Course Number: PYTH-158

Duration: 3 days (19.5 hours)

Format: Live, hands-on

Deep Learning Training Overview

This hands-on, live Deep Learning training course teaches attendees how to use TensorFlow to create Convolutional Neural Networks (CNN) for image recognition and Recurrent Neural Networks (RNN) for natural language processing.

Location and Pricing

This course is taught as a private, live online class for teams of 3 or more. All our courses are hands-on, instructor-led, and tailored to fit your group’s goals and needs. Most Accelebrate classes can be flexibly scheduled for your group, including delivery in half-day segments across a week or set of weeks. To receive a customized proposal and price quote for online corporate training, please contact us.

Objectives

  • Install TensorFlow
  • Understand deep learning and its role in AI
  • Understand neural networks
  • Code a neural network from scratch
  • Understand the architectural differences between deep neural networks and deep learning
  • Use TensorFlow and Keras to apply deep learning
  • Understand Convolutional Neural Networks
  • Apply image recognition tasks
  • Understand Recurrent Neural Networks
  • Apply natural language processing
  • Apply sentiment analysis
  • Understand use cases for Convolutional Neural Networks
  • Understand use cases for Recurrent Neural Networks

Prerequisites

All students must have Basic Python programming skills.

Outline

Expand All | Collapse All

Course Introduction
Overview of Deep Learning
Install Anaconda
Learn how to use Jupyter Notebooks
Essential libraries
  • TensorFlow
  • Keras
  • NumPy
  • Scikit Learn
Introduction to Neural Networks
  • The math behind neural networks
  • Back propagation
  • Understanding the intuition behind neural networks
  • Coding a neural network from scratch
  • Deep neural networks
Understanding TensorFlow
  • Applying neural networks using TensorFlow/Keras
From Deep Neural Networks to Deep Learning
Understanding unstructured data
Image recognition
Introduction to Convolutional Neural Networks (CNN)
Convolutional layers
Pooling layers
Fully-connected layers
Using TensorFlow to create a CNN
Image Recognition Project
Understanding Sequence Models
Introduction to Recurrent Neural Networks (RNN)
Natural Language Processing (NLP)
Text Preprocessing
Word Embeddings
Sentiment Analysis
Using TensorFlow to Create an RNN
Natural Language Processing Project
Introduction to Auto Encoders
Introduction to Generative Adversarial Networks (GAN)
Structured Activity/Exercises/Case Studies:
  • Learn how to use Jupyter Notebooks
  • Coding a neural network from scratch
  • Applying neural networks using TensorFlow/Keras
  • Using TensorFlow to create a CNN
  • Image recognition project
  • Using TensorFlow to create an RNN
  • Natural Language Processing project
Conclusion

Training Materials:

All Deep Learning training students receive comprehensive courseware.

Software Requirements:

  • Any Windows, Linux, or macOS operating system
  • Python 3.x installed (Anaconda bundle recommended)
  • An IDE with Python support (Jupyter Notebook, Spyder, or PyCharm Community Edition)


Learn faster

Our live, instructor-led lectures are far more effective than pre-recorded classes

Satisfaction guarantee

If your team is not 100% satisfied with your training, we do what's necessary to make it right

Learn online from anywhere

Whether you are at home or in the office, we make learning interactive and engaging

Multiple Payment Options

We accept check, ACH/EFT, major credit cards, and most purchase orders



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