Deep Learning with Apache SINGA & R Training

Live Online & Classroom Enterprise Training

Master core concepts of SINGA distributed deep learning platform. Be ready to deploy large scale industry-grade models with better accuracy, lesser training time and lesser programming effort.Gain an in-depth understanding of basic and advanced deep learning algorithms in our Deep Learning with Apache SINGA & R training.

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Key Features
  • Lifetime Access

  • CloudLabs

  • 24x7 Support

  • Real-time code analysis and feedback

  • 100% Money Back Guarantee

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What is Deep Learning with R Course about?

Gain firsthand expertise on installing, configuring, and deploying Apache SINGA and make use of the Python APIs for running synchronous, asynchronous and hybrid training frameworks. Through practical guided exercises leverage the stochastic gradient descent (SGD) to find optimized parameters for many popular feed-forward models such as convolutional neural networks (CNN) and recurrent neural networks (RNN).

In cloud labs, practice job submission, training linear regression models, multi-layer perceptron models, convolutional neural network models, and a recurrent neural network model.

Be the expert of deploying complex deep learning models using Apache SINGA and Python.

 Suggested Audience

Researchers, engineers, and developers seeking to utilize Apache SINGA as a deep learning framework

 

What are the objectives of Deep Learning with R Course ?

This Deep Learning with SINGA course enables you to:

  • Install and configure Apache SINGA through conda packages
  • Gain a deep understanding of major components of the software stack of SINGA
  • Access implementation and modules of SINGA stack through Python APIs
  • Train a model on a single node or in a cluster Submit a job for training
  • Train a linear regression model, multi-layer perceptron model, CNN, RNN
  • Deliver scalable models that can work on the complex and large datasets

 

Prerequisite

Required: Working knowledge of Linux, Python, Machine Learning, Deep Learning Models

Available Training Modes

Live Online Training

12 Hours

Classroom Training

 

2 Days
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Who is Deep Learning with R Course for?

  • Anyone who wants to add Deep Learning with R skills to their profile
  • Teams getting started on Deep Learning with R projects
  • What are the prerequisites for Deep Learning with R Course?

    Course Outline

    • Installation & Introduction
      • From Conda
      • From Source in Linux with CPU Only
      • Major Components of Software Stack
      • Core: Tensor and Devices Abstactions for storing model variable and operation
      • Model: Higher Level Classes for Machine Learning
      • IO: Data Loading, Processing, Message Passing
    • Core: Device
      • Python API for optimization of memory and executio
    • Core: Tensor
      • Tensor Implementation for storing variables
      • Linear algebraic operation with Tensor
      • Python API
    • Model
      • Layer-Wrapping C++ Layers for simpler construction of APIs
      • Layer-Python API
      • Feedforward Net-Neural net classes for constructing nets using Layers
      • Initializer-Popular initialization methods for parameters values
      • Initializer-Python API
      • Loss-Training loss implementation using python tensor
      • Metric-Evaluating model's performance using Python
      • Optimizer-Optimizing model parameters
    • IO
      • Data-Loading and fetching data batches
      • Image Tool-Utility model for image augmentation
    • Quick Start
      • Quick Start
      • Training on a Single Node
      • Preparing data and job configuration
      • Training without parallelism
      • Asynchronous parallel training
      • Synchronous parallel training
      • Training in a Cluster
      • Starting Zookeeper
      • Training with GPUS
    • Job Submission
      • NeuralNet
      • TrainOneBatch
      • Updater
      • Cluster Topology
    • Training Linear Regression
      • Using Tensor module of PySINGA
      • Generating Training Data
      • Training via SGD
    • Training Multi-layer Perceptron (MLP)
      • Importing PySINGA modules
      • Generating Training Data
      • Creating MLP Model
      • Training the model
    • Training convolution neural network (CNN)
      • Data Preparation
      • Creating CNN Model
      • Initializing the Parameters
      • Setting up the optimizer and tensors
      • Conducting SGD
      • Saving and Loading the model
      • Prediction & Dubugging
    • Train Char-Recurrent Neural Network (RNN)
      • Dataset Pre-processing
      • Creating the network
      • Conducting SGD
      • Checkpoint
      • Sample

    Who is the instructor for this training?

    The trainer for this Deep Learning with Apache SINGA & R Training has extensive experience in this domain, including years of experience training & mentoring professionals.

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