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Chatbot Engineering with Python - From Basics to Transformer-Powered AI
Build and tokenize chat data
01 Build Patterns And Responses Training Data (6:34)
02 Tokenize Chat Data For Training (4:30)
Clean and process data for machine learning
01 Clean Chat Data For Machine Learning (3:04)
02 Build Bag Of Words For Ml Model (4:24)
03 Split Data For Machine Learning (3:34)
Build and train TensorFlow model on chat data
01 Build A Tensorflow Machine Learning Model For Chat (4:54)
02 Test Chatbot Machine Learning Model (9:09)
03 Categorize Chat Question With Ml (7:25)
04 Pick A Chatbot Response In Top Category (8:18)
Source Files
Transformer Project Overview
01 Introduction To Transformer Neural Networks (4:25)
02 Transformer Project Overview (7:54)
Source files
Preprocess text data for Transformer chatbot ML
01 Connect To Google Drive Dataset In Colab (3:42)
02 Read Text Files In Python (9:11)
03 Read Movie Conversation Text File In Python (10:52)
04 Clean Text Data For NLP (6:04)
05 Remove Contractions From Text Data With Python (9:29)
06 Preprocess Text Data For Transformer Chatbot Ml (6:10)
Source files
Tokenize and filter sentences with Python
01 Build Tokenizer With TFDs (7:26)
02 Add Padding To Tokenized Sentences With Python (3:01)
03 Build Tensorflow Dataset For Ml (3:08)
Source Files
Build multi head attention layer for chatbot ML Python
01 Calculate Scaled Dot Product Attention (4:51)
02 Set Up Multi Head Attention Layer In Python Nn (5:16)
03 Split Attention Layer Into Multiple Heads (4:03)
04 Add Scaled Dot Product Attention And Final Layer (5:17)
Source files
Build token masks for neural network
01 Mask Padding Tokens With Python (4:32)
02 Build Lookahead Mask For Future Tokens (3:47)
Source files
Build positional encoding machine learning
01 Set Up Positional Encoding Layer In Neural Network (2:57)
02 Build Positional Encoding Layer With Tensorflow Keras (10:52)
Source files
Build input encoder for neural network
01 Build Input Encoder For Neural Network (5:23)
02 Combine Input And Positional Encoding (5:30)
Source files
Build decoder for NLP ML model
01 Set Up Decoder Layer With Python (6:25)
02 Combine Output And Positional Encoding For Decoder (10:44)
Source files
Combine encoding and decoding in NN
01 Combine Encoding And Decoding In Nn (7:03)
02 Build Custom Ml Model Learning Rate (3:31)
03 Build Custom Model Loss Function (3:12)
04 Compile Neural Network With Python (4:24)
05 Zero Out Padding Tokens In Attention (1:38)
06 Limit And Pad Tokenized Sentences (5:25)
Source files
Evaluate chatbot neural network
01 Handle New Chatbot Question Input (5:07)
02 Decode Tokens Into Words (2:24)
Source files
02 Build Custom Ml Model Learning Rate
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