NLP Datasets: How good is your deep learning model?
With the rapid advance in NLP models we have outpaced out ability to measure just how good they are at human level language tasks. We need better NLP datasets now...
Read more →Posts tagged with "machine-learning"
Explore our collection of articles about machine learning, deep learning, neural networks, and AI techniques.
Best Machine Learning Books (Updated for 2020)
The list of the best machine learning & deep learning books for 2020.
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Tokenizers: How machines read
We will cover often-overlooked concepts vital to NLP, such as Byte Pair Encoding, and discuss how understanding them leads to better models.
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Distilling knowledge from Neural Networks to build smaller and faster models
This article discusses GPT-2 and BERT models, as well using knowledge distillation to create highly accurate models with fewer parameters than their teachers
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Naïve Bayes for Machine Learning – From Zero to Hero
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.
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A Pirate's Guide to Accuracy, Precision, Recall, and Other Scores
Once you've built your classifier, you need to evaluate its effectiveness with metrics like accuracy, precision, recall, F1-Score, and ROC curve.
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Introduction to Adversarial Machine Learning
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.
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Training Neural Nets: a Hacker’s Perspective
This deep dive is all about neural networks - training them using best practices, debugging them and maximizing their performance using cutting edge research.
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Attention Mechanism
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.
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N-Shot Learning: Learning More with Less Data
Is it possible to use machine learning with small data? Yes, it is! Here's N-Shot Learning.
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Gated Recurrent Unit (GRU) With PyTorch
The Gated Recurrent Unit (GRU) is the newer version of the more popular LSTM. Let's unveil this network and explore the differences between these 2 siblings.
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Generative Adversarial Networks - The Story So Far
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...
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Long Short-Term Memory: From Zero to Hero with PyTorch
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.
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Introduction to Genetic Algorithms
Genetic algorithms are a specific approach to optimization problems that can estimate known solutions and simulate evolutionary behavior in complex systems.
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Using Deep Learning and TensorFlow Object Detection API for Corrosion Detection and Localization
While computer vision techniques have been used with limited success for detecting corrosion from images, Deep Learning has opened up whole new possibilities
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An introduction to Q-Learning: Reinforcement Learning
Learn about the basic concepts of reinforcement learning and implement a simple RL algorithm called Q-Learning.
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A Beginner’s Guide on Recurrent Neural Networks with PyTorch
Learn the basics of Recurrent Neural Networks and build a simple Language Model with PyTorch
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Introduction to K-Means Clustering in Python with scikit-learn
In this article, get a gentle introduction to the world of unsupervised learning and see the mechanics behind the old faithful K-Means algorithm.
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A Gentle Introduction to Text Summarization in Machine Learning
Text summarization is a common problem in the fields of machine learning and natural language processing (NLP). In this article, we'll explore how to create a simple extractive text summarization...
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Introduction to Anomaly Detection in Python
Learn what anomalies are and several approaches to detect them along with a case study.
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Meta-Reinforcement Learning
The general trend in machine learning research is to stop fine-tuning models, and instead use a meta-learning algorithm that automatically finds the best architecture and hyperparameters. What about meta-reinforcement learning...
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Best Deep Learning Courses: Updated for 2019
The list of the best machine learning & deep learning courses and MOOCs for 2019.
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Best Deep Learning Books: Updated for 2019-2020
The list of the best machine learning & deep learning books for 2019.
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Controlling a 2D Robotic Arm with Deep Reinforcement Learning
Learn how to control a robotic arm using deep reinforcement learning techniques.
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Recommending Similar Fashion Images with Deep Learning
Explore how deep learning is changing the fashion industry by training your own visual recommendation model for similar fashion images using TensorFlow and FloydHub
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Building a Toy Self-Driving Car: Part One
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.
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On Building an Instagram Street Art Dataset and Detection Model
Build your own deep learning dataset and detection model using public Instagram photos of #streetart.
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Haggis, Not Haggis: How to build a haggis detection app with TensorFlow, Keras, and FloydHub for Burns Night
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.
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Inverting Facial Recognition Models
Can we teach a neural net to convert face embedding vectors back to images?
