Applied Deep Learning with PyTorch

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: 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