https://github.com/farshidnooshi/tensorflow-notebooks

A collection of notebooks with TensorFlow and the Keras API for various deep-learning and machine learning problems
https://github.com/farshidnooshi/tensorflow-notebooks

cnn-keras deep-learning keras lstm machine-learning neural-network nlp-machine-learning rnn rnn-tensorflow tensorflow tensorflow-examples tensorflow-tutorials

Last synced: 3 months ago
JSON representation

A collection of notebooks with TensorFlow and the Keras API for various deep-learning and machine learning problems

• Host: GitHub
• URL: https://github.com/farshidnooshi/tensorflow-notebooks
• Owner: FarshidNooshi
• License: apache-2.0
• Created: 2022-06-12T16:31:19.000Z (over 2 years ago)
• Default Branch: master
• Last Pushed: 2023-06-16T09:46:55.000Z (over 1 year ago)
• Last Synced: 2024-09-26T21:01:51.340Z (3 months ago)
• Topics: cnn-keras, deep-learning, keras, lstm, machine-learning, neural-network, nlp-machine-learning, rnn, rnn-tensorflow, tensorflow, tensorflow-examples, tensorflow-tutorials
• Language: Jupyter Notebook
• Homepage:
• Size: 4.32 MB
• Stars: 29
• Watchers: 3
• Forks: 3
• Open Issues: 5
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        


  
 In The Name Of GOD 





  

  

[![CI](https://github.com/FarshidNooshi/TensorFlow-Notebooks/actions/workflows/action.yml/badge.svg)](https://github.com/FarshidNooshi/TensorFlow-Notebooks/actions/workflows/action.yml)


# TensorFlow Notebooks

This repository hosts my extra works and projects in the field of Machine Learning and deep-learning problems with the **TensorFlow platform**. the repository contains several folders in which each of them is for an specific course(or specialization) or project.

## TensorFlow Developer

This folder is for my works(assignments&labs) at TensorFlow Developer Coursera Specialization program and courses which I have taken for that specialization. below is the list of all the specializations and courses with their respective certificates That I have had.

- [**Machine Learning**](https://www.coursera.org/account/accomplishments/certificate/8YFX6GGF8PB9) by Stanford University

- [**TensorFlow Developer Specilization**](https://www.coursera.org/account/accomplishments/specialization/certificate/GS2KGD5NEU3D) by DeepLearning.AI 

  - Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

  - Convolutional Neural Networks in TensorFlow

  - Natural Language Processing in TensorFlow

  - Sequences, Time Series and Prediction

- [**Deep Learning Specialization**](https://www.coursera.org/account/accomplishments/specialization/certificate/KAC9TXFGAVPA) by DeepLearning.AI

  - Neural Networks and Deep Learning

  - Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

  - Structuring Machine Learning Projects

  - Convolutional Neural Networks

  - Sequence Models

- **Reinforcement Learning Specialization** by University of Alberta

  - Fundamentals of Reinforcement Learning

- [**Introduction to Artificial Intelligence (AI)**](https://www.coursera.org/account/accomplishments/certificate/4BBSHBTDPSXR) by IBM

## Seeds Dataset Classifier

This folder is for a classifier for the Seeds dataset from [here](https://archive.ics.uci.edu/ml/datasets/seeds). the data is first preprocessed with standard normalization and then feeded to various architectures of neural networks to see the overfitting effect and learning curves.

for better understanding of the classfier **Tensorboard** is used to analyze the results of the learning, and other callbacks such as

early stopping is also used to compile the models. for pre-processing the data _Pandas_ library were used.

## RCV1 Dataset Visualization

In this project, we have used the RCV1 dataset to visualize the data. 

The dataset is available in the following link: [RCV1 Dataset](https://scikit-learn.org/0.18/datasets/rcv1.html) 

The dataset is a collection of news articles from BBC. The dataset is divided into 5 categories: Business, Entertainment, Politics, Sport, Tech. and the visualization is done with Self-Organizing Map (SOM) and K-Means Clustering. 

# Contribution

If you find a bug or typo please raise an issue :)