TensorFlow has been gaining immense popularity over the past few months, owing to its power and ease of use. This video aims to help you leverage the power of TensorFlow to perform image processing. Beginning with an introduction to image processing, the video will take you through TensorFlow's API-like graph tensor, which can be used for image classification. Starting off with basic 2D images, the video will gradually take you through recognizing more complex images, colors, shapes, and so on. Making use of the Python API, you will move on to classifying and training your model to identify more complex images such as face and expression detection, while you will also perform classification using regression.

To carry out their experiments, the team trained their model in TensorFlow, employing a public dataset of road signs. While the dataset of a few thousand training examples was relatively small, the results plainly show the potential vulnerabilities of deep learning artificial neural networks used in autonomous driving systems when real objects are modified. "Unlike prior work, […] here we focus on evasion attacks where attackers can only modify the testing data instead of training data (poisoning attack)," explained the researchers. "In evasion attacks, an attacker can only change existing physical road signs. Here we assume that an attacker gains access to the classifier after it has been trained ('white-box' access)."

Deep learning, a variation of machine learning (ML), represents the major driver toward artificial intelligence (AI). As deep learning delivers superior data fusion capabilities over other ML approaches, Gartner predicts that by 2019, deep learning will be a critical driver for best-in-class performance for demand, fraud and failure predictions. "Deep learning is here to stay and expands ML by allowing intermediate representations of the data," said Alexander Linden, research vice president at Gartner. "It ultimately solves complex, data-rich business problems. Deep learning can, for example, give promising results when interpreting medical images in order to diagnose cancer early. It can also help improve the sight of visually impaired people, control self-driving vehicles, or recognise and understand a specific person's speech."

Deep learning, a form of machine learning that mimics the way humans process information by using neural networks, is quickly becoming a go-to tool for businesses interested in artificial intelligence (AI) systems that improve efficiency and enable innovative new business models. In fact, 80 percent of data scientists will include deep learning as part of their AI toolkits by 2018, predicted Gartner. And by 2019, deep learning will be delivering demand, fraud and failure predictions with "best-in-class performance," said the analyst firm in a Sept. 20 announcement. "Deep learning is here to stay and expands ML [machine learning] by allowing intermediate representations of the data," said Alexander Linden, research vice president at Gartner, in prepared remarks. "It ultimately solves complex, data-rich business problems. Deep learning can, for example, give promising results when interpreting medical images in order to diagnose cancer early. It can also help improve the sight of visually impaired people, control self-driving vehicles, or recognize and understand a specific person's speech."

The prediction of crime occurrences [1–7] has received considerable attention on account of its prospective benefits. This predictive capability would notably contribute to effective police patrols. According to the 2014 Chicago crime record, there were a total of 274,064 incidents of crime in 2014 and an average of 750 cases per day in that city. The results of these crimes, including injuries and deaths, are very serious. Fundamental crime prevention requires the strengthening of patrols, which is costly in terms of financial and human resources.

Neural networks are "slow" for many reasons, including load/store latency, shuffling data in and out of the GPU pipeline, the limited width of the pipeline in the GPU (as mapped by the compiler), the unnecessary extra precision in most neural network calculations (lots of tiny numbers that make no difference to the outcome), the sparsity of input data (lots of 0s), and many many other factors. How can we make deep neural network training, testing, and predictions faster? One way is to write faster algorithms, like the relu activation function, which is much faster than tanh and sigmoid, and another is to write better compilers to map the neural network into the hardware. A third approach is what I want to tell you about today. Making better hardware, and by better I mean faster processing speed.

We've designed these tutorials so that you can traverse the curriculum in more than one way. This evolving creature is a collaborative effort (see contributors tab).

E-commerce websites such as Amazon, Alibaba, Flipkart, and Walmart sell billions of products. Machine learning (ML) algorithms involving products are often used to improve the customer experience and increase revenue, e.g., product similarity, recommendation, and price estimation. The products are required to be represented as features before training an ML algorithm. In this paper, we propose an approach called MRNet-Product2Vec for creating generic embeddings of products within an e-commerce ecosystem. We learn a dense and low-dimensional embedding where a diverse set of signals related to a product are explicitly injected into its representation. We train a Discriminative Multi-task Bidirectional Recurrent Neural Network (RNN), where the input is a product title fed through a Bidirectional RNN and at the output, product labels corresponding to fifteen different tasks are predicted. The task set includes several intrinsic characteristics about a product such as price, weight, size, color, popularity, and material. We evaluate the proposed embedding quantitatively and qualitatively. We demonstrate that they are almost as good as sparse and extremely high-dimensional TF-IDF representation in spite of having less than 3% of the TF-IDF dimension. We also use a multimodal autoencoder for comparing products from different language-regions and show preliminary yet promising qualitative results.

Neural networks, especially large-scale deep neural networks, have made remarkable success in various applications such as computer vision, natural language processing, etc. [14][21]. However, large-scale neural networks are both memory-intensive and computation-intensive, thereby posing severe challenges when deploying those large-scale neural network models on memory-constrained and energy-constrained embedded devices. To overcome these limitations, many studies and approaches, such as connection pruning [9][8], low rank approximation [7][12], sparsity regularization [23][16] etc., have been proposed to reduce the model size of large-scale (deep) neural networks. LDR Construction and LDR Neural Networks: Among those efforts, low displacement rank (LDR) construction is a type of structure-imposing technique for network model reduction and computational complexity reduction. By regularizing the weight matrices of neural networks using the format of LDR matrices (when weight matrices are square) or the composition of multiple LDR matrices (when weight matrices are non-square), a strong structure is naturally imposed to the construction of neural networks. Since an LDR matrix typically requires O(n) independent parameters and exhibits fast matrix operation algorithms [18], an immense space for network model and computational complexity reduction can be enabled. Pioneering work in this direction [3][20] applied special types of LDR matrices (structured matrices), such as circulant 1 Figure 1: Examples of commonly used LDR (structured) matrices, i.e., circulant, Cauchy, Toeplitz, Hankel, and Vandermonde matrices.

Deep convolutional neural networks (DCNNs) have been used to achieve state-of-the-art performance on many computer vision tasks (e.g., object recognition, object detection, semantic segmentation) thanks to a large repository of annotated image data. Large labeled datasets for other sensor modalities, e.g., multispectral imagery (MSI), are not available due to the large cost and manpower required. In this paper, we adapt state-of-the-art DCNN frameworks in computer vision for semantic segmentation for MSI imagery. To overcome label scarcity for MSI data, we substitute real MSI for generated synthetic MSI in order to initialize a DCNN framework. We evaluate our network initialization scheme on the new RIT-18 dataset that we present in this paper. This dataset contains very-high resolution MSI collected by an unmanned aircraft system. The models initialized with synthetic imagery were less prone to over-fitting and provide a state-of-the-art baseline for future work. Keywords: Deep learning, convolutional neural network, semantic segmentation, multispectral, unmanned aerial system, synthetic imagery 1. Introduction Semantic segmentation is the pixel-wise classification of an image, i.e., every pixel is assigned its own label.