This paper introduces several deep learning algorithms: Artificial Neural Network (NN), FM-Deep Learning, Convolutional NN and Recurrent NN, and expounds their theory, development history and applications in disease prediction; we analyze the defects in the current disease prediction field and give ...
This review explores three foundational deep learning architectures—AlexNet, VGG16, and GoogleNet—that have significantly advanced the field of computer vision.
In the next section, I’ll briefly survey the different kinds of machine learning and the different kinds of machine learning models. Then I’ll discuss 14 of the most commonly used machine learning and deep learning algorithms, and explain how those algorithms relate to the creation of models...
In the next section, I’ll briefly survey the different kinds of machine learning and the different kinds of machine learning models. Then I’ll discuss 14 of the most commonly used machine learning and deep learning algorithms, and explain how those algorithms relate to the creation of models...
Asynchronous algorithms below are removed in current version but can be found in v0.1. Async Advantage Actor Critic (A3C) Async One-Step Q-Learning Async One-Step Sarsa Async N-Step Q-Learning Continuous A3C Distributed Deep Deterministic Policy Gradient (Distributed DDPG, aka D3PG) Parallelized...
Torch is written in Lua, and used at NYU, Facebook AI lab and Google DeepMind. It claims to provide a MATLAB-like environment for machine learning algorithms. Why did they choose Lua/LuaJIT instead of the more popular Python? They said inTorch7 paperthat “Lua is easily to be integrated...
Reward normalizationoradvantage normalizationin batch can have great improvements on performance (learning efficiency, stability) sometimes, although theoretically on-policy algorithms like PPO should not apply data normalization during training due to distribution shift. For an in-depth look at this problem...
A deep learning application is more than just the network. You also need to take the pre- and postprocessing logic of the application into consideration. Some of the tools and techniques we discussed have been used for quantizing such algorithms for a couple of decades ...
DL4J is a JVM-based, industry-focused, commercially supported, distributed deep-learning framework intended to solve problems involving massive amounts of data in a reasonable amount of time DeepLearning4J is a slick platform and it offers a suite of state of the art deep learning algorithms, ...
Machine learning libraries offer developers and data scientists resources to build, deploy and train models that incorporate data sets to generate predictions and take specific actions. Models employdeep learningalgorithms for image recognition, language processing, computer vision and data analytics. These...