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Applied Deep Learning with PyTorch

Key Features of this Applied Deep Learning with PyTorch Course:

  • After-course instructor coaching benefit
  • After-course computing sandbox included
  • End-of-course exam included

Who Should Attend This Course

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.

Select specific date to see price, venue and full details.

Learning Objectives

  • 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

Pre-Requisites

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.

Course Content

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: Analysing 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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