Books, Presentations, Workshops, Notebook Labs, and Model Zoo for Software Engineers and Data Scientists wanting to learn the TF.Keras Machine Learning framework

TensorFlow and Deep Learning Tutorials

Detection of Accounting Anomalies using Deep Autoencoder Neural Networks - A lab we prepared for NVIDIA's GPU Technology Conference 2018 that will walk you through the detection of accounting anomalies using deep autoencoder neural networks. The majority of the lab content is based on Jupyter Notebook, Python and PyTorch.

中文的 tensorflow tutorial with jupyter notebooks

Scripts and notebooks to accompany the book Data-Driven Methods for Dynamic Systems

Ipython Notebooks for solving problems like classification, segmentation, generation using latest Deep learning algorithms on different publicly available text and image data-sets.

Implementation of a series of Neural Network architectures in TensorFow 2.0

This repository contains a notebook implementing an autoencoder based approach for intrusion detection, the full documentation of the study will be available shortly.

Collection of tensorflow notebooks tutorials for implementing some basic Deep Learning architectures.

Implementation notebooks and scripts of Artistic CNN Models and Generative Models like GANs, VAEs, GMMs, Boltzmann Machine etc. in TensorFlow, and Python. This repo aims to understand and make amazing things out of Neural Network layers.

Jupyter notebook of my autoencoder presentation

Final year project experimenting with clustering and topological data analysis of scRNA-seq data using Python and R across two Jupyter notebooks

A reference folder mapping some of my deep-learning repositories: RNN, GAN, DCGAN, seq2seq, Transfer learning, Autoencoders, Semi-supervised learning

Implementation of the fast neural style transfer algorithm on Keras. Includes Jupyter notebooks, python script and web app.

Object Detection and depth EStimation. A deep learning course project.

This repository contains my personal notes and Jupyter notebooks on Deep Learning Specialization course at the university Haute-Alsace.

This is notebook where I just create random idenitity and Autoencoder networks to actually see what the convolution layers are learning. Mostly I visualise the internal layers activation and conclusions can be drawn about what the hidden layers are learning. For anyone who is trying to build CNN. This can also help them guide them into thinking …

Generating images using Variational autoencoders

Implementation notebooks and scripts of the Deep Learning Nanodegree Foundations program.

Collection of my Deep Learning and Machine Learning notebooks.