Applied Deep Learning: Build a Chatbot – Theory, Application

Applied Deep Learning: Build a Chatbot – Theory, Application, available at Free, has an average rating of 4.34, with 42 lectures, 1 quizzes, based on 946 reviews, and has 49682 subscribers.

You will learn about 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 This course is ideal for individuals who are Anybody enthusiastic about Deep Learning Applications It is particularly useful for Anybody enthusiastic about Deep Learning Applications.

Enroll now: Applied Deep Learning: Build a Chatbot – Theory, Application

Summary

Title: Applied Deep Learning: Build a Chatbot – Theory, Application

Price: Free

Average Rating: 4.34

Number of Lectures: 42

Number of Quizzes: 1

Number of Published Lectures: 42

Number of Published Quizzes: 1

Number of Curriculum Items: 43

Number of Published Curriculum Objects: 43

Original Price: Free

Quality Status: approved

Status: Live

What You Will Learn

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

Who Should Attend

Anybody enthusiastic about Deep Learning Applications

Target Audiences

Anybody enthusiastic about Deep Learning Applications

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!

Course Curriculum

Chapter 1: Theory Part 1 – RNNs and LSTMs

Lecture 1: BEFORE WE START………..PLEASE READ THIS

Lecture 2: Introduction to RNNs Part 1

Lecture 3: Introduction to RNNs Part 2

Lecture 4: Playing with the Activations

Lecture 5: LSTMs

Lecture 6: LSTM Variants

Lecture 7: LSTM Step-by-Step Example Walktrough

Chapter 2: Theory Part 2 – Sequence Modeling

Lecture 1: Sequence Modeling

Lecture 2: Attention Mechanism in LSTMs

Lecture 3: How Attention Mechanisms Work

Chapter 3: Practical Part 1 – Introduction to PyTorch

Lecture 1: Installing PyTorch and an Introduction

Lecture 2: Torch Tensors Part 1

Lecture 3: Torch Tensors Part 2

Lecture 4: Numpy Bridge, Tensor Concatenation ad Adding Dimensions

Chapter 4: Practical Part 2 – Processing the Dataset

Lecture 1: The Dataset

Lecture 2: Processing the Dataset Part 1

Lecture 3: Processing the Data Part 2

Lecture 4: Processing the Dataset Part 3

Lecture 5: Processing the Dataset Part 4

Lecture 6: Processing the Words

Lecture 7: Processing the Text

Lecture 8: Processing the Text Part 2

Lecture 9: Filtering the Text

Lecture 10: Getting Rid of Rare Words

Chapter 5: Practical Part 3 – Data Preperation

Lecture 1: Preparing the Data for Model Part 1

Lecture 2: Understanding the zip function

Lecture 3: Preparing the Data for Model Part 2

Lecture 4: Preparing the Data for Model Part 3

Lecture 5: Preparing the Data for Model Part 4

Chapter 6: Practical Part 4 – Building the Model

Lecture 1: Understanding the Encoder

Lecture 2: Defining the Encoder

Lecture 3: Understanding Pack Padded Sequence

Lecture 4: Designing the Attention Model

Lecture 5: Designing the Decoder Part 1

Lecture 6: Designing the Decoder Part 2

Chapter 7: Practical Part 5 – Training the Model

Lecture 1: Creating the Loss Function

Lecture 2: Teacher Forcing

Lecture 3: Visualize Training Part 1

Lecture 4: Visualize Training Part 2

Lecture 5: Training

Lecture 6: Proceeding

Chapter 8: Deep Learning with Transformers

Lecture 1: Transformers

Instructors

Fawaz Sammani
Computer Vision Researcher

Rating Distribution

1 stars: 24 votes
2 stars: 37 votes
3 stars: 162 votes
4 stars: 338 votes
5 stars: 385 votes

Frequently Asked Questions

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