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Build Advanced Chatbot with Transformer Neural Network
Mammoth Interactive Courses Introduction
Preface
00 About Mammoth Interactive (1:12)
01 How To Learn Online Effectively (13:46)
Source Files
Transformer Project Overview
Introduction To Transformer Neural Networks (4:25)
Transformer Project Overview (7:54)
Source files
Preprocess text data for Transformer chatbot ML
Connect To Google Drive Dataset In Colab (3:42)
Read Text Files In Python (9:11)
Read Movie Conversation Text File In Python (10:52)
Clean Text Data For Nlp (6:04)
Remove Contractions From Text Data With Python (9:29)
Preprocess Text Data For Transformer Chatbot Ml (6:10)
Source files
Tokenize and filter sentences with Python
Build Tokenizer With Tfds (7:26)
Add Padding To Tokenized Sentences With Python (3:01)
Build Tensorflow Dataset For Ml (3:08)
Source Files
Build multi head attention layer for chatbot ML Python
Calculate Scaled Dot Product Attention (4:51)
Set Up Multi Head Attention Layer In Python Nn (5:16)
Split Attention Layer Into Multiple Heads (4:03)
Add Scaled Dot Product Attention And Final Layer (5:17)
Source files
Build token masks for neural network
Mask Padding Tokens With Python (4:32)
Build Lookahead Mask For Future Tokens (3:47)
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Build positional encoding machine learning
Set Up Positional Encoding Layer In Neural Network (2:57)
Build Positional Encoding Layer With Tensorflow Keras (5:26)
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Build input encoder for neural network
Build Input Encoder For Neural Network (5:23)
Combine Input And Positional Encoding (5:30)
Source files
Build decoder for NLP ML model
Set Up Decoder Layer With Python (6:25)
Combine Output And Positional Encoding For Decoder (5:22)
Source files
Combine encoding and decoding in NN
Combine Encoding And Decoding In Nn (7:03)
Build Custom Ml Model Learning Rate (3:31)
Build Custom Model Loss Function (3:12)
Compile Neural Network With Python (4:24)
Zero Out Padding Tokens In Attention (1:38)
Limit And Pad Tokenized Sentences (5:25)
Source files
Evaluate chatbot neural network
Handle New Chatbot Question Input (5:07)
Decode Tokens Into Words (2:24)
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Source files
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