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Applied Deep Learning with PyTorch

Applied Deep Learning with PyTorch

Course 1277

Duration: 2 days
Language: English
Level: Intermediate

Get This Course $1,950

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

#1277

Guaranteed to Run - you can rest assured that the class will not be cancelled.
Aug 22 - 23 9:00 AM - 4:30 PM EDT

Ottawa or AnyWare
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

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

#1277

Guaranteed to Run - you can rest assured that the class will not be cancelled.
Aug 22 - 23 9:00 AM - 4:30 PM EDT

Ottawa or AnyWare
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

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Delivered when, where, and how you want it
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Expert team coaching

#1277

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

Lesson 1: Introduction to Deep Learning and PyTorch

Understanding Deep Learning
PyTorch Introduction

Lesson 2: Building Blocks of Neural Networks

Introduction to Neural Networks
Data Preparation
Building a Neural Network

Lesson 3: A Classification Problem Using DNN

Problem Definition
Dealing with an Underfitted or Overfitted Model
Deploying Your Model

Lesson 4: Convolutional Neural Networks

Building a CNN
Data Augmentation
Batch Normalization

Lesson 5: Style Transfer

Style transfer
Implementation of Style Transfer Using the VGG-19 Network Architecture

Lesson 6: Analyzing the Sequence of Data with RNNs

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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