How to Perform Face Recognition With VGGFace2 in Keras
In this article, you will learn about the VGGFace and VGGFace2 models for face recognition and how to install the keras_vggface library to make use of these models in Python with Keras; how to develop a face identification system to predict the name of celebrities in given photographs; how to develop a face verification system to confirm the identity of a person given a photograph of their face.
http://bit.ly/32oOOlX
In this article, you will learn about the VGGFace and VGGFace2 models for face recognition and how to install the keras_vggface library to make use of these models in Python with Keras; how to develop a face identification system to predict the name of celebrities in given photographs; how to develop a face verification system to confirm the identity of a person given a photograph of their face.
http://bit.ly/32oOOlX
Machine Learning Mastery
How to Perform Face Recognition With VGGFace2 in Keras - Machine Learning Mastery
Face recognition is a computer vision task of identifying and verifying a person based on a photograph of their face.
Recently, deep learning convolutional neural networks have surpassed classical methods and are achieving state-of-the-art results on standard…
Recently, deep learning convolutional neural networks have surpassed classical methods and are achieving state-of-the-art results on standard…
Tips for Training Likelihood Models
This is a tutorial on common practices in training generative models that optimize likelihood directly, such as autoregressive models and normalizing flows.
http://bit.ly/2NQEmAq
This is a tutorial on common practices in training generative models that optimize likelihood directly, such as autoregressive models and normalizing flows.
http://bit.ly/2NQEmAq
Evjang
Tips for Training Likelihood Models
This is a tutorial on common practices in training generative models that optimize likelihood directly, such as autoregressive models and ...
Building Lyft’s Marketing Automation Platform
This post on Lyft's Engineering blog walks-through the machine learning system that enables Lyft's marketing at scale. It's fairly high-level but it's a good read and includes worthwhile details along the way.
https://lft.to/2NWlCiU
This post on Lyft's Engineering blog walks-through the machine learning system that enables Lyft's marketing at scale. It's fairly high-level but it's a good read and includes worthwhile details along the way.
https://lft.to/2NWlCiU
Medium
Building Lyft’s Marketing Automation Platform
Machine learning based marketing automation to improve cost and volume efficiency in an ever-changing marketplace.
👍1
NumPy implementations of various ML models
Repository of mostly pure NumPy implementations of machine learning models. These are bare-bones implementations and aren't optimized to be efficient. They're optimized to be understanding how they work.
http://bit.ly/2lbReDe
Repository of mostly pure NumPy implementations of machine learning models. These are bare-bones implementations and aren't optimized to be efficient. They're optimized to be understanding how they work.
http://bit.ly/2lbReDe
GitHub
GitHub - ddbourgin/numpy-ml: Machine learning, in numpy
Machine learning, in numpy. Contribute to ddbourgin/numpy-ml development by creating an account on GitHub.
Let's meet at AIUkraine 2019 in September!
AI Ukraine is an annual and most professional industry conference powered by AltexSoft.
The conference will include three stages:
- Data Science & Machine Learning
- BigData & Analytics
- Business & Startups
Special promo code for 7% discount for our subscribers - DSDigest-AI2019
Registration and more information: http://bit.ly/2O5DOXz
AI Ukraine is an annual and most professional industry conference powered by AltexSoft.
The conference will include three stages:
- Data Science & Machine Learning
- BigData & Analytics
- Business & Startups
Special promo code for 7% discount for our subscribers - DSDigest-AI2019
Registration and more information: http://bit.ly/2O5DOXz
Top 25 pandas tricks
25 tricks that will help you to work faster and write better pandas code.
http://bit.ly/2O65qMn
25 tricks that will help you to work faster and write better pandas code.
http://bit.ly/2O65qMn
nbviewer.jupyter.org
Notebook on nbviewer
Check out this Jupyter notebook!
Anomaly Detection for Dummies
This tutorial explores unsupervised anomaly detection for univariate and multivariate data. Covers a variety of detection strategies with python code snippets and screenshots.
Jupyter notebook: http://bit.ly/2O2PFp9
http://bit.ly/2O7unaa
This tutorial explores unsupervised anomaly detection for univariate and multivariate data. Covers a variety of detection strategies with python code snippets and screenshots.
