English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 4h 03m | 685 MB
Train and predict your models using the Google Cloud ML Engine
TensorFlow has become the first choice for deep learning tasks because of the way it facilitates building powerful and sophisticated neural networks. The Google Cloud Platform is a great place to run TF models at scale, and perform distributed training and prediction.
This course shows you how to use Google Cloud to train TensorFlow models and use them to predict results for multiple users. You will learn to efficiently train neural networks using large datasets and to serve your training models.
With this video course, you will use the power of Google’s Cloud Platform to train deep neural networks faster. This course supplies various examples of training in Google Cloud ML Engine. You will also learn to run predictions for your model using the cloud. You will explore topics such as cloud infrastructures, convolutional neural networks, training deep networks, model serving, and more.
By the end of the course, you will be expert at training and serving neural models, and beyond.
Learn
- Get access to powerful computers with GPUs organized in clusters to optimize your performance
- Train bigger models faster using the Google Cloud infrastructure
- Explore machine types and learn how to configure clusters to solve problems
- Train deep learning models using the Google Cloud ML Engine
- Run classical machine learning algorithms with TensorFlow
- Build a state-of-the-art object detection network
- Run your trained models to get predictions using the ML Engine API
Table of Contents
A Quick Start with Google Cloud Platform
1 The Course Overview
2 Introduction to the Google Cloud Platform
3 Getting a GCP Account
4 Walking Through the GCP Console and Google Cloud SDK
5 Google Compute and Google Storage
6 AI Platform Overview
7 Example Workflow with AI Platform Notebooks
Machine Learning with TensorFlow Fundamentals
8 What Is TensorFlow and What are TensorFlow APIs
9 Lab – Programming in TensorFlow
10 Using TensorBoard
11 Overview of Machine Learning
12 Lab – Linear Regression on TensorFlow
13 Logistic Regression
14 Lab – Logistic Regression on TensorFlow
15 K-Nearest Neighbor (KNN)
16 Lab – KNN on TensorFlow
Basic Model Training with TensorFlow 2.0
17 Lab – Project Setup
18 Lab – Staging Data and Preprocessing
19 Model Training Using Keras API
20 Lab – Testing Model Predictions
21 Lab – Exporting the Model for Production
Advanced Model Training with TensorFlow 2.0
22 Project Setup
23 Lab – Staging Data and Preprocessing
24 Defining the Model
25 Lab – Defining the Distribution Strategy
26 Lab – Distributed Training
27 Lab – Monitor Learning Process in TensorBoard
Serving Model Predictions with TensorFlow on GCP
28 Overview – Methods for Serving TensorFlow Models on GCP
29 Lab – Setting Up TensorFlow Serving
30 Inference Using TensorFlow Serving
31 Lab – Deploying Models to AI Platform
32 Lab – Inference Using AI Platform
33 Lab – Setting Up Cloud Functions for TensorFlow
34 Lab – Inference Using Cloud Functions
Neural Networks
35 Introduction to Neural Network
36 Feedforward and Activation Function
37 Gradient Descent
38 Backpropagation
39 One Hot Encoding, Softmax, and Cross-Entropy
40 Lab – Building a Simple Neural Network on TensorFlow
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