Some DNN model optimization experiments and notebooks

Deep Learning Machine Learning Templates

An Ipython notebook, shows how I solved Tap30 Competition problem by kaggle using Recurrent neural networks.

In this notebook, I implemented a recurrent neural network (Long short-term memory) using PyTorch that performs sentiment analysis.

A repository of notebooks containing recurrent neural nets engineered for NLP tasks

This repo helps keep track about exercises, Jupyter Notebooks and projects from the Deep Learning Nanodegree Program offered at Udacity.

Learning how to use jupyter notebook, tensorflow, numpy and other librairies for machine learning projects. Following Thibaut Neveu's tutorial.

This repository contains all the notebooks, datasets and projects that have been implemented as a part of my Tensorflow 2 Keras bootcamp.

This project is intended to use Amazon's SageMaker platform, it is assumed to have a working notebook instance which can be used to clone the deployment repository.

This repository contains programming assignments for the Deep Learning Specialization by deeplearning.AI. It includes Jupyter Notebooks for exercises in neural networks, hyperparameter tuning, convolutional networks, and sequence models.

The repository contains notebooks created for collecting and preprocessing the corpus of diary entries and for experiments on creating models for predicting gender, age groups of authors and the time period of text creation.

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

In this notebook, I built gradient boosting classifier and LSTM model to classify and predict the mineral scaling potential of formation water in US shale basins.

Python Jupyter notebooks for building and evaluating deep learning models using both the FNG values and simple closing prices to determine if the FNG indicator provides a better signal for cryptocurrencies than the normal closing price data.

Text generation using a character-based RNN with LSTM cells. We will work with a dataset of Shakespeare's writing from Andrej Karpathy's The Unreasonable Effectiveness of Recurrent Neural Networks. Given a sequence of characters from this data ("Shakespear"), train a model to predict the next character in the sequence ("e"). Longer sequences of …

Deep Learning Python Notebooks

Notebooks prepared while reading the Deep Learning book

A series of notebooks to familiarize with some important data processing and analysis pipelines based on PyTorch

Implementation notebooks and scripts of the computer vision nanodegree program from udacity in pytorch.

A series of documented Jupyter notebooks implementing various neural nets.