In The Name Of GOD
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 by Stanford University
- TensorFlow Developer Specilization 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 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) by IBM
Seeds Dataset Classifier
This folder is for a classifier for the Seeds dataset from here. 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 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 :)
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