H2O has announced several new advancements including improved security features, semi-supervised deep learning, and capabilities for large Java models. New features were also added for tree algorithms like gradient boosted machines to improve accuracy, and H2O will now support convolutional and recurrent neural networks for tasks like image recognition and time series analysis. Deep Water was announced as H2O's next generation deep learning platform to bring enterprise deep learning capabilities.

1. H2O Advancements
Arno Candel, PhD
Chief Architect, Physicist & Hacker, H2O.ai
@ArnoCandel
July 18 2016

2. New Enterprise Features
Auth & Security LDAP/Kerberos/HTTPS/Encryption for max. compliance, IPv6
Semi-Supervised Pre-train Deep Learning model on unlabeled data, then fine-tune
Large POJOs Productionize large Java models (multi-GB source code)
Hyper Parameter
Tuning
Automatically tunes model parameters for the desired metric,
with convergence-based early stopping for models and search
Steam Platform for Data Products - next-gen product
Sparkling Water 2.0 The killer app for the latest version of Apache Spark
Advanced Munging Bringing R’s data.table to H2O - scalable, fast and distributed
Mission-Critical Capabilities for Enterprise Production Use

3. New Features for H2O Tree Algorithms: GBM + DRF
Highest Accuracy for Smarter Applications
Optimal missing
value handling
Missing data has meaning, is rarely missing at random
Optimal splits are found taking missing values into account
Quantile-based
histograms
Finds optimal split points for data with outliers,
e.g. -99999,0,1,2,3,4,5
New Algorithm ExtraTreesClassifier (pick best among random split points)
Robust Regression:
Huber loss for GBM
Higher accuracy for models on data with outliers
quadratic loss for inliers, linear loss for outliers
More tuning
parameters for
GBM
col_sample_rate_change_per_level - H2O exclusive
learn_rate_annealing - faster training
min_split_improvement - avoids overfitting
max_abs_leafnode_pred - avoids overfitting
sample_rate_per_class - for imbalanced datasets

4. Integration with existing
GPU backends
Leverage open-source tools and research for
TensorFlow, Caffe, mxnet, Theano, etc.
Scalability and Ease of
Use/Deployment of H2O
Distributed training, real-time model inspection
Flow, R, Python, Spark/Scala, Java, REST, POJO, Steam
Convolutional Neural
Networks
Image, video, speech recognition, etc.
Recurrent Neural
Networks
Sequences, time series, etc.
NLP: natural language processing
Hybrid Neural Networks
Architectures
Speech to text translation, image captioning, scene
parsing, etc.
Deep Water: Next-Gen Deep Learning in H2O
Enterprise Deep Learning for Business Transformation
Much more about Deep Learning tomorrow afternoon!