BOOK's WEBINAR: ChatGPT, LLMs and Deep Learning with Python (Saturday 15 & Sunday 16 February 2025)

Indicative Educational Material (Access to my book and its supplementary material (slides, python code, etc.) will only be given to registered participants)

You can register for the 100 hours Educational Program using the link below:
Book's Webinar Official Page from the University of the Aegean      




 

Chapter 1. Transformers, Large Language Models, and ChatGPT.pdf


1.1 Transformers

1.2 Attention and Seif-Attention

1.3 Multi-head Attention

1.4 Positional Encoding

1.5 Large Language Models and ChatGPT

1.6 Encoder Transformers

1.7 Decoder Transformers

1.8 Encoder and Decoder Transformers

1.9 Multi-model Transformers and Google's Med Gemini

1.10 Chapter Questions

Chapter 1 (sample)

 

Chapter2. Large Language Models and Recommendation Algorithms.pdf


2.1 The Vector Space Model, bag of words and Tokenization

2.2 Word Embedding, Word2Vec and skip-grams approaches

2.3 Recommendations based on Content Data

2.4 Decision Tree Classifier

2.5 Feature Selection

2.6 Naive Bayes Classifier

2.7 Python Exercise: Recommend Friends based on the CBF Algorithm

Chapter2.ppt

 

Chapter3. Collaborative Filtering Algorithms.pdf


2.1 Introduction in CF algorithms

2.2 Factors Affecting the CF Process

2.3 Comparative Performance of CF algorithms

2.4 Chapter Questions

2.5 Python Exercise: Constructing a User-User Similarity Matrix

Chapter3.pptx

Python code in Google Colab

 

Chapter4. Context-aware AI Systems.pdf


4.1 Sequence-aware AI Systems (Sequencial Transformers)

4.2 Location-aware AI Systems

4.3 AI systems for LBSNs

4.4 Types of Recommendations in LBSNs

4.5 Hybrid and Ensemble Methods

4.6 Types of Hybrid Systems

4.7 Python Exercise: Recommend Friends based on a Hybrid Algorithm

Chapter4.pptx

Python code in Google Colab

Chapter 5. Matrix Decomposition Algorithms.pdf


5.1 Eigenvalue Decomposition

5.2 Singular Value Decomposition

5.3 From SVD to UV-decomposition

5.4 Tensor Decomposition

5.5 From SVD to UV-decomposition

5.6 Tensor Decomposition

5.7 Tucker Decomposition

5.8 Python Exercise: Apply UV-decomposition to a User-Item Rating Matrix

Chapter 5.pptx

Python code in Google Colab

Chapter 6. Deep Neural Nets and Genetic Algorihtms.pdf


6.1 Perceptron's structure

6.2 Single-layer Perceptron

6.3 Multi-layer Perceptron

6.4 Convolutional Neural Networks

6.5 Recurrent Neural Networks

6.6 Genetic Algorithms

6.7 Genetic Operations

6.8 Python Exercise: Use a Neural Network to Factorise a User-Item Rating Matrix

Slides (draft)

Python code in Google Colab


Chapter 7. Deep Reinforcement Learning.pdf


7.1 Q-learning Algorithm

7.2 Step-by-Step Execusion of the Q-learning Algorithm

7.3 Deep Reinforcement Learning

7.4 Deep Q-Network with Experience Replay Algorithm

7.5 Advantage Actor Critic Algorithm

7.6 Python Exercise 1: Implement the Tabular Q-learning Algorithm

7.7 Python Exercise 2: Implement the DQN Algorithm

Chapter7.pptx

Python code in Google Colab

Chapter 8. Deep Graph Neural Networks.pdf


8.1 Graphs Fundamentals

8.2 Local-based Similarity Measures

8.3 Global-based Similarity Measures

8.4 Knowledge Graphs

8.5 Graph Convolutional Networks

8.6 Python Exercise: Graph-based Recommendations for an Online Newspaper

chapter 8.pptx

(Book Example 8.5) Python code in Google Colab

(Book Example 8.10) Code for a Graph Convolution Neural Network

Chapter 9. Εvaluation Metrics of AI systems.pdf


9.1 Introduction to AI Models' Evaluation

9.2 MAE and RMSE

9.3 Precision, Recall and F1 metric

9.4 ROC curve and AUC metric

9.5 Normalized Discounted Cumulative Gain

9.6 Beyond Accuracy Metrics

9.7 Python Exercise: Build an Evaluation Framework

chapter9.pptx

Python code in Google Colab

Chapter 10. New Trends in LLMs and Recommender Systems.pdf


10.1 From LLMs to LMMs (multi-modal), ChatGPT and Med Gemini

10.1.1. Audio transformer, Word transformer, Video transformer, Image transformer.

10.2 Ethics in AI Systems

10.3 Fairness, Accountability, Censorship

10.4 Privacy and AI Systems

10.5 Systems' Architecture for Privacy

10.6 Algorithmic Techniques for Privacy Protection

10.7 Legal Framework for Privacy Protection

Chapter10.pptx


Intro in Python

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