In the chapters that follow, we present real-world code examples of most of these networks and cover considerations of training and tuning for different kinds of neural networks. In Chapter 5 we see how these concepts come together in API examples in which we see the DL4J deep learning library in action. Before we move on to some more examples, let's discuss a few topics that come up frequently in the context of deep learning. The debate around deep learning making other modeling algorithms obsolete comes up many times on internet message boards. The answer today is "no" because for many simpler machine learning applications, we see far simpler algorithms work just fine for the required model accuracy. Models like logistic regression are also easier to work with, so we need to gauge the level of effort against the required accuracy in the domain when making this decision.

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We print out the PyTorch version we are using. We see that we are using PyTorch version 0.2.0_4. The first thing we're going to do is we're going to define a PyTorch tensor and we're going to initialize it using the random functionality which pulls a random number between 0 to 1. Then we're going to multiply it by 100 so that we have a number between 0 to 100 and we cast it to an Int PyTorch tensor just so it's cleaner when we go to look at the numbers. Then we can see that it is a PyTorch IntTensor of size 2x3x4. Cast this one to an Int as well.

When we hear about Convolutional Neural Network (CNNs), we typically think of Computer Vision. CNNs were responsible for major breakthroughs in Image Classification and are the core of most Computer Vision systems today, from Facebook's automated photo tagging to self-driving cars. More recently we've also started to apply CNNs to problems in Natural Language Processing and gotten some interesting results. In this post I'll try to summarize what CNNs are, and how they're used in NLP. The intuitions behind CNNs are somewhat easier to understand for the Computer Vision use case, so I'll start there, and then slowly move towards NLP. The for me easiest way to understand a convolution is by thinking of it as a sliding window function applied to a matrix. That's a mouthful, but it becomes quite clear looking at a visualization: Imagine that the matrix on the left represents an black and white image.

Amazon Sagemaker was launched by Amazon back in November 2017. It was built with the promise of simplifying machine learning on the cloud. The software was a response not only to the increasing importance of machine learning, but also the fact that there is a demand to perform machine learning in the cloud. Amazon Sagemaker is clearly a smart move by Amazon that will consolidate the dominance of AWS in the cloud market. Amazon Sagemaker is Amazon's premium cloud-based service which serves as a platform for machine learning developers and data scientists to build, train and deploy machine learning models on the cloud.

Part of what fuels societal angst about the use of increasingly sophisticated, quasi-autonomous algorithms collectively known as AI is the fear that machines will supplant people in an ever-growing number of jobs. While this is undoubtedly true and has been for every prior generation of new technology from the power loom to the backhoe, the more significant question is whether AI replaces or merely displaces people? Namely, does it lead to the unemployed or to the differently employed. Medicine is one of the most promising areas of AI development and as I discussed last week, in hospitals and clinics there's a good argument that deep learning (DL) will augment human expertise, not supersede it. Ground zero of the DL disruption in medicine is imaging, however as my column pointed out, experts are optimistic that AI will make for better radiologists, not fewer.

Restricted Boltzmann Machines (RBMs) are a class of generative neural network that are typically trained to maximize a log-likelihood objective function. We argue that likelihood-based training strategies may fail because the objective does not sufficiently penalize models that place a high probability in regions where the training data distribution has low probability. To overcome this problem, we introduce Boltzmann Encoded Adversarial Machines (BEAMs). A BEAM is an RBM trained against an adversary that uses the hidden layer activations of the RBM to discriminate between the training data and the probability distribution generated by the model. We present experiments demonstrating that BEAMs outperform RBMs and GANs on multiple benchmarks.

Parameters in deep neural networks which are trained on large-scale databases can generalize across multiple domains, which is referred as "transferability". Unfortunately, the transferability is usually defined as discrete states and it differs with domains and network architectures. Existing works usually heuristically apply parameter-sharing or fine-tuning, and there is no principled approach to learn a parameter transfer strategy. To address the gap, a parameter transfer unit (PTU) is proposed in this paper. The PTU learns a fine-grained nonlinear combination of activations from both the source and the target domain networks, and subsumes hand-crafted discrete transfer states. In the PTU, the transferability is controlled by two gates which are artificial neurons and can be learned from data. The PTU is a general and flexible module which can be used in both CNNs and RNNs. Experiments are conducted with various network architectures and multiple transfer domain pairs. Results demonstrate the effectiveness of the PTU as it outperforms heuristic parameter-sharing and fine-tuning in most settings.

Batch Normalization (BN) is capable of accelerating the training of deep models by centering and scaling activations within mini-batches. In this work, we propose Decorrelated Batch Normalization (DBN), which not just centers and scales activations but whitens them. We explore multiple whitening techniques, and find that PCA whitening causes a problem we call stochastic axis swapping, which is detrimental to learning. We show that ZCA whitening does not suffer from this problem, permitting successful learning. DBN retains the desirable qualities of BN and further improves BN's optimization efficiency and generalization ability. We design comprehensive experiments to show that DBN can improve the performance of BN on multilayer perceptrons and convolutional neural networks. Furthermore, we consistently improve the accuracy of residual networks on CIFAR-10, CIFAR-100, and ImageNet.

In this report, we present a new reinforcement learning (RL) benchmark based on the Sonic the Hedgehog (TM) video game franchise. This benchmark is intended to measure the performance of transfer learning and few-shot learning algorithms in the RL domain. We also present and evaluate some baseline algorithms on the new benchmark.