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种子名称： [CourseClub.Me] Coursera - Machine Learning Engineering for Production (MLOps) Specialization
文件类型： 视频
文件数目： 125个文件
文件大小： 1.21 GB
收录时间： 2021-11-4 21:51
已经下载： 3次
资源热度： 192
最近下载： 2024-5-5 06:52

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[CourseClub.Me] Coursera - Machine Learning Engineering for Production (MLOps) Specialization.torrent

1. Introduction to Machine Learning in Production/introduction-to-machine-learning-in-production - 01 - Specialization overview.mp439.95MB

1. Introduction to Machine Learning in Production/introduction-to-machine-learning-in-production - 02 - Welcome.mp420.35MB

1. Introduction to Machine Learning in Production/introduction-to-machine-learning-in-production - 03 - Steps of an ML Project.mp49.04MB

1. Introduction to Machine Learning in Production/introduction-to-machine-learning-in-production - 04 - Case study speech recognition.mp424.28MB

1. Introduction to Machine Learning in Production/introduction-to-machine-learning-in-production - 05 - Course outline.mp46.44MB

1. Introduction to Machine Learning in Production/introduction-to-machine-learning-in-production - 06 - Key challenges.mp425.62MB

1. Introduction to Machine Learning in Production/introduction-to-machine-learning-in-production - 07 - Deployment patterns.mp420.05MB

1. Introduction to Machine Learning in Production/introduction-to-machine-learning-in-production - 08 - Monitoring.mp418.25MB

1. Introduction to Machine Learning in Production/introduction-to-machine-learning-in-production - 09 - Pipeline monitoring.mp416.17MB

1. Introduction to Machine Learning in Production/introduction-to-machine-learning-in-production - 10 - Modeling overview.mp49.4MB

1. Introduction to Machine Learning in Production/introduction-to-machine-learning-in-production - 11 - Key challenges.mp48.98MB

1. Introduction to Machine Learning in Production/introduction-to-machine-learning-in-production - 12 - Why low average error isn't good enough.mp40B

1. Introduction to Machine Learning in Production/introduction-to-machine-learning-in-production - 13 - Establish a baseline.mp415.99MB

1. Introduction to Machine Learning in Production/introduction-to-machine-learning-in-production - 14 - Tips for getting started.mp411.07MB

1. Introduction to Machine Learning in Production/introduction-to-machine-learning-in-production - 15 - Error analysis example.mp413.74MB

1. Introduction to Machine Learning in Production/introduction-to-machine-learning-in-production - 16 - Prioritizing what to work on.mp410.23MB

1. Introduction to Machine Learning in Production/introduction-to-machine-learning-in-production - 17 - Skewed datastes.mp420.11MB

1. Introduction to Machine Learning in Production/introduction-to-machine-learning-in-production - 18 - Performance auditing.mp415.76MB

1. Introduction to Machine Learning in Production/introduction-to-machine-learning-in-production - 19 - Data-centric AI development.mp47.52MB

1. Introduction to Machine Learning in Production/introduction-to-machine-learning-in-production - 20 - A useful picture of data augmentation.mp40B

1. Introduction to Machine Learning in Production/introduction-to-machine-learning-in-production - 21 - Data augmentation.mp416.45MB

1. Introduction to Machine Learning in Production/introduction-to-machine-learning-in-production - 22 - Can adding data hurt.mp414.57MB

1. Introduction to Machine Learning in Production/introduction-to-machine-learning-in-production - 23 - Adding features.mp418.1MB

1. Introduction to Machine Learning in Production/introduction-to-machine-learning-in-production - 24 - Experiment tracking.mp48.86MB

1. Introduction to Machine Learning in Production/introduction-to-machine-learning-in-production - 25 - From big data to good data.mp48.85MB

1. Introduction to Machine Learning in Production/introduction-to-machine-learning-in-production - 26 - Why is data definition hard.mp410.06MB

1. Introduction to Machine Learning in Production/introduction-to-machine-learning-in-production - 27 - More label ambiguity examples.mp414.75MB

1. Introduction to Machine Learning in Production/introduction-to-machine-learning-in-production - 28 - Major types of data problems.mp420MB

1. Introduction to Machine Learning in Production/introduction-to-machine-learning-in-production - 29 - Small data and label consistency.mp415.3MB

1. Introduction to Machine Learning in Production/introduction-to-machine-learning-in-production - 30 - Improving label consistency.mp416.04MB

1. Introduction to Machine Learning in Production/introduction-to-machine-learning-in-production - 31 - Human level performance (HLP).mp418.51MB

1. Introduction to Machine Learning in Production/introduction-to-machine-learning-in-production - 32 - Raising HLP.mp421.81MB