Jupyter notebook: http://bit.ly/2O2PFp9
http://bit.ly/2O7unaa
GitHub
susanli2016/Machine-Learning-with-Python
Python code for common Machine Learning Algorithms - susanli2016/Machine-Learning-with-Python
TRFL
TRFL is a library built on top of TensorFlow that exposes several useful building blocks for implementing Reinforcement Learning agents, which was developed by the Research Engineering team at DeepMind.
http://bit.ly/2O8nfKu
TRFL is a library built on top of TensorFlow that exposes several useful building blocks for implementing Reinforcement Learning agents, which was developed by the Research Engineering team at DeepMind.
http://bit.ly/2O8nfKu
GitHub
GitHub - deepmind/trfl: TensorFlow Reinforcement Learning
TensorFlow Reinforcement Learning. Contribute to deepmind/trfl development by creating an account on GitHub.
Top 10 Statistics Mistakes Made by Data Scientists
Data scientists with no background in statistics are hardly a rarity today. Here are 10 mistakes that data scientists make using statistics, with examples and solutions included.
http://bit.ly/2Ockqbz
Data scientists with no background in statistics are hardly a rarity today. Here are 10 mistakes that data scientists make using statistics, with examples and solutions included.
http://bit.ly/2Ockqbz
Medium
Top 10 Statistics Mistakes Made by Data Scientists
Avoid those mistakes, some of which could derail your career. Especially useful for data science coders without a statistics background
Building Data Pipelines With Kafka
To get value from this post, we recommend that you have Kafka installed. The article is for beginner engineers who are going to build their first data pipeline. If you are a seasoned pro, you might as well scan the article to freshen up your knowledge, too.
http://bit.ly/2OfsGaF
To get value from this post, we recommend that you have Kafka installed. The article is for beginner engineers who are going to build their first data pipeline. If you are a seasoned pro, you might as well scan the article to freshen up your knowledge, too.
http://bit.ly/2OfsGaF
Medium
Building Data Pipelines With Kafka
Goal: Today our goal is to write a data pipeline using Kafka.
AI Behind LinkedIn Recruiter Search and Recommendation Systems
LinkedIn Recruiter is a LinkedIn’s key tool to help recruiters and hiring managers source suitable talent and identify “talent pools”. This blog post highlights a few unique challenges in information retrieval, system and recommender modeling, system design, architecture, and product deployment.
http://bit.ly/2Mb6VX2
LinkedIn Recruiter is a LinkedIn’s key tool to help recruiters and hiring managers source suitable talent and identify “talent pools”. This blog post highlights a few unique challenges in information retrieval, system and recommender modeling, system design, architecture, and product deployment.
http://bit.ly/2Mb6VX2
Linkedin
The AI Behind LinkedIn Recruiter search and recommendation systems
Co-authors: Qi Guo, Sahin Cem Geyik, Cagri Ozcaglar, Ketan Thakkar, Nadeem Anjum, and Krishnaram Kenthapadi
Learning World Graphs to Accelerate Hierarchical Reinforcement Learning
An autonomous agent often counters various tasks within a single complex environment. Our two-stage framework proposes to first build a simple directed weighted graph abstraction over the world in an unsupervised task-agnostic manner and then to accelerate the hierarchical reinforcement learning of a diversity of downstream tasks.
Paper: http://bit.ly/2MfmWeq
http://bit.ly/2MfmXz0
An autonomous agent often counters various tasks within a single complex environment. Our two-stage framework proposes to first build a simple directed weighted graph abstraction over the world in an unsupervised task-agnostic manner and then to accelerate the hierarchical reinforcement learning of a diversity of downstream tasks.
Paper: http://bit.ly/2MfmWeq
http://bit.ly/2MfmXz0
MIT Deep Learning Basics: Introduction and Overview with TensorFlow
This blog post provides an overview of deep learning in 7 architectural paradigms with links to TensorFlow tutorials for each. It accompanies the following lecture on Deep Learning Basics as part of MIT course 6.S094.
http://bit.ly/2Mnf1vs
This blog post provides an overview of deep learning in 7 architectural paradigms with links to TensorFlow tutorials for each. It accompanies the following lecture on Deep Learning Basics as part of MIT course 6.S094.
http://bit.ly/2Mnf1vs
Medium
MIT Deep Learning Basics: Introduction and Overview with TensorFlow
As part of the MIT Deep Learning series of lectures and GitHub tutorials, we are covering the basics of using neural networks to solve…
Personalized Recommendations for Experiences Using Deep Learning
In this blog post, you will learn how TripAdvisor’s newly-developed ‘Recommended For You’ (RFY) model generates personalized recommendations on their website using users’ browsing history and deep learning.
http://bit.ly/2MgiDPU
In this blog post, you will learn how TripAdvisor’s newly-developed ‘Recommended For You’ (RFY) model generates personalized recommendations on their website using users’ browsing history and deep learning.
http://bit.ly/2MgiDPU
Introducing Dagster
Dagster is an open-source Python library for building systems like ETL processes and ML pipelines.
