Tag Archive Deep Learning

Deploying deep learning models on AWS lambda

AWS Lambda is a serverless computing service provided by Amazon Web Services. The definition of serverless architecture is — it is a stateless compute container designed for event-driven solutions just like microservice architecture where monolithic applications are broken into simple smaller services which are easy to code, manage and scale.

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Generating Adversarial Samples in Keras (Tutorial)

As deep learning technologies power increasingly more services, associated security risks become more critical to address. Adversarial Machine Learning is a branch of machine learning that exploits the mathematics underlying deep learning systems in order to evade, explore, and/or poison machine learning models. Evasion attacks are the most common adversarial attack method due to their ease of implementation and potential for being highly disruptive. During an evasion attack, the adversary tries to evade a fully trained model by engineering samples to be misclassified by the model. This attack does not assume any influence over the training data.

Evasion attacks have been demonstrated in the context of autonomous vehicles where the adversary manipulates traffic signs to confuse the learning model. Research suggests that deep neural networks are susceptible to adversarial based evasion attacks due to their high degree of non-linearity as well as insufficient model averaging and regularization.

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Scaling Reward Values for Improved Deep Reinforcement Learning

Deep Reinforcement Learning involves using a neural network as a universal function approximator to learn a value function that maps state-action pairs to their expected future reward given a particular reward function. This can be done many different ways. For example, a Monte Carlo based algorithm will observe total rewards following state-action pairs from a complete episode to make build training data for the neural network. Alternatively, a Temporal Difference approach would use incremental rewards from single time-steps and bootstrap off of predicted future rewards from the latest version of the value function model. However, no matter what approach is taken, it is important that the neural network is being efficiently fitted to the data in order to optimize the learning algorithm. There are many factors that determine a neural networks ability to fit to training data. In this post we will examine how scaling our outputs can affect our rate of convergence.

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Training Recurrent Neural Networks on Long Sequence

Deep Recurrent Neural Networks (RNN) are a type of Artificial Neural Network that takes the networks previous hidden state as part of its input, effectively allowing the network to have a memory. This makes RNNs useful for modeling sequential or time-series data such as stock prices. However, training RNNs on sequences greater than a few hundred time steps can be difficult. In this post, we will explore three tools that can allow for more efficient training of RNN models with long sequences: Optimizers, Gradient Clipping, and Batch Sequence Length.

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A deep learning solution to detect and mask human faces in videos

The objective of the project is to develop a smart deep-learning based solution, which possesses the capability to detect human faces in images / videos and mask them. The solution discussed in this article comprises of a set of open source tools and combining them together to form a pipeline, which processes a given video and outputs another video with faces masked in them.

Read the rest of the article at Mindboard’s Medium channel.