Google Colab is one of the most famous cloud services for seasoned data scientists, researchers, and software engineers. While Google Colab seems easy to start, some things are difficult to use. There are several benefits of using Colab over using your own local machines. To create a new Notebook on Colab, open https://colab.research.google.com/, Here you can click on NEW NOTEBOOK to start a new notebook and start running your code in it.

I haven't seen many posts in the wild on how end-to-end machine learning works, so this post covers the process of creating an end-to-end proof-of-concept (POC) machine learning product, Venti, which is a Medium-like site that generates VC thinkpieces. All the code for serving the model is here, and here for generating inferences. The app, Venti, is live here. A few months ago, Emmanuel Amiesen sent me a copy of his book, Building Machine Learning Applications. Just got this in the mail from @mlpowered and looking forward to digging in (maybe when I'm getting 5 hours of continuous sleep.) It looks great so far and what I like about it is that explains common ML prod jargon like "inference" and "data leakage." I read it with a lot of interest because there is not a lot of "official" literature out in the field about what machine learning orchestration and pipelines should look like.

Udemy Coupon - Gain hands-on skills using 8 AI techniques (no coding!), and position yourself for this digital age (Level 1) Created by AI Ascent LLC. Course Instructor: Ivy Munoko, ACCA, CISA English [Auto-generated] Students also bought Artificial Intelligence A-Z: Learn How To Build An AI Deep Learning and Computer Vision A-Z: OpenCV, SSD & GANs Artificial Intelligence 2018: Build the Most Powerful AI Artificial Intelligence: Reinforcement Learning in Python Artificial Intelligence II - Neural Networks in Java Preview this Course GET COUPON CODE Description This course will provide students and professionals a 360 degree view of the current Artificial Intelligence techniques used in Business, Finance, Accounting and Auditing. Through a mix of lectures, hands-on exercises (code free!), case studies, reflection exercises and knowledge-check quizzes, this course will provide a quick and deep overview of Artificial Intelligence (AI) in business. In this digital age, all careers have a computing element as digitization and automation comes to the forefront of business processes. Those who complete this course will be able to reposition themselves to initiate and implement AI innovation within their teams, and confidently walk into this AI age.

The model uncertainty obtained by variational Bayesian inference with Monte Carlo dropout is prone to miscalibration. In this paper, different logit scaling methods are extended to dropout variational inference to recalibrate model uncertainty. Expected uncertainty calibration error (UCE) is presented as a metric to measure miscalibration. The effectiveness of recalibration is evaluated on CIFAR-10/100 and SVHN for recent CNN architectures. Experimental results show that logit scaling considerably reduce miscalibration by means of UCE. Well-calibrated uncertainty enables reliable rejection of uncertain predictions and robust detection of out-of-distribution data.

System identification is a key step for model-based control, estimator design, and output prediction. This work considers the offline identification of partially observed nonlinear systems. We empirically show that the certainty-equivalent approximation to expectation-maximization can be a reliable and scalable approach for high-dimensional deterministic systems, which are common in robotics. We formulate certainty-equivalent expectation-maximization as block coordinate-ascent, and provide an efficient implementation. The algorithm is tested on a simulated system of coupled Lorenz attractors, demonstrating its ability to identify high-dimensional systems that can be intractable for particle-based approaches. Our approach is also used to identify the dynamics of an aerobatic helicopter. By augmenting the state with unobserved fluid states, a model is learned that predicts the acceleration of the helicopter better than state-of-the-art approaches. The codebase for this work is available at https://github.com/sisl/CEEM.

Hierarchical DQN (h-DQN) is a two-level architecture of feedforward neural networks where the meta level selects goals and the lower level takes actions to achieve the goals. We show tasks that cannot be solved by h-DQN, exemplifying the limitation of this type of hierarchical framework (HF). We describe the recurrent hierarchical framework (RHF), generalizing architectures that use a recurrent neural network at the meta level. We analyze the expressiveness of HF and RHF using context-sensitive grammars. We show that RHF is more expressive than HF. We perform experiments comparing an implementation of RHF with two HF baselines; the results corroborate our theoretical findings.

