1 Introduction--ZtVV7RvGYY.mp4 |
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03. Machine Learning Workflow - Part 1 Introduction--ZtVV7RvGYY.zh-CN.vtt |
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03. Meet Andrew.html |
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03. Nd016 WebND Ud123 Gitcourse BETAMOJITO L1 15 Git The Big Picture-dVil8e0yptQ.ar.vtt |
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03. Nd016 WebND Ud123 Gitcourse BETAMOJITO L1 17 Git The Big Picture 2-rFtUkk-sCqw.ar.vtt |
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03. Nd016 WebND Ud123 Gitcourse BETAMOJITO L3 25 Git Log Vs Git Log --Oneline Walkthru-rn6v_QgYFnU.ar.vtt |
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03. Nd016 WebND Ud123 Gitcourse BETAMOJITO L3 25 Git Log Vs Git Log --Oneline Walkthru-rn6v_QgYFnU.mp4 |
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03. Nd016 WebND Ud123 Gitcourse BETAMOJITO L3 27 Confession Corner-xtsugblSwrU.ar.vtt |
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03. Notebook Calculate Containment.html |
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03. Notebook Sentiment RNN.html |
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03. Possible Projects.html |
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03. Pre-Notebook Linear Autoencoder.html |
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03. Pre-Notebook Time-Series Forecasting.html |
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03. Problem Introduction.html |
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03. Random Uniform.html |
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03. Refactoring Code.html |
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03. Reverting A Commit.html |
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03. SageMaker Instance Utilization Limits.html |
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03. Testing and Data Science.html |
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03. Text Processing.html |
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03. Text Processing-pqheVyctkNQ.pt-BR.vtt |
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03. The Web.html |
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03. The World Wide Web-Rxn-zCyg_iA.en.vtt |
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03. The World Wide Web-Rxn-zCyg_iA.mp4 |
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03. Training _ Memory.html |
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03. Upload Data to S3.html |
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03. Why Use an Elevator Pitch.html |
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03. Workspace.html |
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04. 01 Writing Clean Code V1-wNaiahWCwkQ.en.vtt |
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04. 01 Writing Clean Code V1-wNaiahWCwkQ.mp4 |
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04. 02 Processing Energy Data V2-zxnoYK4sYgk.en.vtt |
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04. BertelsmannArvato Project Overview.html |
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04. ConNet 021 MNISTClassification V1 V2-a7bvIGZpcnk.en.vtt |
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04. Create Your Elevator Pitch.html |
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04. Data Pre-Processing.html |
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04. Deploying and Using a Sentiment Analysis Model.html |
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04. Object Oriented Programming Syntax-Y8ZVw1LHI8E.en.vtt |
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04. Resetting Commits.html |
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04. SageMaker Instance Utilization Limits.html |
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04. VGG Model _ Classifier.html |
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05. 03 LinearLearner V1-pjs5pP9OOMc.en.vtt |
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05. 4 EncodingWords Sol V1-4RYyn3zv1Hg.en.vtt |
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05. APIs [advanced version].html |
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05. Arvato Terms and Conditions.html |
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05. Bag of Words.html |
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05. ConNet 022 How Computers Interpret Images V1-mEPfoM68Fx4.en.vtt |
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05. Create A Repo - Outro-h7j4STDFCjs.ar.vtt |
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05. Create A Repo - Outro-h7j4STDFCjs.en.vtt |
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05. Defining _ Training an Autoencoder.html |
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05. Deployment L2 C2 V2-TRUCNy5Eqjc.en.vtt |
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05. Deployment L4 C4 V1-Q2Vthdca49I.en.vtt |
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05. Dynamic Programming.html |
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05. Encoding Words, Solution.html |
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05. Exercise Creating Time Series.html |
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05. Exercise OOP Syntax Practice - Part 1.html |
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05. Git Diff.html |
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05. How Computers Interpret Images.html |
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05. Interview with Art - Part 1.html |
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05. Knowledge.html |
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05. L1 032 Model Design V1 RENDER V2-zxNoSTZ3s90.en.vtt |
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05. Launch an Instance.html |
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05. Lesson Outro.html |
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05. LinearLearner _ Class Imbalance.html |
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05. Machine Learning Workflow.html |
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05. Merging.html |
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05. Mini-Project Solution - Tuning the Model.html |
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05. Mini-Project Updating a Sentiment Analysis Model.html |
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05. Model Design.html |
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05. Nd016 WebND Ud123 Gitcourse BETAMOJITO L1 30 Configure Terminal-CCYjHfBk9hw.ar.vtt |
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05. Nd016 WebND Ud123 Gitcourse BETAMOJITO L3 42 Git Log -P Output Walkthru-A8Kwocr-K8c.ar.vtt |
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05. Normal Distribution.html |
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05. Outro.html |
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05. Setting up a Notebook Instance.html |
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05. Unit Testing Tools.html |
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05. Use Your Elevator Pitch on LinkedIn.html |
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05. Viewing File Changes.html |
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05. Windows Setup.html |
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06. 02 Writing Modular Code V2-qN6EOyNlSnk.en.vtt |
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06. 23 Train Script V2-1cbvRmKvQIg.en.vtt |
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06. 44 Accessing The API Through Web Address SC 44 V2-nygWkgUQNfo.en.vtt |
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06. 4 A Simple Solution V2-Jh3mbomqpw8.en.vtt |
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06. A Couple of Notes about OOP.html |
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06. A Simple Solution.html |
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06. BertelsmannArvato Project Workspace.html |
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06. Cloning the Deployment Notebooks.html |
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06. ConNet 03 MLPStructure_ClassScore V1 V1-fP0Odiai8sk.en.vtt |
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06. Course Outro.html |
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06. Create Your Profile With SEO In Mind.html |
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06. Exercise Define a LinearLearner.html |
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06. Exercise Training Script.html |
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06. Getting Rid of Zero-Length.html |
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06. Having Git Ignore Files.html |
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06. Mini-Project Solution - Fixing the Error and Testing.html |
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06. MLP Structure _ Class Scores.html |
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06. Notebook Transfer Learning, Flowers.html |
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06. Viewing A Specific Commit.html |
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17. ConNet 14 MLPvsCNN V1 V2-Q7CR3cCOtJQ.en.vtt |
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17. Exercise Define a Model w Specifications.html |
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18. Advanced OOP Topics.html |
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18. Containers - Straight From the Experts.html |
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20. L2 17 Version Control In Data Science V1 V1-EQzrLC88Bzk.en.vtt |
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21. Comparing Cloud Providers.html |
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21. L1C13 Creating New Data Solution V4-4l2UHyyVV7Y.en.vtt |
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21. L2 18 Version Control Git Branches V1 V2-C92YcuwjZOs.en.vtt |
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22. Exercise K-means Estimator _ Selecting K.html |
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23. Closing Remarks On Deployment-fXl_MCYzcOU.en.vtt |
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23. Example Flask + Pandas.html |
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23. Exercise K-means Predictions (clusters).html |
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33. 21 CNNs For Image Classification RENDER V2-smaw5GqRaoY.en.vtt |
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