Code Machine learning models without any frameworks, Numpy only.

• Neural Network
• Clustering
• Decomposition
• Probabilistic
• Regression
• Trees based
• Timeseries
• Signal processing
• Monte-carlo

1. Deep Feed-forward

• gradient descent
• momentum
• nesterov
• rmsprop
• adagrad
• adam

2. Vanilla recurrent

• gradient descent
• momentum
• nesterov
• rmsprop
• adagrad
• adam

3. Long-short-term-memory recurrent

• gradient descent
• momentum
• nesterov
• rmsprop
• adagrad
• adam

4. gated-recurrent-unit recurrent

• gradient descent
• momentum
• nesterov
• rmsprop
• adagrad
• adam

5. Convolutional

• atrous 1D
• atrous 2D
• average pooling 1D
• average pooling 2D
• convolution 1D
• convolution 2D
• max pooling 1D
• max pooling 2D

6. batch-normalization
7. Dropout
8. Regularization
9. Neuro-evolution

• Iris classification
• Iris classification + Novelty search
• Regression

10. Evolution-strategy

1. DBScan
2. K-Mean
3. K-Nearest Neighbors

1. Latent Dirichlet Allocation
2. Latent Semantic Analysis
3. Linear Decomposition Analysis
4. Non-negative Matrix Feature
5. Principal Component Analysis
6. TSNE

1. Gaussian TF-IDF
2. Multinomial TF-IDF
3. Hidden Markov
4. Neural Network

1. Linear
2. Polynomial
3. Lasso
4. Ridge
5. Sigmoid logistic

1. Decision Tree
2. Random Forest
3. Adaptive Boosting
4. Bagging
5. Gradient Boosting

1. Moving Average
2. Linear Weight Moving Average
3. John-Ehlers
4. Noise Removal-Get
5. Anchor Smoothing
6. Detect Outliers
7. ARIMA
8. Seasonal Decomposition

1. Convolutional 1D
2. Convolutional 2D
3. Pass-Filters

1. Markov Chain

• metropolis hasting normal distribution
• metropolis hasting stock forecasting

2. Pi estimation
3. Stock market prediction

Some of results are not good because of softmax and cross entropy functions I code.

If found any error on my chain-rules, feel free to branch.