I am using this repository to understand how neural networks learn, how optimization works, how different architectures solve different problems, and how deep learning models can be applied to real-world datasets.
- Perceptron & ANN
- CNN & Image Classification
- Data Preprocessing
- Scaling & Encoding
- Activation Functions
- Loss Functions & Optimizers
- Model Evaluation
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Perceptron & ANN on Iris dataset. TensorFlow • Keras • Scikit-learn |
ANN for predicting whether a plant needs water. TensorFlow • Keras • SGD |
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Perceptron, ANN & CNN comparison on MNIST. TensorFlow • Keras • CNN |
Clone the repository:
git clone https://github.com/suryanshsingh-codes/LearningDeepLearning.gitMove into the repository:
cd LearningDeepLearningInstall dependencies:
pip install -r requirements.txtLaunch Jupyter Notebook:
jupyter notebooknumpy
pandas
matplotlib
seaborn
scikit-learn
tensorflow
jupyter