1. Introduction to Machine Learning in Production/introduction-to-machine-learning-in-production - 33 - Obtaining data.mp420.71MB

1. Introduction to Machine Learning in Production/introduction-to-machine-learning-in-production - 34 - Data pipeline.mp410.66MB

1. Introduction to Machine Learning in Production/introduction-to-machine-learning-in-production - 35 - Meta-data, data provenance and lineage.mp40B

1. Introduction to Machine Learning in Production/introduction-to-machine-learning-in-production - 36 - Balanced train dev test splits.mp410.81MB

1. Introduction to Machine Learning in Production/introduction-to-machine-learning-in-production - 37 - What is scoping.mp48.11MB

1. Introduction to Machine Learning in Production/introduction-to-machine-learning-in-production - 38 - Scoping process.mp413.49MB

1. Introduction to Machine Learning in Production/introduction-to-machine-learning-in-production - 39 - Diligence on feasibility and value.mp423.23MB

1. Introduction to Machine Learning in Production/introduction-to-machine-learning-in-production - 40 - Diligence on value.mp413.19MB

1. Introduction to Machine Learning in Production/introduction-to-machine-learning-in-production - 41 - Milestones and resourcing.mp44.78MB

2. Machine Learning Data Lifecycle in Production/machine-learning-data-lifecycle-in-production - 01 - Specialization overview.mp439.95MB

2. Machine Learning Data Lifecycle in Production/machine-learning-data-lifecycle-in-production - 02 - Course Overview.mp416.02MB

2. Machine Learning Data Lifecycle in Production/machine-learning-data-lifecycle-in-production - 03 - Overview.mp418.03MB

2. Machine Learning Data Lifecycle in Production/machine-learning-data-lifecycle-in-production - 04 - ML Pipelines.mp410.2MB

2. Machine Learning Data Lifecycle in Production/machine-learning-data-lifecycle-in-production - 05 - Importance of Data.mp412.61MB

2. Machine Learning Data Lifecycle in Production/machine-learning-data-lifecycle-in-production - 06 - Example Application Suggesting Runs.mp410.98MB

2. Machine Learning Data Lifecycle in Production/machine-learning-data-lifecycle-in-production - 07 - Responsible Data Security, Privacy & Fairness.mp40B

2. Machine Learning Data Lifecycle in Production/machine-learning-data-lifecycle-in-production - 08 - Case Study Degraded Model Performance.mp40B

2. Machine Learning Data Lifecycle in Production/machine-learning-data-lifecycle-in-production - 09 - Data and Concept Change in Production ML.mp40B

2. Machine Learning Data Lifecycle in Production/machine-learning-data-lifecycle-in-production - 10 - Process Feedback and Human Labeling.mp414.56MB

2. Machine Learning Data Lifecycle in Production/machine-learning-data-lifecycle-in-production - 11 - Detecting Data Issues.mp412.44MB

2. Machine Learning Data Lifecycle in Production/machine-learning-data-lifecycle-in-production - 12 - TensorFlow Data Validation.mp48.82MB

2. Machine Learning Data Lifecycle in Production/machine-learning-data-lifecycle-in-production - 13 - Introduction to Preprocessing.mp410.05MB

2. Machine Learning Data Lifecycle in Production/machine-learning-data-lifecycle-in-production - 14 - Preprocessing Operations.mp411.82MB

2. Machine Learning Data Lifecycle in Production/machine-learning-data-lifecycle-in-production - 15 - Feature Engineering Techniques.mp420.32MB

2. Machine Learning Data Lifecycle in Production/machine-learning-data-lifecycle-in-production - 16 - Feature Crosses.mp45.28MB

2. Machine Learning Data Lifecycle in Production/machine-learning-data-lifecycle-in-production - 17 - Preprocessing Data at Scale.mp417.63MB

2. Machine Learning Data Lifecycle in Production/machine-learning-data-lifecycle-in-production - 18 - TensorFlow Transform.mp417.53MB

2. Machine Learning Data Lifecycle in Production/machine-learning-data-lifecycle-in-production - 19 - Hello World with tf.Transform.mp410.38MB

2. Machine Learning Data Lifecycle in Production/machine-learning-data-lifecycle-in-production - 20 - Feature Spaces.mp46.32MB

2. Machine Learning Data Lifecycle in Production/machine-learning-data-lifecycle-in-production - 21 - Feature Selection.mp45.71MB

2. Machine Learning Data Lifecycle in Production/machine-learning-data-lifecycle-in-production - 22 - Filter Methods.mp48.01MB

2. Machine Learning Data Lifecycle in Production/machine-learning-data-lifecycle-in-production - 23 - Wrapper Methods.mp47.26MB

