پیشنهاد فرادرس

آموزش مدل های Keras در محیط های Multi Cloud

دسته بندی ها: آموزش Cloud ، آموزش شبکه ، آموزش های پلورال سایت (Pluralsight) ، هوش مصنوعی ، یادگیری ماشینی (Machine Learning)

در این دوره با توابع ساده و شهودی و کلاس هایی که Keras ارائه می دهد برای ساخت مدل های عصبی شبکه، نحوه ساختن و آموزش شبکه های عصبی، شبکه های عصبی مکرر و توانایی آنها را برای ذخیره حالت با استفاده از خروجی ها، ایجاد و آموزش این مدل ها  در سیستم عامل های محبوب Cloud، Azure، AWS و GCP، بررسی IaaS و PaaS، ساخت مدل های یادگیری ماشینی، رگرسیون خطی و غیره آشنا می شوید.

سرفصل:

  • معرفی دوره
  • پیش نیازها
  • نورون ها و شبکه های عصبی
  • نصب TensorFlow و Keras
  • کار با مدل های متوالی
  • ذخیره مدل ها
  • CNNs و فیلد پذیرفته شده محلی
  • معماری های CNN
  • طبقه بندی با استفاده از CNN
  • استفاده از Functional API در Keras
  • LSTM Cell
  • رایانش ابری - IaaS در مقابل PaaS
  • AMI یادگیری عمیق AWS
  • معرفی AWS SageMaker
  • و غیره
Keras Models Can Help In Multi Cloud Environments Publisher:Pluralsight Author:Janani Ravi Duration:2h 56m Level:Beginner

This in depth training gives you a first hand look at Keras Models. For multi cloud environments, Keras Models is what you need to create the perfect network.
As machine learning and deep learning techniques become popular, the importance of intuitive and simple abstractions that enable fast development and quick prototyping of these models become critical. In this course, Building and Deploying Keras Models in a Multi-cloud Environment, you'll learn the simple and intuitive functions and classes that Keras offers to build neural network models. First, you'll gain an understanding of the basic working of a neuron and how neural networks are structured and trained. You'll study the simplest form of a model, a network for linear regression which can be built using the simple Sequential model class in Keras, along with other forms of Sequential models such as convolutional neural networks for image classification. Next, you'll move on to recurrent neural networks and understand their ability to store state using outputs from previous time instances, and build a sequence-to-sequence RNN for language translation from English to French using Keras' functional API. Lastly, you'll learn to build and train these models on the most popular cloud platforms, Azure, AWS and the GCP. You'll study their IaaS and PaaS offerings for machine learning and use deep learning VMs or the distributed training framework to train our models. By the end of this course, you will be very comfortable using the Keras high-level API to build your machine learning models and know how you can take these models to the cloud for training at scale.
Course Overview
Course Overview
2m
Composing Sequential Models in Keras
Module Overview
1m
Prerequisites and Course Outline
1m
Neurons and Neural Networks
5m
Introducing Keras
2m
Demo: Installing TensorFlow and Keras
1m
Working with Sequential Models
4m
Training a Neural Network: Gradient Descent Optimization and Back Propagation
3m
Saving Models
1m
Demo: Sequential Model for Linear Regression
5m
Demo: Classification Using the Iris Dataset - Data Preparation and One Hot Encoding
4m
Demo: Sequential Model for Classification and Saving to Disk
5m
Demo: Loading a Saved Model
1m
CNNs and the Local Receptive Field
1m
Convolution
2m
Feature Maps and the Convolutional Layer
3m
Pooling
2m
CNN Architectures
1m
Demo: CNN for Image Classification - Cat and Dog
4m
Demo: Training A CNN
3m
Demo: Classification Using a CNN
2m
Demo: Visualizing NNs Using GraphViz and Quiver
4m
Using the Functional API in Keras
Module Overview
0m
The Functional API
2m
The Recurrent Neuron
3m
Training RNNs - Vanishing and Exploding Gradients
4m
The LSTM Cell
3m
Working with RNNs Using Vectors and Sequences
3m
The Encoder Decoder for Language Translation
1m
Representing Inputs and Targets in Our Language Translation Model
4m
Inputs to the Decoder During Training
2m
Inputs to the Decoder During Prediction
2m
Demo: Getting the Language Translations Dataset
5m
Demo: One Hot Encoding of English and French Sentences
5m
Demo: Training the Encoder Decoder
5m
Demo: Encoder Decoder for Prediction
3m
Demo: English to French Translation
4m
Running Keras on Microsoft Azure
Module Overview
1m
Cloud Computing: IaaS vs. PaaS
5m
Azure Machine Learning Offerings
4m
Demo: Keras on the Azure Deep Learning VM
6m
Running Keras on Amazon AWS
Module Summary
0m
The AWS Deep Learning AMI
1m
Introducing AWS SageMaker
4m
Demo: Running Keras on the AWS Deep Learning AMI
6m
Running Keras on Google Cloud ML Engine
Module Overview
0m
Introducing Cloud MLE
4m
Training a Model Using Cloud MLE
2m
Steps Involved in Working with Cloud MLE
1m
Demo: Enabling Cloud MLE and Creating Buckets on the GCP
3m
Demo: Iris Classifier Code and Creating a Python Package
3m
Demo: Running Training Locally and on the Cloud
3m
Module Summary and Further Study
1m

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