Understand the Theory of how Chatbots work and implement them in Python and PyTorch!
Free tutorial
Created by Fawaz Sammani
English
What you’ll 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
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
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Course content
8 sections • 42 lectures • 6h 10m total lengthCollapse all sections
Theory Part 1 – RNNs and LSTMs7 lectures • 45min
- BEFORE WE START………..PLEASE READ THIS01:16
- Introduction to RNNs Part 110:31
- Introduction to RNNs Part 208:21
- Test Your Understanding4 questions
- Playing with the Activations09:25
- LSTMs08:47
- LSTM Variants03:22
- LSTM Step-by-Step Example Walktrough03:38
Theory Part 2 – Sequence Modeling3 lectures • 30min
- Sequence Modeling13:13
- Attention Mechanism in LSTMs05:45
- How Attention Mechanisms Work11:14
Practical Part 1 – Introduction to PyTorch4 lectures • 38min
- Installing PyTorch and an Introduction10:16
- Torch Tensors Part 109:55
- Torch Tensors Part 208:48
- Numpy Bridge, Tensor Concatenation ad Adding Dimensions09:23
Practical Part 2 – Processing the Dataset10 lectures • 1hr 21min
- The Dataset10:13
- Processing the Dataset Part 106:34
- Processing the Data Part 207:42
- Processing the Dataset Part 307:51
- Processing the Dataset Part 405:43
- Processing the Words10:54
- Processing the Text09:00
- Processing the Text Part 208:42
- Filtering the Text06:10
- Getting Rid of Rare Words08:33
Practical Part 3 – Data Preperation5 lectures • 46min
- Preparing the Data for Model Part 111:34
- Understanding the zip function05:25
- Preparing the Data for Model Part 207:10
- Preparing the Data for Model Part 309:34
- Preparing the Data for Model Part 412:18
Practical Part 4 – Building the Model6 lectures • 1hr 22min
- Understanding the Encoder05:40
- Defining the Encoder23:12
- Understanding Pack Padded Sequence07:26
- Designing the Attention Model15:21
- Designing the Decoder Part 113:52
- Designing the Decoder Part 216:12
Practical Part 5 – Training the Model6 lectures • 47min
- Creating the Loss Function06:06
- Teacher Forcing06:04
- Visualize Training Part 113:13
- Visualize Training Part 209:53
- Training11:18
- Proceeding00:10
Deep Learning with Transformers1 lecture • 1min
- Transformers00:32
