English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 5h 57m | 1.92 GB
Take your Deep Learning skills to the next level using TensorFlow and Google Cloud AI
Deep Learning uses a cascade of multiple layers of nonlinear processing units for feature extraction and transformation on large volumes of data in order to make decisions about high dimensional data.
If you’re looking to scale out your Deep Learning models and deploy your model into production then look no further because this video course will help you get the most out of TensorFlow and Keras to accelerate the training of your Deep Learning models and deploy your model at scale on the Cloud. Tools and frameworks such as TensorFlow, Keras, and Google Cloud MLE are used to showcase the strengths of various approaches, trade-offs, and building blocks for creating, training and evaluating your distributed deep learning models with GPU(s) and deploying your model to the Cloud. You will learn how to design and train your deep learning models and scale them out for larger datasets and complex neural network architectures on multiple GPUs using Google Cloud ML Engine. You’ll learn distributed techniques such as how parallelism and distribution work using low-level TensorFlow and high-level TensorFlow APIs and Keras.
Towards the end of the course, you will develop, train, and deploy your models using TensorFlow and Google Cloud Machine Learning Engine.
This video course adopts a tutorial-like approach to provide the right blend of theory,practical, and best practices in this rapidly developing area while providing a grounding in essential concepts that remain timeless and practical.
What You Will Learn
- Gain hands-on experience designing, training, and deploying your Deep Learning models with TensorFlow and Keras to handle large volumes of data and complex neural network architectures
- Get a better understanding of how parallelism and distribution work in TensorFlow and Keras
- Design and experiment with complex neural network architectures using low-level TensorFlow while also using TensorFlow’s high level APIs and Keras
- Scale out training and prediction using different distributed techniques such as data parallelism using GPUs on our local machine and in the cloud using Google Cloud ML Engine
- Develop, train, and deploy models using Google Cloud MLE to production.
- Deploy your model as a production level API
Table of Contents
Installation
1 The Course Overview
2 Installation
Keras Introduction
3 Introduction
4 Keras Backends
5 Design and Compile a Model
6 Model Training, Evaluation, and Prediction
7 Training with Data Augmentation
8 Training with Transfer Learning and Data Augmentation
Scaling Deep Learning Using Keras and TensorFlow
9 Introduction to TensorFlow
10 Introduction to TensorBoard
11 Types of Parallelism in Deep Learning – Synchronous and Asynchronous
12 Distributed TensorFlow
13 Configuring Keras to use TensorFlow for Distributed Problems
Training, Tuning, and Serving Our Model in the Cloud
14 Introduction
15 Introduction to Google Cloud Machine Learning Engine
16 Datasets, Feature Columns, and Estimators
17 Representing Data in TensorFlow
18 Quick Dive into TensorFlow Estimators
19 Creating Data Input Pipelines
20 Setting Up Our Estimator
21 Packaging Our Model
22 Training with Google Cloud ML Engine
23 Hyperparameter Tuning in the Cloud
24 Deploying Our Model for Prediction
25 Creating Our Prediction API
26 Wrapping Up
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