essence - AutoDiff DAG constructor, built on numpy and Cython

•    Python

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A directed acyclic computational graph builder, built from scratch on numpy and C, with auto-differentiation supported. This was not just another deep learning library, its clean code base was supposed to be read. Great for any one who want to learn about Backprop design in deep learning libraries.

machine-learning dropout lstm mnist lenet neural-turing-machines question-answering computational-graphs auto-differentiation convolutional-neural-networks convolutional-networks recurrent-neural-networks lstm-model deep-learning deep-q-network reinforcement-learning cartpole

time-series-forecasting-pytorch - Acquiring data from Alpha Vantage and predicting stock prices with PyTorch's LSTM

•    Jupyter

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Deep learning is part of a broader family of machine learning methods based on artificial neural networks, which are inspired by our brain's own network of neurons. Among the popular deep learning paradigms, Long Short-Term Memory (LSTM) is a specialized architecture that can "memorize" patterns from historical sequences of data and extrapolate such patterns for future events. Since the financial market is naturally comprised of historical sequences of equity prices, more and more quantitative researchers and finance professionals are using LTSM to model and predict market price movements. In this project, we will go through the end-to-end machine learning workflow of developing an LTSM model to predict stock market prices using PyTorch and Alpha Vantage APIs.

machine-learning deep-learning neural-network pytorch lstm-model time-series-forecasting