Deep Learning with TensorFlow Training

Deep Learning with TensorFlow Programming is specifically used for creating Data Flow across the complete Deep Learning Application

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Duration: 2 Days

Course fee:$399.00 (₹0)

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Product Description

TensorFlow Deep Learning Training focuses particularly for the developers intending to learn Machine Learning Problems. The complete Open Source software library for the flow of data across the range of tasks leads to the development of TensorFlow Application. This training includes the process of Linear and Regression using various mathematical expressions for creation data flow for Machine Learning Application. TensorFlow corresponds to the primary data flow representation across range of programming tasks. Leading Hyperparameter Optimization Algorithms are explained in detail in this training. Complete understanding of the fully connected Deep Networks is made available in the training. Apart from fully connected network, Convolutional networks are also considered in this training. The training will benefit the developer for understanding various classification metrics used when dealing with large number of Data and its related information for Applications.

Objectives

  • Understanding the basic understanding of Linear Algebra and Calculus
  • Designing systems having capability to detect object in images
  • Understanding the concept of Neural Networks in detail

Advantages

  • Gaining Expertise in Depth Knowledge on TensorFlow API and primitives
  • Understanding concept of training and tuning ML system with TensorFlow
  • TensorFlow concept with Convolutional Networks, recurrent Networks and LSTMs

 

Additional Information

Day 1

1. Basic Overview of Deep Learning

– Understanding Deep Learning Primitives
– Overview of Architecture in Deep Learning

2. Introduction to TensorFlow Primitives

– Overview of Tensors
– Understanding basic computations in TensorFlow
– Dealing with Imperative and Declarative Programming

3. Understanding Linear and Logistic Regression

– Using Mathematical Review
– TensorFlow Learning concepts
– Training Linear and Logistics Models

4. Overview of Fully Connected Deep Networks

– Introduction to Fully Connected Deep networks
– Understanding Neurons in FCN
– Concept of Training Fully Connected Neural Networks
– TensorFlow Implementation in detail

5. Understanding concept of Hyperparameter Optimization in detail

– Overview of Model evaluation and Hyperparameter Optimization
– Understanding the Metrics, Metrics and Metrics
    – Using Binary Classification Metrics
    – Using Multiclass Classification Metrics
    – Using Regression Metrics
– Dealing with Hyperparameter Optimization Algorithms

Day 2

1. Overview of Convolutional Neural Networks

– Basic Introduction to Convolutional Architectures
– Basic Applications for Convolutional Architectures
– Training a Convolutional Network in TensorFlow

2. Dealing with Recurrent Neural Networks

– Understanding Recurrent Architecture
– Using recurrent Cells
– Understanding Application of Recurrent Models
– Using Neural Turing Machines
– Working with Recurrent Neural Networks in practice
– Processing of Penn Treebank Corpus

3. Training Large Deep Networks

– Using Custom hardware for Deep Networks
– Understanding CPU Training
– Dealing with Distributed Deep Network Training
– Using Data Parallel Training with Multiple GPUs

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