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amirjahantab/README.md

Hi there 👋

I'm a junior data scientist, My expertise lies in using statistical analysis and machine learning techniques.

🔭 Expertise

  • Python
  • Advanced Statistical Analysis (EDA, Inferential and descriptive Statistics)
  • Machine learning (supervised and unsupervised learning)
  • Deep learning
  • Data visualization (Matplotlib, Seaborn, Dash)
  • Computer Vision
  • Linux
  • Familiar with Backend Development (Django, Flask, RestAPI)

🌱 Projects

Here are some of my projects that showcase my skills and expertise:

Building a Word-Level Language Model for Persian

Developed a word-level language model for Persian text using Masnavi by Rumi, applying LSTM neural networks in TensorFlow/Keras for text generation. Implemented preprocessing, tokenization, and sequence modeling to predict and generate Persian language sequences.

Check out the repository for more information.

Object Localization and Classification

implementation of an object localization and classification model, focusing on the task of identifying and locating objects within images. The dataset used for this project is from Kaggle's competition. The model achived an Accuracy over $95$%.

Check out the repository for more information.

Facial Recognition

This project focuses on the development and implementation of a face recognition system using deep learning techniques.The dataset used for this project is from Kaggle's competition

Check out the repository for more information

CIFAR-10 Image Classifier

This project showcases the classification of the CIFAR-10 dataset using three different neural network architectures: a Linear Model, an Artificial Neural Network (ANN), and a Convolutional Neural Network (CNN).The model achived an Top 3 Accuracy over $94$%.

Check out the repository for more information.

Cat vs Dog Classifier

This project focuses on classifying images of cats and dogs using Convolutional Neural Networks (CNNs) with PyTorch. The dataset used for this project is from Kaggle's Dogs vs Cats Redux competition.

Check out the repository for more information.

Digit Classifier

This project explores the implementation of a custom activation function in a CNN model for digit classification using the MNIST dataset. The PPRsigELU activation function introduces flexibility through learnable parameters, potentially improving the model's performance.

Check out the repository for more information.

Pinned Loading

  1. Cats_vs_Dogs_Classification Cats_vs_Dogs_Classification Public

    This project focuses on classifying images of cats and dogs using Convolutional Neural Networks (CNNs) with PyTorch. The dataset used for this project is from Kaggle's "Dogs vs Cats Redux" competit…

    Jupyter Notebook

  2. CIFAR-10 CIFAR-10 Public

    This project showcases the classification of the CIFAR-10 dataset using three different neural network architectures: a Linear Model, an Artificial Neural Network (ANN), and a Convolutional Neural …

    Jupyter Notebook

  3. Regularization Regularization Public

    An effective way to avoid (or at least to reduce) overfitt

    Jupyter Notebook

  4. Create_RestFul_API Create_RestFul_API Public

    Effortlessly manage movie and TV show watchlists with this Django project, providing a RESTful API.

    Python

  5. estate_consultant_project estate_consultant_project Public

    Real estate consultant project under the command line in Python

    Python 2

  6. Concurrency_In_Python Concurrency_In_Python Public

    Concurrency is the ability of a program to handle multiple tasks simultaneously. In Python, there are two primary ways to achieve concurrency: multi-threading and multi-processing.

    Python 1