Deep convolutional neural networks (CNNs) are powerful tools for a wide range of vision tasks, but the enormous amount of memory and compute resources required by CNNs poses a challenge in deploying them on constrained devices. Existing compression techniques show promising performance in reducing the size and computation complexity of CNNs for efficient inference, but there lacks a method to integrate them effectively. In this paper, we attend to the statistical properties of sparse CNNs and present focused quantization, a novel quantization strategy based on powers-of-two values, which exploits the weight distributions after fine-grained pruning. The proposed method dynamically discovers the most effective numerical representation for weights in layers with varying sparsities, to minimize the impact of quantization on the task accuracy. Multiplications in quantized CNNs can be replaced with much cheaper bit-shift operations for efficient inference. Coupled with lossless encoding, we build a compression pipeline that provides CNNs high compression ratios (CR) and minimal loss in accuracies. In ResNet-50, we achieve a $ 18.08 \times $ CR with only $ 0.24\% $ loss in top-5 accuracy, outperforming existing compression pipelines.

The existence of adversarial examples has been demonstrated for a quite large set of deep learning architectures [10, 11, 36]. An adversarial input is a carefully forged data instance that aims at driving the model into an incorrect or unexpected behaviour. Moreover, the adversarial setup requires that those instances must be as much as possible indistinguishable from genuine inputs. In the present work, motivated by the extensive studies carried out on adversarial inputs for discriminative models, we extend the adversarial context into the increasingly popular generative models field. In particular, we focused on the most promising class of architectures, called Generative Adversarial Networks (GANs) [9]. GANs implicitly perform generative modeling of a target data distribution by training a deep neural network architecture. This is composed by two neural networks, a generator and a discriminator that are trained simultaneously in a zero-sum game. In the end, the generator learns a deterministic mapping between a latent representation and an approximation of the target data distribution. What we show with the present work is that a pre-trained generator can be forced to reproduce an arbitrary output if fed by a suitable adversarial input.

We describe an efficient implementation of clause guidance in saturation-based automated theorem provers extending the ENIGMA approach. Unlike in the first ENIGMA implementation where fast linear classifier is trained and used together with manually engineered features, we have started to experiment with more sophisticated state-of-the-art machine learning methods such as gradient boosted trees and recursive neural networks. In particular the latter approach poses challenges in terms of efficiency of clause evaluation, however, we show that deep integration of the neural evaluation with the ATP data-structures can largely amortize this cost and lead to competitive real-time results. Both methods are evaluated on a large dataset of theorem proving problems and compared with the previous approaches. The resulting methods improve on the manually designed clause guidance, providing the first practically convincing application of gradient-boosted and neural clause guidance in saturation-style automated theorem provers.

Natural Language Processing (NLP) is a hot topic into Machine Learning field. This course is an advanced course of NLP using Deep Learning approach. Before starting this course please read the guidelines of the lesson 2 to have the best experience in this course. This course starts with the configuration and the installation of all resources needed including the installation of Tensor Flow CPU/GPU, Cuda and Keras. You will be able to use your GPU card if you have one, to accelate so fast the processes.

Deep Learning is a revolutionary field, but for it to work as intended, it requires data. The area related to these big datasets is known as Big Data, which stands for the abundance of digital data. Data is as important for Deep Learning algorithms as the architecture of the network itself, i.e., the software. Acquiring and cleaning the data is one of the most valuable aspects of the work. Without data, the neural networks cannot learn.

Uses Conda (installed with Miniconda). This Docker image is made to serve as a base for other images and projects for Machine Learning, Data Science, Deep Learning, etc. It does not try to include every possible package. On the contrary, it tries to be as slim as possible, but having the minimal common requirements (the difficult parts) for most projects. By being slim, apart from reducing the size, it can be kept current more easily, and it can be tailored for each project, being equally useful for development and production.

Google announced today that it has made energy produced by wind farms more viable using the artificial intelligence software of its London-based subsidiary DeepMind. By using DeepMind's machine learning algorithms to predict the wind output from the farms Google uses for its green energy initiatives, the company says it can now schedule set deliveries of energy output, which are more valuable to the grid than standard, non-time-based deliveries. According to Google, this software has improved the "value" of the wind energy these farms are providing by 20 percent over a baseline where no such time-based predictions are being performed. We don't know exactly what that value is in monetary terms or in terms of energy output. We also don't know where exactly this is being deployed, although Google works with wind farms largely in the Midwest, where some of its US data centers are located.

Shan Carter, a researcher at Google Brain, recently visited his daughter's second-grade class with an unusual payload: an array of psychedelic pictures, filled with indistinct shapes and warped pinwheels of color. He passed them around the class, and was delighted when the students quickly deemed one of the blobs a dog ear. A group of 7-year-olds had just deciphered the inner visions of a neural network. Carter is among the researchers trying to pierce the "black box" of deep learning. Neural networks have proven tremendously successful at tasks like identifying objects in images, but how they do so remains largely a mystery.

Google and online learning hub Udacity have launched a free course designed to make it simpler for software developers to grasp the fundamentals of machine learning. The "Intro to TensorFlow for Deep Learning" course is designed to be more accessible to developers than previous machine-learning courses offered by Udacity. "Our goal is to get you building state-of-the-art AI applications as fast as possible, without requiring a background in math," says Mat Leonard, head of the School of AI at Udacity. "If you can code, you can build AI with TensorFlow. You'll get hands-on experience using TensorFlow to implement state-of-the-art image classifiers and other deep learning models. You'll also learn how to deploy your models to various environments including browsers, phones, and the cloud."

Artificial intelligence (AI) stands out as a transformational technology of our digital age--and its practical application throughout the economy is growing apace. For this briefing, Notes from the AI frontier: Insights from hundreds of use cases (PDF–446KB), we mapped both traditional analytics and newer "deep learning" techniques and the problems they can solve to more than 400 specific use cases in companies and organizations. Drawing on McKinsey Global Institute research and the applied experience with AI of McKinsey Analytics, we assess both the practical applications and the economic potential of advanced AI techniques across industries and business functions. Our findings highlight the substantial potential of applying deep learning techniques to use cases across the economy, but we also see some continuing limitations and obstacles--along with future opportunities as the technologies continue their advance. Ultimately, the value of AI is not to be found in the models themselves, but in companies' abilities to harness them.