Muhammad Mubashar
Summary
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RockstarMuhammad Mubashar is a Senior Learning Technologist and machine learning engineer with 7 years’ experience building and deploying deep learning education and production projects, currently helping run DeepLearning.AI’s flagship specialisation used by 700k+ learners. He combines hands-on model development in TensorFlow/Keras and Python (Flask/SQLAlchemy backends) with program management—standardising core processes, designing course materials, and leading cross-functional teams to deliver high‑quality learning products. Notably, he contributed course notebooks and assignments to DeepLearning.AI’s public TensorFlow repos and implemented an open-source one‑shot face recognition prototype integrated into a production app. Based in Lahore, he balances technical leadership with a collaborative, challenge‑driving style that peers actively seek out for guidance.
8 years of coding experience
Github Skills (7)
neural-network10
keras10
machine-learning10
jupyter-notebook10
tensorflow10
python10
image-classification10
Programming languages (2)
Github contributions (5)
https-deeplearning-ai/tensorflow-2-public
Jun 2021 - Sep 2021
Role in this project:
ML Engineer
ML EngineerContributions:81 commits, 21 PRs, 64 pushes in 3 months
Contributions summary:Muhammad's commits focus on adding and modifying content related to machine learning, specifically within the context of deep learning and TensorFlow 2. The primary contribution involves adding and modifying code for Course 1 materials, including a Jupyter Notebook (C1_W3_Assignment.ipynb) that demonstrates the creation of a Keras model for image classification, which suggests a focus on model development and implementation within the TensorFlow ecosystem. Additionally, the user integrated files for C2 and C2 W1 assignment files, indicative of the setup and management of the course's structure.
https-deeplearning-ai/tensorflow-1-public
Jun 2021 - Sep 2021
Role in this project:ML Engineer
ML EngineerContributions:1 review, 132 commits, 17 PRs in 2 months
Contributions summary:Muhammad's commits primarily involve the creation of a Jupyter Notebook for a "Hello World" deep learning example using a neural network and the MNIST dataset. The focus is on demonstrating the building blocks of a neural network by showing how to use a simple model to learn the relationship between two numbers and the impact of changing the parameters. The commits include implementation of the code for imports, defining the model, compiling the model, providing the data, training the model, and prediction.
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