种子简介
种子名称:
340 - AWS Certified Machine Learning - Specialty
文件类型:
视频
文件数目:
95个文件
文件大小:
5 GB
收录时间:
2020-9-2 21:30
已经下载:
3次
资源热度:
165
最近下载:
2024-12-18 22:56
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种子包含的文件
340 - AWS Certified Machine Learning - Specialty.torrent
001 - Course Introduction.mp440.88MB
002 - About the Training Architect.mp421.76MB
003 - About the Exam.mp4152.23MB
004 - Artificial Intelligence.mp421.17MB
005 - What Is Machine Learning?.mp440.75MB
006 - What Is Deep Learning?.mp438.57MB
007 - Section Introduction.mp411.25MB
008 - Machine Learning Lifecycle.mp459.8MB
009 - Supervised, Unsupervised, and Reinforcement.mp440.67MB
010 - Optimization.mp431.59MB
011 - Regularization.mp415.99MB
012 - Hyperparameters.mp424.14MB
013 - Validation.mp414.97MB
014 - Section Introduction.mp49.75MB
015 - Feature Selection and Engineering.mp435.56MB
016 - Principal Component Analysis (PCA).mp440.5MB
017 - Missing and Unbalanced Data.mp443.4MB
018 - Label and One Hot Encoding.mp414.79MB
019 - Splitting and Randomization.mp416.63MB
020 - RecordIO Format.mp421.1MB
021 - Section Introduction.mp48.12MB
022 - Logistical Regression.mp432.31MB
023 - Linear Regression.mp424.26MB
024 - Support Vector Machines.mp421.53MB
025 - Decision Trees.mp438.69MB
026 - Random Forests.mp425.95MB
027 - K-Means.mp433.84MB
028 - K-Nearest Neighbour.mp413.83MB
029 - Latent Dirichlet Allocation (LDA) Algorithm.mp438.9MB
030 - Section Introduction.mp47.99MB
031 - Neural Networks.mp471.87MB
032 - Convolutional Neural Networks (CNN).mp445.07MB
033 - Recurrent Neural Networks (RNN).mp441.96MB
034 - Section Introduction.mp417.04MB
035 - Confusion Matrix.mp449MB
036 - Sensitivity and Specificity.mp484.23MB
037 - Accuracy and Precision.mp431.64MB
038 - ROC_AUC.mp469.45MB
039 - Gini Impurity.mp435.11MB
040 - F1 Score.mp425.38MB
041 - Introduction to Jupyter Notebooks.mp466.91MB
042 - ML and DL Frameworks.mp440.28MB
043 - TensorFlow.mp483.18MB
044 - PyTorch.mp439.69MB
045 - MXNet.mp432.39MB
046 - Scikit-learn.mp494.28MB
047 - S3.mp490.6MB
048 - Glue.mp483.87MB
049 - Athena.mp486.56MB
050 - QuickSight.mp447.42MB
051 - Kinesis, Streams, Firehose, Video, and Analytics.mp482.27MB
052 - EMR with Spark.mp432.85MB
053 - EC2 for ML.mp460.82MB
054 - Amazon ML.mp413.03MB
055 - Section Introduction.mp437.5MB
056 - Amazon Rekognition (Images) Part 1.mp490.78MB
057 - Amazon Rekognition (Images) Part 2 - the API.mp4161.34MB
058 - Amazon Rekognition (Video).mp448.47MB
059 - Amazon Polly.mp452.04MB
060 - Amazon Transcribe.mp453.32MB
061 - Amazon Translate.mp488.63MB
062 - Amazon Comprehend.mp471.78MB
063 - Amazon Lex.mp477.77MB
064 - Amazon Service Chaining with AWS Step Functions.mp466.14MB
065 - Section Introduction.mp419.05MB
066 - What is Amazon SageMaker?.mp439.73MB
067 - The Three Stages.mp413.36MB
068 - Control (Console_SDK_Notebooks).mp484.27MB
069 - SageMaker Notebooks.mp495.83MB
070 - Data Preprocessing.mp459.61MB
071 - Ground Truth.mp440.26MB
072 - Preprocessing Image Data (Pinehead NotPinehead).mp4192.2MB
073 - Algorithms.mp469.62MB
074 - SageMaker Algorithms - Architecture 1.mp452.83MB
075 - SageMaker Algorithms - Architecture 2.mp448.95MB
076 - SageMaker Algorithms - Architecture 3.mp423.65MB
077 - Training an Image Classifier - Part 1 (Pinehead NotPinehead).mp4126.75MB
078 - Training an Image Classifier - Part 2 (Pinehead NotPinehead).mp431.65MB
079 - Hyperparameter Tuning.mp457.57MB
080 - Inference Pipelines.mp418.2MB
081 - Real-Time and Batch Inference.mp424.18MB
082 - Deploy an Image Classifier (Pinehead, NotPinehead).mp4110.49MB
083 - Accessing Inference from Apps.mp416.97MB
084 - Create a custom API for inference - Part 1 (Pinehead NotPinehead).mp465.2MB
085 - Create a custom API for inference - Part 2 (Pinehead NotPinehead).mp4105.93MB
086 - Securing SageMaker Notebooks.mp4112.56MB
087 - SageMaker and the VPC.mp423.12MB
088 - Section Introduction.mp410.16MB
089 - DeepLens - Part 1.mp4171.78MB
090 - DeepLens - Part 2.mp450.88MB
091 - DeepRacer - Part 1.mp4175.45MB
092 - DeepRacer - Part 2.mp438.74MB
093 - How to Answer Questions.mp4119.63MB
094 - How to Prepare.mp462.68MB
095 - Goodbye!.mp440.43MB