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Learning & Improving
🎯
Learning & Improving
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animikhaich/README.md

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🧐 About Me

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👋 Hi there! I'm Animikh and I'm a Machine Learning Engineer, who loves to watch Anime and play Video Games. I'm also a MS in AI student working with Prof. Eshed Ohn-Bar on end-to-end Autonomous Driving @ H2X Lab.

Before this, I was a Computer Vision Engineer and Lead @ Wobot.ai, where I spearheaded the development of a robust Deep Learning tech stack, that powers real-time video analytics across hundreds of cameras worldwide today.

I ❤️ to build and believe Multi-Modal Self-Supervised Learning is the path to AGI 🤫. I focus on Generative AI, Autonomous Driving, Scene Understanding and 3D Vision.

I'm always open to new opportunities and a nice chat ☕. Feel free to reach out to me on LinkedIn or animikhaich@gmail.com.

💻 Tech Stack

Visual Studio Code Sublime Text Linux macOS Windows

Python C++ TensorFlow PyTorch Keras OpenCV NumPy scikit-learn mlflow Matplotlib Flask

AWS Azure Git Docker

📊 Stats

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🔗 Socials

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  1. No-Code-Classification-Toolkit No-Code-Classification-Toolkit Public

    Containerized Tensorflow-based image classification training utility with Streamlit-based interface designed to choose between common architectures and optimizers for quick hyperparameter tuning.

    Python 7 3

  2. 3D-Text2LIVE 3D-Text2LIVE Public

    Zero-shot, text-driven appearance manipulation on multiple views of an object to generate 3D renderings.

    Python 2 1

  3. VGGNet-Tensorflow VGGNet-Tensorflow Public

    VGGNet-Family (11, 13, 16 & 19) Implementation to train on ImageNet 2012 using Tensorflow 2.x

    Jupyter Notebook 5 1

  4. Semantic-Segmentation-using-AutoEncoders Semantic-Segmentation-using-AutoEncoders Public

    Lightweight and Fast Person Segmentation using Autoencoders (Trained Weights Included)

    Jupyter Notebook 19 7

  5. ECG-Atrial-Fibrillation-Classification-Using-CNN ECG-Atrial-Fibrillation-Classification-Using-CNN Public

    This is a CNN based model which aims to automatically classify the ECG signals of a normal patient vs. a patient with AF and has been trained to achieve up to 93.33% validation accuracy.

    Jupyter Notebook 46 18

  6. Deep-Convolutional-Background-Subtractor Deep-Convolutional-Background-Subtractor Public

    End-to-end CNN-based Autoencoder that can segment any objects even if it is out of the classes present in the training set.

    Jupyter Notebook 4 1