Data preprocessing for deep learning: How to build an efficient big data pipeline
How to develop high performance input pipelines in Tensorflow using the ETL pattern and functional programming
Data preprocessing for deep learning: Tips and tricks to optimize your data pipeline using Tensorflow
How to optimize the data processing pipeline using batching, prefetching, streaming, caching and iterators
MLOps
Best practices on Machine Learning infrastructure. How to build, maintain and scale procuction-ready deep learning systems.
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Introduction to Kubernetes with Google Cloud: Deploy your Deep Learning model effortlessly
What is Kubernetes? What are the basic principles behind it? Why it might be the best option to deploy Machine Learning applications? What features it provides to help us maintain and scale our infrastructure? How to set up a simple Kubernetes cluster in Google cloud?
Scalability in Machine Learning: Grow your model to serve millions of users
Follow along with a small AI startup on its journey to scale from 1 to millions of users. Learn what's a typical process to handle steady growth in the userbase, and what tools and techniques one can incorporate. All from a machine learning perspective
Pytorch
Implement basic Deep Learning models and advanced real-life applications with Pytorch
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Understanding einsum for Deep learning: implement a transformer with multi-head self-attention from scratch
Learn about the einsum notation and einops by coding a custom multi-head self-attention unit and a transformer block
Recurrent neural networks: building a custom LSTM cell
Are you interested to see how recurrent networks process sequences under the hood? That’s what this article is all about. We are going to inspect and build our own custom LSTM model. Moreover, we make some comparisons between recurrent and convolutional modules, to maximize our understanding.
Software
Best practices on Machine Learning Enginnering. How to implement, deploy and scale deep learning systems.
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JAX vs Tensorflow vs Pytorch: Building a Variational Autoencoder (VAE)
A side-by-side comparison of JAX, Tensorflow and Pytorch while developing and training a Variational Autoencoder from scratch
A complete Weights and Biases tutorial
Learn about the Weights and Biases library with a hands-on tutorial on the different features and visualizations.
Tensorflow Extended (TFX) in action: build a production ready deep learning pipeline
A tutorial on how to get started with Tensorflow Extended and how to design and execute a Deep Learning pipeline
Deploy a Deep Learning model as a web application using Flask and Tensorflow
How to expose a deep learning model, built with Tensorflow, as an API using Flask. Learn how to build a web application to serve the model to the users and how to send requests to it with an HTTP client.