JAX for Machine Learning: how it works and why learn it
An introduction to JAX, its best features alongside with code snippets for you to get started
The theory behind Latent Variable Models: formulating a Variational Autoencoder
Explaining the mathematics behind generative learning and latent variable models and how Variational Autoencoders (VAE) were formulated (code included)
Best bootcamps and programs to learn Machine Learning and Data Science
A curated list of the top bootcamps and platforms to learn Machine Learning and Data Science.
A journey into Optimization algorithms for Deep Neural Networks
An overview of the most popular optimization algorithms for training deep neural networks. From stohastic gradient descent to Adam, AdaBelief and second-order optimization
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
How to use Docker containers and Docker Compose for Deep Learning applications
Learn how to containerize a deep learning model using Docker. Start with the basic concepts behind containers, package a Tensorflow application with Docker and combine multiple images using Docker compose
How to use uWSGI and Nginx to serve a Deep Learning model
Serving a Tensorflow model to users with Flask, uWSGI as a web server and Nginx as a reverse proxy. Why we need both uWSGI and Flask, why we need Nginx on top of uWSGI and how everything is connected together?
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.
Distributed Deep Learning training: Model and Data Parallelism in Tensorflow
How to train your data in multiple GPUs or machines using distributed methods such as mirrored strategy, parameter-server and central storage.
How to train a deep learning model in the cloud
How to create a VM instance in Google cloud, transfer a deep learning model and run a training job using external data from cloud storage
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
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
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
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)