Applied Deep Learning: Build a Chatbot - Theory, Application

Understand the Theory of how Chatbots work and implement them in Python and PyTorch!
Rating: 4.2 out of 5 (579 ratings)
27,299 students
Applied Deep Learning: Build a Chatbot - Theory, Application
Rating: 4.2 out of 5 (579 ratings)
27,299 students
Understand the theory behind Sequence Modeling
Understand the theory of how Chatbots work
Undertand the theory of how RNNs and LSTMs work
Get Introduced to PyTorch
Implement a Chatbot in PyTorch
Undertand the theory of different Sequence Modeling Applications

Requirements

  • Some Basic High School Mathematics
  • Some Basic Programming Knowledge
  • Some basic Knowledge about Neural Networks
Description

In this course, you'll learn the following:

  • RNNs and LSTMs

  • Sequence Modeling

  • PyTorch

  • Building a Chatbot in PyTorch

We will first cover the theoretical concepts you need to know for building a Chatbot, which include RNNs, LSTMS and Sequence Models with Attention.

Then we will introduce you to PyTorch, a very powerful and advanced deep learning Library. We will show you how to install it and how to work with it and with PyTorch Tensors.

Then we will build our Chatbot in PyTorch!

Please Note an important thing: If you don't have prior knowledge on Neural Networks and how they work, you won't be able to cope well with this course. Please note that this is not a Deep Learning course, it's an Application of Deep Learning, as the course names implies (Applied Deep Learning: Build a Chatbot). The course level is Intermediate, and not Beginner. So please familiarize yourself with Neural Networks and it's concepts before taking this course.  If you are already familiar, then your ready to start this journey!

Who this course is for:
  • Anybody enthusiastic about Deep Learning Applications
Curriculum
8 sections • 42 lectures • 6h 9m total length
  • Before we Start
  • Introduction to RNNs Part 1
  • Introduction to RNNs Part 2
  • Test Your Understanding
  • Playing with the Activations
  • LSTMs
  • LSTM Variants
  • LSTM Step-by-Step Example Walktrough
  • Sequence Modeling
  • Attention Mechanism in LSTMs
  • How Attention Mechanisms Work
  • Installing PyTorch and an Introduction
  • Torch Tensors Part 1
  • Torch Tensors Part 2
  • Numpy Bridge, Tensor Concatenation ad Adding Dimensions
  • The Dataset
  • Processing the Dataset Part 1
  • Processing the Data Part 2
  • Processing the Dataset Part 3
  • Processing the Dataset Part 4
  • Processing the Words
  • Processing the Text
  • Processing the Text Part 2
  • Filtering the Text
  • Getting Rid of Rare Words
  • Preparing the Data for Model Part 1
  • Understanding the zip function
  • Preparing the Data for Model Part 2
  • Preparing the Data for Model Part 3
  • Preparing the Data for Model Part 4
  • Understanding the Encoder
  • Defining the Encoder
  • Understanding Pack Padded Sequence
  • Designing the Attention Model
  • Designing the Decoder Part 1
  • Designing the Decoder Part 2
  • Creating the Loss Function
  • Teacher Forcing
  • Visualize Training Part 1
  • Visualize Training Part 2
  • Training
  • Proceeding
  • Transformers

Instructor
Computer Vision Researcher
Fawaz Sammani
  • 4.4 Instructor Rating
  • 1,111 Reviews
  • 30,544 Students
  • 3 Courses

I am a researcher doing my research in Computer Vision. Through out my research period, i have achieved many of my research goals and published multiple research papers. I have three courses, one which provides a complete guide to Image Processing with MATLAB, where you will master the basics of Image Processing and build interfaces for them,  another course which is a complete guide to Neural Networks, where you'll master neural networks and deep learning topics in depth both theoretically and practically in one of the most powerful deep learning frameworks! I am extremely happy to share my knowledge and experience throughout my courses!