Sharpen your software engineering skills and build robust models. MLOps, data processing and model deployment should definitely be in you arsenal

Data Processing · Software · Pytorch

How distributed training works in Pytorch: distributed data-parallel and mixed-precision training

Learn how distributed training works in pytorch: data parallel, distributed data parallel and automatic mixed precision. Train your deep learning models with massive speedups.

MLOps · Software · Tensorflow

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

Software · Tensorflow · Pytorch · Autoencoders

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


Build a Transformer in JAX from scratch: how to write and train your own models

How to develop and train a Transformer with JAX, Haiku and Optax. Learn by example how to code Deep Learning models in JAX


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

MLOps · Software

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?

MLOps · Software

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

MLOps · Software

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

MLOps · Software

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?

MLOps · Software · Tensorflow

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.

MLOps · Software · Tensorflow

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.

MLOps · Software

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

Software · Tensorflow

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 Processing · Software · Tensorflow

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 Processing · Software · Tensorflow

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

Software · Tensorflow

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)

Software · Tensorflow

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

Software · Tensorflow

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

MLOps · Software

Deep Learning in Production: Laptop set up and system design

An article course on how to write and deploy deep learning systems in production. python code optimization, cloud hosting and system design

Software · Convolutional Neural Networks · Machine Learning

Neural Network from scratch-part 2

How to buld a Convolutional neural network library using C++ and OpenCL

Software · Machine Learning

Neural Network from scratch-part 1

How to buld a neural network library using C++ and OpenCL