Modern Deep Learning Techniques using TensorFlow Training

Level: Foundation
Rating: 3.6/5 3.60/5 Based on 10 Reviews

In this course, we first start with an introduction to Deep Learning. Then we will look at the Tensorflow framework and preview its main components as well as the overall API hierarchy. Tensorflow 2.x was launched with tight integration of Keras, eager execution by default, and Pythonic function execution, among other new features and improvements.

Next, we will discuss how to train on large datasets using the Dataset API; how to use feature columns to prepare the data for training; and how activation functions are needed in order for the model to be able to learn nonlinearities in the data.

We introduce the tf.keras API which is TensorFlow's high-level API for building and training deep learning models. We will explore the Sequential and Functional APIs and learn how to use them to create deep learning models.

Finally, we discuss how to deploy models and use them to solve a real world problem.

Key Features of this Modern Deep Learning Techniques using TensorFlow Training:

  • After-course instructor coaching benefit

You Will Learn How To:

  • Understanding artificial neural networks
  • Tensorflow 2.0, fundamental and building blocks
  • Training, Testing and Evaluating a TensorFlow Model
  • Feature Engineering
  • Strategies to deploy a trained TensorFlow Model

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  • 2-day instructor led training course
  • One-on-one after-course instructor coaching
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Note: This course runs for 2 Days

  • Feb 14 - 15 9:00 AM - 4:30 PM EST Ottawa / Online (AnyWare) Ottawa / Online (AnyWare) Reserve Your Seat

  • Apr 4 - 5 9:00 AM - 4:30 PM EDT Herndon, VA / Online (AnyWare) Herndon, VA / Online (AnyWare) Reserve Your Seat

  • Aug 15 - 16 9:00 AM - 4:30 PM EDT Ottawa / Online (AnyWare) Ottawa / Online (AnyWare) Reserve Your Seat

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Modern Deep Learning Techniques using TensorFlow Training Information

  • About This Course

    This course provides the skills and knowledge required to understand TensorFlow, and to use it to solve actual real-world complex problems.

    Hands-On Experience Includes:

    • Tensors and Variables
    • Design and Build Data Pipeline
    • Lab: Introduction to Tensors and Variable

    Every attendee has remote access to dedicated VM with TensorFlow and exercises installed for 3 months after the course to try out and practice

    Who Should Attend 

    Anyone seeking to understand and exploit the benefits of TensorFlow to implement Machine Learning/AI: Data Scientists, developers and analysts, and anyone with some machine learning background.

Modern Deep Learning Techniques using TensorFlow Training Outline

  • Understanding Artificial Neural Networks and TensorFlow

    • Basics of Artificial Neural Networks
    • LifeCycle of Building a TensorFlow Model
    • Understanding Deep Learning
    • DO NOW: Visiting TensorFlow Playgrounds
    • Tensorflow, basic concept
    • Layered Architecture of TensorFlow
    • Constants, Variables and Tensors
    • Lab1: Introduction to Tensors and Variables
  • Feature Engineering with TensorFlow

    • Using
    • Lab2: Exploring
    • Defining a Keras Model
    • Lab3: Using TensorFlow Sequential API
    • Wide and Deep Models
    • Lab4: Using TensorFlow Functional API
  • TensorFlow; Under the Hood

    • Defining gradient Descent
    • Activation functions
    • Hyperparameters
    • Lab5: Keras and Tensorflow
    • Regularization
  • Feature Engineering

    • Introduction to Feature Engineering
    • Lab6: Feature Engineering
    • Raw Data and Features
    • Lab7: Basic Feature Engineering in Keras
    • Feature Crosses
    • Lab8: Advanced Feagure Engineering in Keras.
    • Transform
    • Lab9: Exploring Tf.transform
  • Monitoring and Deployment

    • Monitoring with TensorBoard
    • Lab10: Using Tensorboard to Monitor the performance
    • Saving and Versioning Model
    • Deploying Models

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TensorFlow and Keras FAQs

  • What is TensorFlow?

    TensorFlow is an open source library for numerical computation and it is used for large-scale machine learning. It uses Python as a front-end API for building applications with the framework, while executing those applications in high-performance C++.

  • What is Keras?

    Keras is a leading High-level API. It is written in python and was created to be user friendly and modular.

  • What is the difference between Keras and TensorFlow?

    Keras is a high level library that cannot live on it's own, while TensorFlow is a framework that can live on it's own.

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