15+ Machine/Deep Learning Projects in Ipython Notebooks

Beginner-friendly collection of Python notebooks for various use cases of machine learning, deep learning, and analytics. For each notebook there is a separate tutorial on the relataly.com blog.

دوره جامع یادگیری عمیق با Tensorflow و Keras

Jupyter Notebook for Human Activity Recognition (HAR) with 1D Convolutional Neural Network in Python and Keras

Machine Learning Engineer Nanodegree portfolio, which includes projects and their notebooks/reports.

The repository contains the Jupyter Notebook that perform semantic segmentation using the famous U-Net. The encoder of the U-Net is replaced with the pretrained encoder.

This is a repository with various classifier written in jupyter notebook that works on hand written digits images.

iPython notebook for training a Keras model on the CIFAR-10 dataset

This repository contains the Jupyter Notebook for the InceptionV3 CNN Model trained on the Stanford Dogs Dataset.

Exporting the Mask RCNN with TF-Serving (REST, GRPC, Notebooks provided)

A collection of deep learning notebooks.

This repository contains the jupyter notebooks for the custom-built DenseNet Model build on Tiny ImageNet dataset

Notebooks for colab that use the TPU

This repository contains the jupytor notebook code for digit recognition using machine learning.

TensorFlow 2.X hakkında (Türkçe) öğretici notebooklar paylaşıyorum. Repoyu yararlı bulursanız yıldızlayarak destek olabilirsiniz :)

Jupyter notebooks for practicing Python data analysis and visualization with Matplotlib, NumPy, Pandas, SciPy, and Seaborn.

Bird Classifier developped in tensorflow using pre-trained model from Tensorflow Hub and running on Google Colab

Some useful examples of Deep Learning (.ipynb)

A set of notebooks that explores the power of Recurrent Neural Networks (RNNs), with a focus on LSTM, BiLSTM, seq2seq, and Attention.

A notebook containing implementations of different graph deep node embeddings along with benchmark graph neural network models in tensorflow. This has been taken from https://www.kaggle.com/abhilash1910/nlp-workshop-ml-india-deep-graph-learning to apply GNNs/node embeddings on NLP task.