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What it contains:

No Lesson Intro PyTorch Lab Use Cases
I Introduction to AI Understanding AI Concepts
1 Introduction and History of AI Text Foundations of AI
II Symbolic AI Problem Solving, Planning
2 Knowledge Representation and Expert Systems Text Expert System, Ontology, Concept Graph Decision Making Systems
III Introduction to Neural Networks Neural Network Modeling
3 Perceptron Text Notebook Lab Binary Classification
4 Multi-Layered Perceptron and Creating our own Framework Text Notebook Lab Multi-Class Classification
5 Intro to Frameworks (PyTorch/TensorFlow) Overfitting Text AI Software Development
IV Computer Vision AI Fundamentals: Explore Computer Vision Image Processing
6 Intro to Computer Vision. OpenCV Text Notebook Lab Image Analysis
7 Convolutional Neural Networks CNN Architectures Text Lab Image Recognition
8 Pre-trained Networks and Transfer Learning Training Tricks Text Dropout sample, Adversarial Cat Advanced Image Classification
9 Autoencoders and VAEs Text Dimensionality Reduction
10 Generative Adversarial Networks Artistic Style Transfer Text GAN, Style Transfer Image Generation
11 Object Detection Text Lab Real-time Object Tracking
12 Semantic Segmentation. U-Net Text Pixel-level Image Classification
V Natural Language Processing AI Fundamentals: Explore Natural Language Processing Text Processing
13 Text Representation. Bow/TF-IDF Text Text Classification
14 Semantic word embeddings. Word2Vec and GloVe Text Word Similarities
15 Language Modeling. Training your own embeddings Text Lab Text Prediction
16 Recurrent Neural Networks Text Sequence Prediction
17 Generative Recurrent Networks Text Lab Text Generation
18 Transformers. BERT. Text Text Summarization, Translation
19 Named Entity Recognition Text Lab Information Extraction
20 Large Language Models, Prompt Programming and Few-Shot Tasks Text Text Summarization, Question Answerin

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