Introducing tf-explain, Interpretability for TensorFlow 2.0
A Tensorflow 2.0 library for deep learning model interpretability.
Few-Shot Image Classification with Meta-Learning
Here is how you can teach your model to learn quickly from a few examples.
3 Reasons Why We Are Far From Achieving Artificial General Intelligence
How far we are from achieving Artificial General Intelligence? We answer this through the study of three limitations of current machine learning.
Migrating Monolithic Apps to Serverless Architecture on AWS
If you wish to keep fast-paced development without delaying the migration, you can use the architectural pattern described in this article.
TensorFlow 2.0 Tutorial : Optimizing Training Time Performance
Tricks to improve TensorFlow training time with tf.data pipeline optimizations, mixed precision training and multi-GPU strategies
Hands on hyperparameter tuning with Keras Tuner
Or how hyperparameter tuning with Keras Tuner can boost your object classification network's accuracy by 10%
Deep Learning Memory Usage and Pytorch Optimization Tricks
Understanding memory usage in deep learning models training
Optimize Response Time of your Machine Learning API in Production
This article demonstrates how building a smarter API serving Deep Learning models minimizes the response time.
Determine Your Network Hyperparameters With Bayesian Optimization
Why and how Bayesian Optimization can be used for hyperparameters tuning
About Convolutional Layer and Convolution Kernel
A story of Convnet in machine learning from the perspective of kernel sizes.
Edge Detection in Opencv 4.0, A 15 Minutes Tutorial
This tutorial will teach you, with examples, two OpenCV techniques in python to deal with edge detection.