This Humans of ML interview with Han Xiao covers the ethics of AI, open-source entrepreneurship, how writing made Han a better coder, and more. | Continue reading
The list of the best machine learning & deep learning books for 2020. | Continue reading
[Series] Based on his deep experience, FloydHub CTO Naren discusses how should companies think about & setup their ML infrastructure. This article focuses on AWS EC2 machines. | Continue reading
We will cover often-overlooked concepts vital to NLP, such as Byte Pair Encoding, and discuss how understanding them leads to better models. | Continue reading
This Humans of Machine Learning interview covers Emil Wallner and his hero’s journey, self-taught approach to education, experience with AI, and path to Google. | Continue reading
Within three years deep learning will change front-end development. It will increase prototyping speed and lower the barrier for building software. | Continue reading
This article discusses GPT-2 and BERT models, as well using knowledge distillation to create highly accurate models with fewer parameters than their teachers | Continue reading
Bayes’ Theorem is about more than just conditional probability, and Naive Bayes is a flavor of the theorem which adds to its complexity and usefulness. | Continue reading
Once you've built your classifier, you need to evaluate its effectiveness with metrics like accuracy, precision, recall, F1-Score, and ROC curve. | Continue reading
Machine learning advancements lead to new ways to train models, as well as deceive them. This article discusses ways to train and defend against attacks. | Continue reading
This deep dive is all about neural networks - training them using best practices, debugging them and maximizing their performance using cutting edge research. | Continue reading
What is Attention, and why is it used in state-of-the-art models? This article discusses the types of Attention and walks you through their implementations. | Continue reading
This deep dive on Python parallelization libraries - multiprocessing and threading - will explain which to use when for different data scientist problem sets. | Continue reading
The world of NLP already contains an assortment of pre-trained models and techniques. This article discusses how to best discern which model will work for your goals. | Continue reading
Is it possible to use machine learning with small data? Yes, it is! Here's N-Shot Learning. | Continue reading
The Gated Recurrent Unit (GRU) is the newer version version of the more popular LSTM. Let's unveil this network and explore the differences between these 2 siblings. | Continue reading
This article discusses effective ways of handling the data in machine learning projects. | Continue reading
Getting sufficient clean, reliable data is one of the hardest parts of data science. Web scraping automates the process of visiting web pages, downloading the data, and cleaning the results. With this technique, we can create new datasets from a large compendium of web pages. | Continue reading
This article gives the readers a checklist to structure their machine learning (applies to deep ones too) projects in effective ways. | Continue reading
Generative adversarial networks (GANs) have been the go-to state of the art algorithm to image generation in the last few years. In this article, you will learn about the most significant breakthroughs in this field, including BigGAN, StyleGAN, and many more. | Continue reading
Long Short-Term Memory (LSTM) Networks have been widely used to solve various sequential tasks. Let's find out how these networks work and how we can implement them. | Continue reading
Genetic algorithms are a specific approach to optimization problems that can estimate known solutions and simulate evolutionary behavior in complex systems. | Continue reading
While computer vision techniques have been used with limited success for detecting corrosion from images, Deep Learning has opened up whole new possibilities | Continue reading
Learn about the basic concepts of reinforcement learning and implement a simple RL algorithm called Q-Learning. | Continue reading
The Key Insights Behind the Greatest Language Model of all Time | Continue reading
Learn the basics of Recurrent Neural Networks and build a simple Language Model with PyTorch | Continue reading
In this article, get a gentle introduction to the world of unsupervised learning and see the mechanics behind the old faithful K-Means algorithm. | Continue reading
Text summarization is a common in machine learning. In this article, we'll explore how to create a simple extractive text summarization algorithm. | Continue reading
Learn what anomalies are and several approaches to detect them along with a case study. | Continue reading
Let's undercover what they will be the Top 10 NLP trends of 2019 | Continue reading
Explore how deep learning is changing the fashion industry by training your own visual recommendation model for similar fashion images using TensorFlow and FloydHub | Continue reading
Learn the history and technology of autonomous cars in this Part 1 of a series on building a self-driving toy car with Raspberry Pi, Keras, and FloydHub GPUs. | Continue reading
Build your own deep learning dataset and detection model using public Instagram photos of #streetart. | Continue reading
Use TensorFlow to build your own haggis-hunting app for Burns Night! The Scottish quest for the mythical wild haggis just got easier with deep learning. | Continue reading
Can we teach a neural net to convert face embedding vectors back to images? | Continue reading
Let's build an NLP model that can help out your customer support agents by suggesting previously-asked, similar questions. | Continue reading
Neural networks are transforming the way we study DNA and population genetics. Learn more about deep learning at Bayer Crop Science from Lex Flagel in this #humansofml interview. | Continue reading
Most of your customer support questions have already been asked. Learn how to use sentence embeddings to automate your customer support with AI. | Continue reading
Dive into deep reinforcement learning by training a model to play the classic 1970s video game Pong — using Keras, FloydHub, and OpenAI's "Spinning Up." | Continue reading
Dive into deep reinforcement learning by training a model to play the classic 1970s video game Pong — using Keras, FloydHub, and OpenAI's "Spinning Up." | Continue reading
Explore the latest trends in Brain-Computer Interfaces - and train a deep learning model to predict what people are doing from fluctuations in their brain voltage readings. | Continue reading
Christine McLeavey Payne may have finally cured songwriter's block. Her project Clara is an LSTM neural network that composes piano and chamber music. Learn more in this @floydhub #humansofml inteview. | Continue reading
Building a cousin image classification app using a convolutional neural net for your Thanksgiving family reunion using fast.ai and FloydHub. | Continue reading
Building a cousin image classification app using a convolutional neural net for your Thanksgiving family reunion using fast.ai and FloydHub. | Continue reading
Jason Antic's DeOldify deep learning project not only colorizes images but also restores them with stunning results. Learn more in this FloydHub #humansofml interview. | Continue reading
Jason Antic's DeOldify deep learning project not only colorizes images but also restores them with stunning results. Learn more in this FloydHub #humansofml interview. | Continue reading
Learn how to code a transformer model in PyTorch with an English-to-French language translation task on the FloydHub blog. | Continue reading
Building an English-French language translator neural network from scratch with deep learning. | Continue reading