ai-hands-on
A group of notebooks and other files which can help you learn AI from scratch.
File Explorer
Download Latest Version (.zip)- FUNDING.yml
- ANN(Artificial_Neural_Network_).ipynb
- Efficient_Iteration_In_Pandas.ipynb
- 1. Math Functions.ipynb
- 2. Partial Derivatives.ipynb
- 3. Vectors.ipynb
- 4. Gradient.ipynb
- 5. Metrics.ipynb
- 6. Probability.ipynb
- 1. Tensor.ipynb
- 2. Matrix Multiplication.ipynb
- 3. Tensor Transposing.ipynb
- 4. Reshaping tensor.ipynb
- 5. Indexing & Slicing.ipynb
- 6. Tensor Concatenating.ipynb
- 7. Linspace and Arrange Tensor.ipynb
- 1.Single Neuron.ipynb
- 2. Layer building.ipynb
- 3. BackPropagation.md
- 1. Attention Mechanism.ipynb
- 2. Self Attention.ipynb
- 3. Multi-head Attention.ipynb
- 4. Decoder.ipynb
- firewall_report.txt
- incident_response.txt
- vulnerability_scan.txt
- cyber_index.faiss
- metadata.npy
- texts.npy
- generate_answer.cpython-313.pyc
- retrieve_context.cpython-310.pyc
- retrieve_context.cpython-313.pyc
- app.py
- create_embeddings.py
- extract_text.py
- generate_answer.py
- llm_provider.py
- retrieve_context.py
- test_integration.py
- test_llm_provider.py
- .env.example
- LICENSE
- README.md
- requirements.txt
- main.cpython-312.pyc
- utils.cpython-312.pyc
- rotated_sample_3.png
- sample_1.png
- sample_2.png
- sample_3.png
- main.py
- pytess_gemini.ipynb
- README.md
- render.yaml
- requirements.txt
- utils.py
- 1_Base_ML_Model_Linear_&_Ridge_Regression.ipynb
- Decision_Tree_Model_Implementation.ipynb
- LM_Implementation.ipynb
- Logistic_Regression_Model_Implementation.ipynb
- NaiΜve_Bayes_Classification_Implementation_.ipynb
- NaΓ―ve_Bayes_Classification_Implementation_.ipynb
- Support_Vector_Machine_[SVM]_Classification_Implementation_.ipynb
- Anthony Molinaro - SQL Cookbook (Cookbooks (O'Reilly))-O'Reilly and Associates (2006).pdf
- Chip Huyen - AI Engineering_ Building Applications with Foundation Models-O'Reilly Media, Inc. (2024).pdf
- Deep Learning Book.pdf
- Hands_On_Machine_Learning_with_Scikit_Learn_and_TensorFlow.pdf
- Human-in-the-LoopMachine Learning.pdf
- Machine Learning With Python CookBook.pdf
- Mathematics for Inference and Machine Learning.pdf
- .gitignore
- customer_analysis.ipynb
- geolocation_analysis.ipynb
- order_items_analysis.ipynb
- order_payments_analysis.ipynb
- order_reviews_analysis.ipynb
- orders_analysis.ipynb
- products_analysis.ipynb
- README.md
- sellers_analysis.ipynb
- test.txt
- 1_LLM_Fundamentals_.ipynb
- 2_Tokens_and_Token_Embeddings.ipynb
- learning_path.md
- __init__.py
- service.py
- utils.py
- main.py
- Project.md
- pyproject.toml
- README.md
- server.py
- test.py
- ATLAS_CLOUD_PROVIDER_REVIEW.md
- LICENSE
- README.md
- requirements.txt
- test.txt
# Installation Guide
1. Get the code
git clone https://github.com/Ramakm/ai-hands-on
Downloads the entire project code from GitHub to your computer.
cd ai-hands-on
Moves into the project folder you just downloaded.
2. Python
Easy RecommendedPrerequisites
pip install -r requirements.txt
Installs the Python libraries listed in requirements.txt (or similar).
jupyter lab
Launches Jupyter in your browser so you can open and run the notebook (.ipynb) files.
jupyter notebook
Launches Jupyter in your browser so you can open and run the notebook (.ipynb) files.
If it runs without errors and prints output in the terminal, it worked.
Pulled directly from this repo's README.
// repository documentation
Was this content helpful?
(0 ratings)