2. Machine Learning Data Lifecycle in Production/machine-learning-data-lifecycle-in-production - 24 - Embedded Methods.mp47.49MB

2. Machine Learning Data Lifecycle in Production/machine-learning-data-lifecycle-in-production - 25 - Data Journey.mp410.6MB

2. Machine Learning Data Lifecycle in Production/machine-learning-data-lifecycle-in-production - 26 - Introduction to ML Metadata.mp413.16MB

2. Machine Learning Data Lifecycle in Production/machine-learning-data-lifecycle-in-production - 27 - ML Metadata in Action.mp46.19MB

2. Machine Learning Data Lifecycle in Production/machine-learning-data-lifecycle-in-production - 28 - Schema Development.mp46.1MB

2. Machine Learning Data Lifecycle in Production/machine-learning-data-lifecycle-in-production - 29 - Schema Environments.mp45.08MB

2. Machine Learning Data Lifecycle in Production/machine-learning-data-lifecycle-in-production - 30 - Feature Stores.mp410.96MB

2. Machine Learning Data Lifecycle in Production/machine-learning-data-lifecycle-in-production - 31 - Data Warehouse.mp45.92MB

2. Machine Learning Data Lifecycle in Production/machine-learning-data-lifecycle-in-production - 32 - Data Lakes.mp44.75MB

2. Machine Learning Data Lifecycle in Production/machine-learning-data-lifecycle-in-production - 33 - Semi-supervised Learning.mp46.49MB

2. Machine Learning Data Lifecycle in Production/machine-learning-data-lifecycle-in-production - 34 - Active Learning.mp45.97MB

2. Machine Learning Data Lifecycle in Production/machine-learning-data-lifecycle-in-production - 35 - Weak Supervision.mp46.95MB

2. Machine Learning Data Lifecycle in Production/machine-learning-data-lifecycle-in-production - 36 - Data Augmentation.mp46.17MB

2. Machine Learning Data Lifecycle in Production/machine-learning-data-lifecycle-in-production - 37 - Time Series.mp412.65MB

2. Machine Learning Data Lifecycle in Production/machine-learning-data-lifecycle-in-production - 38 - Sensors and Signals.mp44.34MB

3. Machine Learning Modeling Pipelines in Production/machine-learning-modeling-pipelines-in-production - 01 - Course Overview.mp410.47MB

3. Machine Learning Modeling Pipelines in Production/machine-learning-modeling-pipelines-in-production - 02 - Hyperparameter Tuning.mp46.59MB

3. Machine Learning Modeling Pipelines in Production/machine-learning-modeling-pipelines-in-production - 03 - Keras Autotuner Demo.mp49.08MB

3. Machine Learning Modeling Pipelines in Production/machine-learning-modeling-pipelines-in-production - 04 - Intro to AutoML.mp47.9MB

3. Machine Learning Modeling Pipelines in Production/machine-learning-modeling-pipelines-in-production - 05 - Understanding Search Spaces.mp42.68MB

3. Machine Learning Modeling Pipelines in Production/machine-learning-modeling-pipelines-in-production - 06 - Search Strategies.mp46.78MB

3. Machine Learning Modeling Pipelines in Production/machine-learning-modeling-pipelines-in-production - 07 - Measuring AutoML Efficacy.mp45.37MB

3. Machine Learning Modeling Pipelines in Production/machine-learning-modeling-pipelines-in-production - 08 - AutoML on the Cloud.mp411.84MB

3. Machine Learning Modeling Pipelines in Production/machine-learning-modeling-pipelines-in-production - 09 - Assignment Setup.mp43.22MB

3. Machine Learning Modeling Pipelines in Production/machine-learning-modeling-pipelines-in-production - 10 - Dimensionality Effect on Performance.mp40B

3. Machine Learning Modeling Pipelines in Production/machine-learning-modeling-pipelines-in-production - 11 - Curse of Dimensionality.mp413.83MB

3. Machine Learning Modeling Pipelines in Production/machine-learning-modeling-pipelines-in-production - 12 - Curse of Dimensionality an Example.mp40B

3. Machine Learning Modeling Pipelines in Production/machine-learning-modeling-pipelines-in-production - 13 - Manual Dimensionality Reduction.mp40B

3. Machine Learning Modeling Pipelines in Production/machine-learning-modeling-pipelines-in-production - 15 - Algorithmic Dimensionality Reduction.mp40B

3. Machine Learning Modeling Pipelines in Production/machine-learning-modeling-pipelines-in-production - 16 - Principal Components Analysis.mp40B

3. Machine Learning Modeling Pipelines in Production/machine-learning-modeling-pipelines-in-production - 17 - Other Techniques.mp410.86MB

