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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How to Unit Test Deep Learning: Tests in TensorFlow, mocking and test coverage
Explore unit testing in tensorflow code using tf.test(), mocking and patching objects, code coverage and different examples of test cases in machine learning applications
Logging and Debugging in Machine Learning - How to use Python debugger and the logging module to find errors in your AI application
A guide on how to debug machine learning code and how to use logs to catch errors in production (including a set of useful Tensorflow functions to make your debugging life easier)
How to build a custom production-ready Deep Learning Training loop in Tensorflow from scratch
Building a custom training loop in Tensorflow and Python with checkpoints and Tensorboards visualizations
Best practices to write Deep Learning code: Project structure, OOP, Type checking and documentation
A deep learning python project template, object oriented techniques such as abstraction, inheritance and static methods, type hints and docstrings