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Using NLP to Automate Customer Support, Part Two
Let's build an NLP model that can help out your customer support agents by suggesting previously-asked, similar questions.
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Exploring DNA with Deep Learning
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.
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Using Sentence Embeddings to Automate Customer Support, Part One
Most of your customer support questions have already been asked. Learn how to use sentence embeddings to automate your customer support with AI.
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Spinning Up a Pong AI With Deep Reinforcement Learning
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."
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Reading Minds with Deep Learning
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.
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Generating Classical Music with Neural Networks
Christine McLeavey Payne may have finally cured songwriter's block. Her recent project Clara is a long short-term memory (LSTM) neural network that composes piano and chamber music. Just give Clara...
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Recognize relatives using deep learning
Building a cousin image classification app using a convolutional neural net for your Thanksgiving family reunion using fast.ai and FloydHub.
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DeOldify: Colorizing and Restoring Old Images and Videos with Deep Learning
Jason Antic's DeOldify deep learning project not only colorizes images but also restores them with stunning results. Learn more about his approach in this FloydHub #humansofml interview.
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Found in translation: Building a language translator from scratch with deep learning
Build an English-French language translator from scratch using PyTorch.
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How to code The Transformer in PyTorch
Learn how to code a transformer model in PyTorch with an English-to-French language translation task
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Practical Guide to Hyperparameters Optimization for Deep Learning Models
Learn techniques for identifying the best hyperparameters for your deep learning projects, including code samples that you can use to get started on FloydHub.
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Asking questions to images with deep learning: a visual-question-answering tutorial
In this deep learning tutorial, we'll build Visual Question Answering (VQA) model that allows people to ask open-ended, common sense questions about the visual world.
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Ten Techniques Learned From fast.ai
Right now, Jeremy Howard – the co-founder of fast.ai – currently holds the 105th highest score for the plant seedling classification contest on Kaggle, but he's dropping fast. Why? His...
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Teaching My Robot With TensorFlow
If you're like me, then you'd do pretty much anything to have your own R2-D2 or BB-8 robotic buddy. Just imagine the adorable adventures you'd have together! I'm delighted to...
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Turning Design Mockups Into Code With Deep Learning
Within three years deep learning will change front-end development. It will increase prototyping speed and lower the barrier for building software.
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Benchmarking FloydHub instances
This post compares all the CPU and GPU instances offered by FloydHub, so that you can choose the right instance type for your training job. Benchmark For our benchmark we...
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Checkpointing Tutorial for TensorFlow, Keras, and PyTorch
Ready to build, train, and deploy AI? Get started with FloydHub's collaborative AI platform for free Try FloydHub for free [https://www.floydhub.com/?utm_source=blog&utm_medium=banner-checkpointing-s...
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Generating Six-Pack Abs With TensorFlow pix2pix
Visualizing results can be a powerful form of motivation and preparation. However, in the fitness domain, it can often be difficult to clearly see this future outcome. Can we use...
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Colorizing B&W Photos with Neural Networks
I’ll show you how to build your own colorization neural net in three steps. The first section breaks down the core logic. We’ll build a bare-bones 40-line neural network as...
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Building Your First ConvNet
Learn how to build your first ConvNet (Convolutional Neural Networks) to classify dogs and cats.
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Getting Started with Deep Learning on FloydHub
This post is aimed at helping new users (especially the ones who are starting out & cannot afford Andrej Karpathy’s rig [https://twitter.com/karpathy/status/648256662554341377]) setup an on-the-go dee...
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Coding the History of Deep Learning
There are six snippets of code that made deep learning what it is today. This article covers the inventors and the background to their breakthroughs. Each story includes simple code...
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Should I buy my own GPUs for Deep Learning?
Deep learning algorithms involve huge amounts of matrix multiplications and other operations which can be massively parallelized. GPUs usually consist of thousands of cores which can speed up these op......
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My First Weekend of Deep Learning
The current wave of deep learning took off five years ago. Exponential progress in computing power followed by a few success stories created the hype. It’s the technology that drives...
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