3. Machine Learning Modeling Pipelines in Production/machine-learning-modeling-pipelines-in-production - 18 - Mobile, IoT, and Similar Use Cases.mp40B

3. Machine Learning Modeling Pipelines in Production/machine-learning-modeling-pipelines-in-production - 19 - Benefits and Process of Quantization.mp40B

3. Machine Learning Modeling Pipelines in Production/machine-learning-modeling-pipelines-in-production - 20 - Post Training Quantization.mp48.65MB

3. Machine Learning Modeling Pipelines in Production/machine-learning-modeling-pipelines-in-production - 21 - Quantization Aware Training.mp46.26MB

3. Machine Learning Modeling Pipelines in Production/machine-learning-modeling-pipelines-in-production - 22 - Pruning.mp420.42MB

3. Machine Learning Modeling Pipelines in Production/machine-learning-modeling-pipelines-in-production - 23 - Distributed Training.mp415.94MB

3. Machine Learning Modeling Pipelines in Production/machine-learning-modeling-pipelines-in-production - 24 - High-Performance Ingestion.mp415.77MB

3. Machine Learning Modeling Pipelines in Production/machine-learning-modeling-pipelines-in-production - 26 - Teacher and Student Networks.mp44.35MB

3. Machine Learning Modeling Pipelines in Production/machine-learning-modeling-pipelines-in-production - 27 - Knowledge Distillation Techniques.mp40B

3. Machine Learning Modeling Pipelines in Production/machine-learning-modeling-pipelines-in-production - 29 - Model Performance Analysis.mp415.69MB

3. Machine Learning Modeling Pipelines in Production/machine-learning-modeling-pipelines-in-production - 31 - TFMA in Practice.mp45.52MB

3. Machine Learning Modeling Pipelines in Production/machine-learning-modeling-pipelines-in-production - 32 - Model Debugging Overview.mp44.72MB

3. Machine Learning Modeling Pipelines in Production/machine-learning-modeling-pipelines-in-production - 33 - Benchmark Models.mp41.57MB

3. Machine Learning Modeling Pipelines in Production/machine-learning-modeling-pipelines-in-production - 35 - Adversarial Attack Demo.mp411.36MB

3. Machine Learning Modeling Pipelines in Production/machine-learning-modeling-pipelines-in-production - 36 - Residual Analysis.mp43.09MB

3. Machine Learning Modeling Pipelines in Production/machine-learning-modeling-pipelines-in-production - 37 - Model Remediation.mp46.21MB

3. Machine Learning Modeling Pipelines in Production/machine-learning-modeling-pipelines-in-production - 38 - Fairness.mp45.27MB

3. Machine Learning Modeling Pipelines in Production/machine-learning-modeling-pipelines-in-production - 39 - Measuring Fairness.mp48.01MB

3. Machine Learning Modeling Pipelines in Production/machine-learning-modeling-pipelines-in-production - 40 - Continuous Evaluation and Monitoring.mp40B

3. Machine Learning Modeling Pipelines in Production/machine-learning-modeling-pipelines-in-production - 41 - Explainable AI.mp411.59MB

3. Machine Learning Modeling Pipelines in Production/machine-learning-modeling-pipelines-in-production - 42 - Model Interpretation Methods.mp413.01MB

3. Machine Learning Modeling Pipelines in Production/machine-learning-modeling-pipelines-in-production - 43 - Intrinsically Interpretable Models.mp40B

3. Machine Learning Modeling Pipelines in Production/machine-learning-modeling-pipelines-in-production - 44 - Model Agnostic Methods.mp42.46MB

3. Machine Learning Modeling Pipelines in Production/machine-learning-modeling-pipelines-in-production - 45 - Partial Dependence Plots.mp47.41MB

3. Machine Learning Modeling Pipelines in Production/machine-learning-modeling-pipelines-in-production - 46 - Permutation Feature Importance.mp40B

3. Machine Learning Modeling Pipelines in Production/machine-learning-modeling-pipelines-in-production - 47 - Shapley Values.mp410.21MB

3. Machine Learning Modeling Pipelines in Production/machine-learning-modeling-pipelines-in-production - 48 - SHapley Additive exPlanations (SHAP).mp40B

3. Machine Learning Modeling Pipelines in Production/machine-learning-modeling-pipelines-in-production - 49 - Testing Concept Activation Vectors.mp40B

3. Machine Learning Modeling Pipelines in Production/machine-learning-modeling-pipelines-in-production - 50 - LIME.mp42.11MB

3. Machine Learning Modeling Pipelines in Production/machine-learning-modeling-pipelines-in-production - 51 - AI Explanations.mp410.35MB