GitHub: http://bit.ly/2MkyYTP
http://bit.ly/2Mkw4yh
Dagster is an open-source Python library for building systems like ETL processes and ML pipelines.
GitHub: http://bit.ly/2MkyYTP
http://bit.ly/2Mkw4yh
GitHub
GitHub - dagster-io/dagster: An orchestration platform for the development, production, and observation of data assets.
An orchestration platform for the development, production, and observation of data assets. - GitHub - dagster-io/dagster: An orchestration platform for the development, production, and observation ...
Python Machine Learning Tutorial: Predicting Airbnb Prices
This tutorial will introduce you to the fundamental concepts of machine learning. As you follow along, you’ll build your very first model from scratch to make predictions while developing a solid understanding of how exactly how your model works.
http://bit.ly/2Mn39JZ
This tutorial will introduce you to the fundamental concepts of machine learning. As you follow along, you’ll build your very first model from scratch to make predictions while developing a solid understanding of how exactly how your model works.
http://bit.ly/2Mn39JZ
Dataquest
Python Machine Learning Tutorial: Predicting Airbnb Prices
Learn about machine learning in Python and build your very first ML model from scratch to predict Airbnb prices using k-nearest neighbors.
Using DVC to create an efficient version control system for data projects
In this article, you will learn what DVC is and how to use it to track the project’s data. DVC simplifies Data projects by using stages and pipelines, which increases productivity and improves collaboration.
http://bit.ly/2MvgLmN
In this article, you will learn what DVC is and how to use it to track the project’s data. DVC simplifies Data projects by using stages and pipelines, which increases productivity and improves collaboration.
http://bit.ly/2MvgLmN
Medium
Using DVC to create an efficient version control system for data projects
At first we were looking for a tool to help us dealing with production data files such as trained machine learning algorithms. In the…
ICML 2019 Videos
Video and slides from the International Conference on Machine Learning
http://bit.ly/2ys9uvG
Video and slides from the International Conference on Machine Learning
http://bit.ly/2ys9uvG
icml.cc
ICML 2019 Videos
ICML Website
How to Develop an Information Maximizing GAN (InfoGAN) in Keras
In this tutorial, you will learn how to develop and train an InfoGAN model from scratch and use the control variables to control which digit is generated by the model.
http://bit.ly/2MxWpJB
#ML #AI #DataScience
In this tutorial, you will learn how to develop and train an InfoGAN model from scratch and use the control variables to control which digit is generated by the model.
http://bit.ly/2MxWpJB
#ML #AI #DataScience
Machine Learning Mastery
How to Develop an Information Maximizing GAN (InfoGAN) in Keras - Machine Learning Mastery
The Generative Adversarial Network, or GAN, is an architecture for training deep convolutional models for generating synthetic images.
Although remarkably effective, the default GAN provides no control over the types of images that are generated. The Information…
Although remarkably effective, the default GAN provides no control over the types of images that are generated. The Information…
Neural Architecture Search at CVPR 2019
In this article, you will learn about neural architecture search (NAS) and how it was presented at CVPR 2019 in Long Beach.
http://bit.ly/333nSbH
In this article, you will learn about neural architecture search (NAS) and how it was presented at CVPR 2019 in Long Beach.
http://bit.ly/333nSbH
drsleep.github.io
Neural Architecture Search at CVPR 2019
PyTorch Transformers for state-of-the-art NLP
If you're doing anything with NLP, this is a great new open-source tool to know about. This library from Hugging Face contains 27 pre-trained models to conduct state-of-the-art NLP/NLU tasks, including BERT, GPT-2, XLNet, etc. It's a unified API too, which makes it easy to use and experiment with the latest techniques.
http://bit.ly/337Mgch
If you're doing anything with NLP, this is a great new open-source tool to know about. This library from Hugging Face contains 27 pre-trained models to conduct state-of-the-art NLP/NLU tasks, including BERT, GPT-2, XLNet, etc. It's a unified API too, which makes it easy to use and experiment with the latest techniques.
http://bit.ly/337Mgch
Medium
PyTorch Transformers for state-of-the-art NLP
Hugging Face open sources a new library that contains up to 27 pretrained models to conduct state-of-the-art NLP/NLU tasks.