We consider the problem of reinforcement learning when provided with a baseline control policy and a set of constraints that the controlled system must satisfy. The baseline policy might arise from a heuristic, a prior application, a teacher or demonstrator data. The constraints might encode safety, fairness or some application-specific requirements. We want to efficiently use reinforcement learning to adapt the baseline policy to improve performance and satisfy the given constraints when it is applied to the new system. The key challenge is to effectively use the baseline policy (which need not satisfy the current constraints) to aid the learning of a constraint-satisfying policy in the new application. We propose an iterative algorithm for solving this problem. Each iteration is composed of three-steps. The first step performs a policy update to increase the expected reward, the second step performs a projection to minimize the distance between the current policy and the baseline policy, and the last step performs a projection onto the set of policies that satisfy the constraints. This procedure allows the learning process to leverage the baseline policy to achieve faster learning while improving reward performance and satisfying the constraints imposed on the current problem. We analyze the convergence of the proposed algorithm and provide a finite-sample guarantee. Empirical results demonstrate that the algorithm can achieve superior performance, with 10 times fewer constraint violations and around 40% higher reward compared to state-of-the-art methods.

Sarcasm is a form of communication in whichthe person states opposite of what he actually means. It is ambiguous in nature. In this paper, we propose using machine learning techniques with BERT and GloVe embeddings to detect sarcasm in tweets. The dataset is preprocessed before extracting the embeddings. The proposed model also uses the context in which the user is reacting to along with his actual response.

Variational Autoencoders (VAE) and their variants have been widely used in a variety of applications, such as dialog generation, image generation and disentangled representation learning. However, the existing VAE models have some limitations in different applications. For example, a VAE easily suffers from KL vanishing in language modeling and low reconstruction quality for disentangling. To address these issues, we propose a novel controllable variational autoencoder framework, ControlVAE, that combines a controller, inspired by automatic control theory, with the basic VAE to improve the performance of resulting generative models. Specifically, we design a new non-linear PI controller, a variant of the proportional-integral-derivative (PID) control, to automatically tune the hyperparameter (weight) added in the VAE objective using the output KL-divergence as feedback during model training. The framework is evaluated using three applications; namely, language modeling, disentangled representation learning, and image generation. The results show that ControlVAE can achieve better disentangling and reconstruction quality than the existing methods. For language modelling, it not only averts the KL-vanishing, but also improves the diversity of generated text. Finally, we also demonstrate that ControlVAE improves the reconstruction quality of generated images compared to the original VAE.

Iterative neural networks (INN) are rapidly gaining attention for solving inverse problems in imaging, image processing, and computer vision. INNs combine regression NNs and an iterative model-based image reconstruction (MBIR) algorithm, often leading to both good generalization capability and outperforming reconstruction quality over existing MBIR optimization models. This paper proposes the first fast and convergent INN architecture, Momentum-Net, by generalizing a block-wise MBIR algorithm that uses momentum and majorizers with regression NNs. For fast MBIR, Momentum-Net uses momentum terms in extrapolation modules, and noniterative MBIR modules at each iteration by using majorizers, where each iteration of Momentum-Net consists of three core modules: image refining, extrapolation, and MBIR. Momentum-Net guarantees convergence to a fixed-point for general differentiable (non)convex MBIR functions (or data-fit terms) and convex feasible sets, under two asymptomatic conditions. To consider data-fit variations across training and testing samples, we also propose a regularization parameter selection scheme based on the "spectral spread" of majorization matrices. Numerical experiments for light-field photography using a focal stack and sparse-view computational tomography demonstrate that, given identical regression NN architectures, Momentum-Net significantly improves MBIR speed and accuracy over several existing INNs; it significantly improves reconstruction quality compared to a state-of-the-art MBIR method in each application.