Active vs Passive Learning
Small amount of Labeled with large amount of Unlabeled Data
Reward based Learning
Advancing Semi-supervised Learning with Unsupervised Data Augmentation
Success in deep learning has largely been enabled by key factors such as algorithmic advancements, parallel processing hardware ( GPU / TPU), and the availability of large-scale labeled datasets, like ImageNet. However, when labeled data is scarce, it can be difficult to train neural networks to perform well.
Advancements in Semi-Supervised Learning with Unsupervised Data Augmentation
I am in this article attempting to understand the progress made within Semi-Supervised Learning (SSL) with Unsupervised Data Augmentation (UDA). Firstly by going through the different well-known machine learning techniques. Secondly by going through the recent blog post accompanied by an article on Google AI on SSL with UDA.
4 Machine Learning Approaches that Every Data Scientist Should Know
With the constant advancements in artificial intelligence, the field has become too big to specialize in all together. There are countless problems that we can solve with countless methods. Knowledge of an experienced AI researcher specialized in one field may mostly be useless for another field.
Applications of Reinforcement Learning in Real World
While Convolution Neural Network (CNN) and Recurrent Neural Network (RNN) are becoming more important for businesses due to their applications in Computer Vision (CV) and Natural Language Processing (NLP), Reinforcement Learning (RL) as a framework for computational neuroscience to model decision making process seems to be undervalued.