Applied Deep Learning with PyTorch

Course 1277

  • Duration: 2 days
  • Language: English
  • Level: Intermediate
Get This Course $2,225
  • 2-day instructor led training course
  • One-on-one after-course instructor coaching
  • Pay later by invoice -OR- at the time of checkout by credit card
  • Guaranteed to Run - you can rest assured that the class will not be cancelled.
    Aug 22 - 23 9:00 AM - 4:30 PM EDT
  • Oct 24 - 25 9:00 AM - 4:30 PM EDT
    Herndon, VA or AnyWare
  • Feb 27 - 28 9:00 AM - 4:30 PM EST
    Ottawa or AnyWare
  • Apr 24 - 25 9:00 AM - 4:30 PM EDT
    Herndon, VA or AnyWare

Starting with the basics of deep learning and their various applications, Applied Deep Learning with PyTorch shows you how to solve trending tasks, such as image classification and natural language processing by understanding the different architectures of the neural networks.

Some working knowledge of Python and familiarity with the basics of machine learning are a must. However, knowledge of NumPy and pandas will be beneficial, but not essential.

Applied Deep Learning with PyTorch is designed for data scientists, data analysts, and developers who want to work with data using deep learning techniques. Anyone looking to explore and implement advanced algorithms with PyTorch will also find this course useful.

Applied Deep Learning with PyTorch Delivery Methods

  • After-course instructor coaching benefit
  • After-course computing sandbox included
  • Learning Tree end-of-course exam included

Applied Deep Learning with PyTorch Course Benefits

  • Detect a variety of data problems to which you can apply deep learning solutions
  • Learn the PyTorch syntax and build a single-layer neural network with it
  • Build a deep neural network to solve a classification problem
  • Develop a style transfer model
  • Implement data augmentation and retrain your model
  • Build a system for text processing using a recurrent neural network

Applied Deep Learning with PyTorch Course Outline

  • Understanding Deep Learning
  • PyTorch Introduction
  • Introduction to Neural Networks
  • Data Preparation
  • Building a Neural Network
  • Problem Definition
  • Dealing with an Underfitted or Overfitted Model
  • Deploying Your Model
  • Building a CNN
  • Data Augmentation
  • Batch Normalization
  • Style transfer
  • Implementation of Style Transfer Using the VGG-19 Network Architecture
  • Recurrent Neural Networks
  • Long Short-Term Memory Networks (LSTMs)
  • LSTM Networks in PyTorch
  • Natural Language Processing (NLP)
  • Sentiment Analysis in PyTorch

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