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Урок 1.
00:03:20
Introduction and Welcome Message
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Урок 2.
00:10:44
Introduction, Key Tips and Best Practices
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Урок 3.
00:17:55
Course Outline and Key Learning Outcomes
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Урок 4.
00:02:51
Project Introduction and Welcome Message
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Урок 5.
00:11:16
Task #1 - Understand the Problem Statement & Business Case
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Урок 6.
00:12:26
Task #2 - Import Libraries and Datasets
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Урок 7.
00:09:36
Task #3 - Perform Image Visualizations
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Урок 8.
00:16:52
Task #4 - Perform Images Augmentation
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Урок 9.
00:07:45
Task #5 - Perform Data Normalization and Scaling
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Урок 10.
00:20:33
Task #6 - Understand Artificial Neural Networks (ANNs) Theory & Intuition
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Урок 11.
00:18:03
Task #7 - Understand ANNs Training & Gradient Descent Algorithm
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Урок 12.
00:13:01
Task #8 - Understand Convolutional Neural Networks and ResNets
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Урок 13.
00:12:46
Task #9 - Build ResNet to Detect Key Facial Points
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Урок 14.
00:07:41
Task #10 - Compile and Train Facial Key Points Detector Model
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Урок 15.
00:04:55
Task #11 - Assess Trained ResNet Model Performance
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Урок 16.
00:12:01
Task #12 - Import and Explore Facial Expressions (Emotions) Datasets
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Урок 17.
00:07:23
Task #13 - Visualize Images for Facial Expression Detection
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Урок 18.
00:13:32
Task #14 - Perform Image Augmentation
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Урок 19.
00:14:58
Task #15 - Build & Train a Facial Expression Classifier Model
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Урок 20.
00:14:14
Task #16 - Understand Classifiers Key Performance Indicators (KPIs)
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Урок 21.
00:13:36
Task #17 - Assess Facial Expression Classifier Model
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Урок 22.
00:07:38
Task #18 - Make Predictions from Both Models 1. Key Facial Points & 2. Emotion
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Урок 23.
00:10:02
Task #19 - Save Trained Model for Deployment
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Урок 24.
00:04:25
Task #20 - Serve Trained Model in TensorFlow 2.0 Serving
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Урок 25.
00:08:24
Task #21 - Deploy Both Models and Make Inference
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Урок 26.
00:02:41
Project Introduction and Welcome Message
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Урок 27.
00:16:35
Task #1 - Understand the Problem Statement and Business Case
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Урок 28.
00:11:38
Task #2 - Import Libraries and Datasets
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Урок 29.
00:20:45
Task #3 - Visualize and Explore Datasets
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Урок 30.
00:10:38
Task #4 - Understand the Intuition behind ResNet and CNNs
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Урок 31.
00:11:51
Task #5 - Understand Theory and Intuition Behind Transfer Learning
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Урок 32.
00:21:08
Task #6 - Train a Classifier Model To Detect Brain Tumors
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Урок 33.
00:09:05
Task #7 - Assess Trained Classifier Model Performance
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Урок 34.
00:13:24
Task #8 - Understand ResUnet Segmentation Models Intuition
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Урок 35.
00:14:21
Task #9 - Build a Segmentation Model to Localize Brain Tumors
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Урок 36.
00:04:06
Task #10 - Train ResUnet Segmentation Model
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Урок 37.
00:12:28
Task #11 - Assess Trained ResUNet Segmentation Model Performance
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Урок 38.
00:02:11
Project Introduction and Welcome Message
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Урок 39.
00:07:18
Task #1 - Understand AI Applications in Marketing
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Урок 40.
00:13:51
Task #2 - Import Libraries and Datasets
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Урок 41.
00:16:47
Task #3 - Perform Exploratory Data Analysis (Part #1)
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Урок 42.
00:19:18
Task #4 - Perform Exploratory Data Analysis (Part #2)
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Урок 43.
00:16:58
Task #5 - Understand Theory and Intuition Behind K-Means Clustering Algorithm
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Урок 44.
00:08:48
Apply Elbow Method to Find the Optimal Number of Clusters
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Урок 45.
00:15:55
Task #7 - Apply K-Means Clustering Algorithm
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Урок 46.
00:10:33
Task #8 - Understand Intuition Behind Principal Component Analysis (PCA)
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Урок 47.
00:08:40
Task #9 - Understand the Theory and Intuition Behind Auto-encoders
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Урок 48.
00:13:05
Task #10 - Apply Auto-encoders and Perform Clustering
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Урок 49.
00:02:40
Project Introduction and Welcome Message
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Урок 50.
00:11:02
Task #1 - Understand the Problem Statement & Business Case
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Урок 51.
00:04:47
Task #2 - Import Libraries and Datasets
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Урок 52.
00:20:44
Task #3 - Visualize and Explore Dataset
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Урок 53.
00:06:03
Task #4 - Clean Up the Data
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Урок 54.
00:20:46
Task #5 - Understand the Theory & Intuition Behind XG-Boost Algorithm
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Урок 55.
00:19:50
Task #6 - Understand XG-Boost Algorithm Key Steps
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Урок 56.
00:07:54
Task #7 - Train XG-Boost Algorithm Using Scikit-Learn
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Урок 57.
00:06:58
Task #8 - Perform Grid Search and Hyper-parameters Optimization
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Урок 58.
00:07:16
Task #9 - Understand XG-Boost in AWS SageMaker
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Урок 59.
00:14:26
Task #10 - Train XG-Boost in AWS SageMaker
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Урок 60.
00:09:43
Task #11 - Deploy Model and Make Inference
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Урок 61.
00:13:11
Task #12 - Train and Deploy Model Using AWS AutoPilot (Minimal Coding Required!)
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Урок 62.
00:01:47
Project Introduction and Welcome Message
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Урок 63.
00:11:14
Task #1 - Understand the Problem Statement & Business Case
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Урок 64.
00:07:07
Task #2 - Import Model with Pre-trained Weights
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Урок 65.
00:09:08
Task #3 - Import and Merge Images
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Урок 66.
00:09:45
Task #4 - Run the Pre-trained Model and Explore Activations
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Урок 67.
00:19:28
Task #5 - Understand the Theory & Intuition Behind Deep Dream Algorithm
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Урок 68.
00:05:38
Task #6 - Understand The Gradient Operations in TF 2.0
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Урок 69.
00:09:11
Task #7 - Implement Deep Dream Algorithm Part #1
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Урок 70.
00:10:27
Task #8 - Implement Deep Dream Algorithm Part #2
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Урок 71.
00:06:46
Task #9 - Apply DeepDream Algorithm to Generate Images
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Урок 72.
00:07:21
Task #10 - Generate DeepDream Video
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Урок 73.
00:01:51
Project Introduction and Welcome Message
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Урок 74.
00:08:54
What is AWS and Cloud Computing
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Урок 75.
00:09:26
Key Machine Learning Components and AWS Tour
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Урок 76.
00:06:20
Regions and Availability Zones
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Урок 77.
00:14:33
Amazon S3
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Урок 78.
00:12:42
EC2 and Identity and Access Management (IAM)
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Урок 79.
00:05:48
AWS Free Tier Account Setup and Overview
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Урок 80.
00:09:15
AWS SageMaker Overview
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Урок 81.
00:10:47
AWS SageMaker Walk-through
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Урок 82.
00:08:42
AWS SageMaker Studio Overview
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Урок 83.
00:07:00
AWS SageMaker Studio Walk-through
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Урок 84.
00:11:04
AWS SageMaker Model Deployment