Colaboratory, or Colab for short, is a Google Research product, which allows developers to write and execute Python code through their browser. Google Colab is an excellent tool for deep learning tasks. It is a hosted Jupyter notebook that requires no setup and has an excellent free version, which gives free access to Google computing resources such as GPUs and TPUs. Since Google Colab is built on top of vanilla Jupyter Notebook, which is built on top of Python kernel, let's look at these technologies before diving into why we should and how we can use Google Colab. There are several tools used in Python interactive programming environments.

This tutorial contains complete code to fine-tune BERT to perform sentiment analysis on a dataset of plain-text IMDB movie reviews. In addition to training a model, you will learn how to preprocess text into an appropriate format. If you're new to working with the IMDB dataset, please see Basic text classification for more details. BERT and other Transformer encoder architectures have been wildly successful on a variety of tasks in NLP (natural language processing). They compute vector-space representations of natural language that are suitable for use in deep learning models.

Now we are moving into the world of'edge computing', in which data is processed close to its source, cutting out the need for it to be sent to the cloud. But computing isn't the only thing taking place on'the edge' – now, AI is being brought to the source of the data as well, allowing'Edge AI' to bring about new standards of speed and intelligence. So, what is Edge AI, what kinds of benefits will it offer, and how will it empower solutions going forward? Currently, the heavy computing capacity required to run deep learning models necessitates that the majority of AI processes be carried out in the cloud. However, running AI in the cloud has its disadvantages, including the fact that it requires an internet connection, and that performance can be impacted by bandwidth and latency limitations.

This is a TensorFlow implementation of the stereo matching algorithm described in the paper "Efficient Deep Learning for Stereo Matching". The code is tested using Tensorflow r1.4 under Ubuntu 14.04 with Python 2.7. The KITTI 2015 dataset has been used for training. This dataset consists of total of 200 scenes for training and of 200 scenes for testing. For more details, please check the KITTI website.

This essay provides a broad overview of the sub-field of machine learning interpretability. While not exhaustive, my goal is to review conceptual frameworks, existing research, and future directions. I follow the categorizations used in Lipton et al.'s Mythos of Model Interpretability, which I think is the best paper for understanding the different definitions of interpretability. We'll go over many ways to formalize what "interpretability" means. Broadly, interpretability focuses on the how. It's focused on getting some notion of an explanation for the decisions made by our models. Below, each section is operationalized by a concrete question we can ask of our machine learning model using a specific definition of interpretability. If you're new to all this, we'll first briefly explain why we might care about interpretability at all.

Mr Amini said: "It was very calibrated to the errors that the network makes, which we believe was one of the most important things in judging the quality of a new uncertainty estimator." The test revealed the network's ability to flag when users should not place full trust in its decisions. In such examples, "if this is a health care application, maybe we don't trust the diagnosis that the model is giving, and instead seek a second opinion," Amini added. Dr Raia Hadsell, a DeepMind artificial intelligence researcher not involved with the workDeep evidential describes regression as "a simple and elegant approach that advances the field of uncertainty estimation, which is important for robotics and other real-world control systems. She added: "This is done in a novel way that avoids some of the messy aspects of other approaches -- [for example] sampling or ensembles -- which makes it not only elegant but also computationally more efficient -- a winning combination."

Fast and accurate fire detection is significant to the sustainable development of human society and Earth ecology. The existence of objects with similar characteristics to fire increases the difficulty of vision-based fire detection. Improving the accuracy of fire detection by digging deeper visual features of fire always remains challenging. Recently, researchers from the Institute of Acoustics of the Chinese Academy of Sciences (IACAS) have proposed an efficient deep learning model for fast and accurate vision-based fire detection. The model is based on multiscale feature extraction, implicit deep supervision, and channel attention mechanism. The researchers utilized the real-time acquired image as the input of the model and normalized the image.

It's hard to believe it's been been over a year since I released my first course on Deep Learning with NLP (natural language processing). A lot of cool stuff has happened since then, and I've been deep in the trenches learning, researching, and accumulating the best and most useful ideas to bring them back to you. So what is this course all about, and how have things changed since then? In previous courses, you learned about some of the fundamental building blocks of Deep NLP. We looked at RNNs (recurrent neural networks), CNNs (convolutional neural networks), and word embedding algorithms such as word2vec and GloVe.

Predictive Physics has been historically based upon the development of mathematical models that describe the evolution of a system under certain external stimuli and constraints. The structure of such mathematical models relies on a set of hysical hypotheses that are assumed to be fulfilled by the system within a certain range of environmental conditions. A new perspective is now raising that uses physical knowledge to inform the data prediction capability of artificial neural networks. A particular extension of this data-driven approach is Physically-Guided Neural Networks with Internal Variables (PGNNIV): universal physical laws are used as constraints in the neural network, in such a way that some neuron values can be interpreted as internal state variables of the system. This endows the network with unraveling capacity, as well as better predictive properties such as faster convergence, fewer data needs and additional noise filtering. Besides, only observable data are used to train the network, and the internal state equations may be extracted as a result of the training processes, so there is no need to make explicit the particular structure of the internal state model. We extend this new methodology to continuum physical problems, showing again its predictive and explanatory capacities when only using measurable values in the training set. We show that the mathematical operators developed for image analysis in deep learning approaches can be used and extended to consider standard functional operators in continuum Physics, thus establishing a common framework for both. The methodology presented demonstrates its ability to discover the internal constitutive state equation for some problems, including heterogeneous and nonlinear features, while maintaining its predictive ability for the whole dataset coverage, with the cost of a single evaluation.

The newly emerged machine learning (e.g. deep learning) methods have become a strong driving force to revolutionize a wide range of industries, such as smart healthcare, financial technology, and surveillance systems. Meanwhile, privacy has emerged as a big concern in this machine learning-based artificial intelligence era. It is important to note that the problem of privacy preservation in the context of machine learning is quite different from that in traditional data privacy protection, as machine learning can act as both friend and foe. Currently, the work on the preservation of privacy and machine learning (ML) is still in an infancy stage, as most existing solutions only focus on privacy problems during the machine learning process. Therefore, a comprehensive study on the privacy preservation problems and machine learning is required. This paper surveys the state of the art in privacy issues and solutions for machine learning. The survey covers three categories of interactions between privacy and machine learning: (i) private machine learning, (ii) machine learning aided privacy protection, and (iii) machine learning-based privacy attack and corresponding protection schemes. The current research progress in each category is reviewed and the key challenges are identified. Finally, based on our in-depth analysis of the area of privacy and machine learning, we point out future research directions in this field.