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Google Cloud Machine Learning with TensorFlow
A Quick Start with Google Cloud Platform
The Course Overview (6:49)
Introduction to the Google Cloud Platform (7:07)
Getting a GCP Account (2:16)
Walking Through the GCP Console and Google Cloud SDK (7:43)
Google Compute and Google Storage (5:49)
AI Platform Overview (2:55)
Example Workflow with AI Platform Notebooks (11:54)
Machine Learning with TensorFlow Fundamentals
What Is TensorFlow and What are TensorFlow APIs? (7:45)
Lab: Programming in TensorFlow (8:53)
Using TensorBoard (3:44)
Overview of Machine Learning (6:27)
Lab: Linear Regression on TensorFlow (10:39)
Logistic Regression (4:31)
Lab: Logistic Regression on TensorFlow (5:35)
K-Nearest Neighbor (KNN) (3:57)
Lab: KNN on TensorFlow (6:44)
Basic Model Training with TensorFlow 2.0
Lab: Project Setup (2:00)
Lab: Staging Data and Preprocessing (5:14)
Model Training Using Keras API (2:02)
Lab: Testing Model Predictions (3:50)
Lab: Exporting the Model for Production (4:29)
Advanced Model Training with TensorFlow 2.0
Project Setup (3:56)
Lab: Staging Data and Preprocessing (10:29)
Defining the Model (3:14)
Lab: Defining the Distribution Strategy (4:02)
Lab: Distributed Training (3:29)
Lab: Monitor Learning Process in TensorBoard (6:31)
Serving Model Predictions with TensorFlow on GCP
Overview: Methods for Serving TensorFlow Models on GCP (15:07)
Lab: Setting Up TensorFlow Serving (1:47)
Inference Using TensorFlow Serving (7:02)
Lab: Deploying Models to AI Platform (5:49)
Lab: Inference Using AI Platform (5:27)
Lab: Setting Up Cloud Functions for TensorFlow (7:34)
Lab: Inference Using Cloud Functions (3:57)
Neural Networks
Introduction to Neural Network (6:25)
Feedforward and Activation Function (3:37)
Gradient Descent (6:43)
Backpropagation (5:19)
One Hot Encoding, Softmax, and Cross-Entropy (7:30)
Lab: Building a Simple Neural Network on TensorFlow (14:21)
Lab: Exporting the Model for Production
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