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nexageapps / repository
Comprehensive AI/ML learning path from basics to building language models. 15+ hands-on Jupyter notebooks covering TensorFlow, PyTorch, CNNs, RNNs, Transformers, and GPT. Perfect for MAI students and self-learners.
Open-source AI education built by a student, for students and learners worldwide.
Basic to Expert. Zero to Language Models. 64 lessons. 100% hands-on.
Quick Start • Student Guide • Exam Prep • LinkedIn • Support Project
This is an independent learning project, NOT official University of Auckland, material. Use responsibly and follow your institution's academic integrity policies. See Academic Integrity Policy for details.
A structured, hands-on learning path from basic arithmetic to complete language models. 64 lessons with runnable code, visualizations, and practical projects.
Perfect for:
| Level | Lessons | Duration | Best For |
|---|---|---|---|
| Basic (B01-B15) | 19 | 2-3 weeks | Foundations & core concepts |
| Intermediate (I01-I15) | 15 | 4-6 weeks | Advanced techniques |
| Advanced (A01-A15) | 15 | 6-8 weeks | Production systems |
| Expert (E01-E15) | 15 | 8-10 weeks | Research & innovation |
# Create virtual environment
python -m venv .venv
source .venv/bin/activate # macOS/Linux
.venv\Scripts\activate # Windows
# Install dependencies
pip install tensorflow torch numpy matplotlib jupyter
jupyter lab
# Open any notebook from Basic/ folder
Or use Google Colab (no setup needed) - Click "Open in Colab" badge in any notebook.
AI/
├── Basic/ # 20 Lessons (B01-B15 + B01a, B05a, B05b, B05c, B09a, B09b, B10a) ✅
├── Intermediate/ # 15 Lessons (I01-I15) ✅
├── Advanced/ # 15 Lessons (A01-A15) ✅
├── Expert/ # 15 Lessons (E01-E15) ✅
├── application/ # Live demos & practical implementations
│ ├── compsci713/ # COMPSCI 713 weekly apps (Wumpus, KG, RNN, NEAT, Q-Learning)
│ └── compsci714/ # COMPSCI 714 weekly apps (Gradient Descent, CNN, Transformer, BPE)
├── documentation/ # Guides & resources
└── landingpage/ # Landing page assets
Interactive demonstrations of AI concepts in action:
| Demo | Concept | Course | Course Page | Notebook | Link |
|---|---|---|---|---|---|
| Wumpus World | Symbolic Logic & Knowledge Representation | UoA-COMPSCI 713 | COMPSCI 713 – AI Fundamentals | — | Play Online |
| Mountain Explorer | Gradient Descent & Optimization | UoA-COMPSCI 714 | COMPSCI 714 – Neural Networks | B02 - Linear Regression · B05b - Training & Optimization | Play Online |
| Blindfold Hill | Gradient Descent, Learning Rate & Convergence | UoA-COMPSCI 714 | COMPSCI 714 – Neural Networks | B02 - Linear Regression · B05b - Training & Optimization | Play Online |
| Neural Network Trainer | Forward Propagation, Backpropagation & Gradient Descent | UoA-COMPSCI 714 | COMPSCI 714 – Neural Networks | B05 - Neural Network Fundamentals · B05a - Neural Networks Theory |
View All Games • Explore the application/ folder for source code and deployment guides.
| Course | Focus | Examples |
|---|---|---|
| COMPSCI 713 | AI Fundamentals | Symbolic Logic, Knowledge Representation, Search, RL, Neuroevolution, Sustainability |
| COMPSCI 714 | Neural Networks | Networks, Gradient Descent, CNNs, Attention |
| COMPSCI 762 | ML Foundations | Regression, Classification, Tuning |
| COMPSCI 703 | Generalising AI | Transfer Learning, Domain Adaptation |
| COMPSYS 721 | Deep Learning | Detection, Time Series, NLP, GANs |
COMPSCI 713 Complete Guide · COMPSCI 714 Complete Guide
| Document | Purpose |
|---|---|
| Student Guide | Course mapping, semester planning, study strategies |
| Exam Prep Guide | Exam strategies, practice problems, concept review |
| COMPSCI 713 Complete Guide | AI Fundamentals course guide with week-by-week lesson alignment |
| COMPSCI 714 Complete Guide | Neural network course guide with lecture alignment |
| Documentation Index | Complete guide to all documentation |
| Academic Integrity | Responsible use guidelines |
Selected from shared topics, language and repository description—not editorial ratings.
WisdomRoots24 /
📊 Comprehensive repository for AI & ML development with Jupyter notebooks, datasets, and deep learning models. Covers supervised & unsupervised learning, NLP, computer vision, and model deployment using TensorFlow & PyTorch. 📈 Ideal for learners from beginner to advanced levels looking to break into AI careers or research.
| Play Online |
| Data Preprocessing Studio | Missing Values, Feature Scaling, Encoding & Feature Engineering | UoA-COMPSCI 714 | COMPSCI 714 – Neural Networks | B06 - Data Preprocessing and Feature Engineering | Play Online |
| PyTorch Assignment Practice | Tabular MLP, BCEWithLogitsLoss, Optuna, FashionMNIST CNN, Saliency Maps | UoA-COMPSCI 714 | COMPSCI 714 – Neural Networks | B05c - MLP on Tabular Data with PyTorch | — |
| Model Evaluation Dashboard | Confusion Matrix, ROC/AUC, Precision-Recall & Cross-Validation | UoA-COMPSCI 713 | COMPSCI 713 – AI Fundamentals | B07 - Model Evaluation | — |
| KG Playground | RDF Triples, Knowledge Graphs, RAG & Conflict Detection | UoA-COMPSCI 713 | COMPSCI 713 – AI Fundamentals | A03 - Retrieval-Augmented Generation | Play Online |
| RNN Explorer | RNN/LSTM/GRU Architecture, Gates & Sequence Modelling | UoA-COMPSCI 713 | COMPSCI 713 – AI Fundamentals | B10 - RNNs · B10a - RNNs (714) | Play Online |
| CNN Explorer | Convolution, Pooling, Feature Maps, Architecture & Playground | UoA-COMPSCI 714 | COMPSCI 714 – Neural Networks | B09 - CNNs | Play Online |
| NEAT Explainer | NeuroEvolution, Topology Mutation, Speciation & Crossover | UoA-COMPSCI 713 | COMPSCI 713 – AI Fundamentals | — | — |
| Transformer Explorer | Self-Attention, Multi-Head Attention, Q/K/V & Architecture | UoA-COMPSCI 714 | COMPSCI 714 – Neural Networks | B11 - Attention & Transformers | Play Online |
| BPE Explorer | Byte Pair Encoding, Tokenization, Subwords & Vocabulary | UoA-COMPSCI 714 | COMPSCI 714 – Neural Networks | B12 - BPE | Play Online |
| Q-Learning Grid World | Reinforcement Learning, Q-Tables, Exploration vs Exploitation | UoA-COMPSCI 713 | COMPSCI 713 – AI Fundamentals | — | — |