Python: Constructing Time Series Sequence Samples Dataset

This post construct the multivariate time series data into sequence samples dataset for RNNs, LSTMs, CNNs, and similar models in Keras or Tensorflow.

Python: t-SNE dimensional reduction technique

This post shows how to use the t-SNE (t-Distributed Stochastic Neighbor Embedding) in Python, which is a non-linear probabilistic technique for dimensionality reduction.

TensorFlow: Weighted Average Layer with Trainable Weights

This post implements TensorFlow code that estimates the weights used to construct an output composite time series from three input time series.

TensorFlow: Weighted Average Layer with Fixed Weights

This post demonstrates the process of implementing a TensorFlow code to calculate the weighted average of the previous layer's output. This simple exercise serves as a practical guide to creating a custom layer in TensorFlow.

TensorFlow: Variational Autoencoder (VAE) for MNIST Digits

This post demonstrates the implementation of Tensorflow code for Variational Autoencoder (VAE) using a well-established example with MNIST digit data.

Python: Solving ODEs with Deep Learning in TensorFlow

This post explains how to implement TensorFlow code to solve ODE problems. This approach is known as 'Physics-Informed Neural Networks' (PINNs) or 'Neural ODEs.'

PyTorch : A Simple MLP model for Univariate Time Series Forecasting

This post explains how to implement a simple MLP model for a univariate time series forecasting using PyTorch. The estimation of the PyTorch model is slightly different from the Keras model, so it's worth focusing on how it differs.

Python : Save and Load Tensorflow Keras Model

This post shows how to save and load your Keras model. The loaded model contains the same parameters (weights and biases) and the model structure as the final model you saved.

Python : Tensorflow Keras Tuner for the optimization of hyper parameters

This post shows how to use the Keras Tuner for the hyper parameter optimization. This can avoid many for-loops effectively, which are used when the grid search for hyper parameters is necessary.

Python: Fixing the Random Seed for Reproducibility

This post shows how to fix the random seed to get reproducible results with Keras.

Python : Plotting Architecture of Tensorflow Keras model

This post shows how to plot an architecture flow of Tensorflow Keras model by using plot_model() function.

Understanding the LSTM model

This post gives a brief introduction to the basic structure of LSTM (Long-Short Term Memory) model with an intuitive figures and explanations. Let's interpret "gate" as "rate" or "weight" for grasping the concept of LSTM faster since we are not engineers but finance-related practitioners.