Fingerprint, as a unique feature of each person, can be divided into different types. In this project, we identify real fingerprints pattern and classify them with convolutional neural networks(CNN). The dataset is the original NIST 8-Bit Gray Scale Images of Fingerprint Image Groups(FIGS). It comprises 4000 images that are originally of size 512 x 512. The image size has been reduced to 128 x 128 for computational purposes. Now that we know how our dataset looks like, we can start our project.

It is available to practically all areas. There is a lot of information accessible today, and using them appropriately can spell achievement and disappointment for brands and associations. Appropriately using information will hold the way to accomplishing objectives for brands, particularly in the coming occasions. The manner by which profound learning and AI contrast is in how every calculation learns. Profound learning robotizes a significant part of the component extraction piece of the cycle, disposing of a portion of the manual human intercession required and empowering the utilization of bigger informational indexes.

This database contains 76 attributes, but all published experiments refer to using a subset of 14 of them. In particular, the Cleveland database is the only one that has been used by ML researchers to this date. The "goal" field refers to the presence of heart disease in the patient. It is integer-valued from 0 (no presence) to 4. This is how we make a model for the prediction of a heart disease dataset using Neural Networks. There are a million ways to make a model because of recent and upcoming developments in the field of Deep Learning.

Take some of the most popular language models, for example. OpenAI trained its GPT-3 model on 45 terabytes of data. To train the final version of MegatronLM, a language model similar to but smaller than GPT-3, Nvidia ran 512 V100 GPUs over nine days. A single V100 GPU can consume between 250 and 300 watts. If we assume 250 watts, then 512 V100 GPUS consumes 128,000 watts, or 128 kilowatts (kW).

Blockchain technology and Artificial Intelligence are technologies that have significant potential and individually can revolutionize industries. There is a strong case for the convergence of AI and blockchain technologies, as combining these technologies and using them in tandem would completely revolutionize industries and existing business models. The use of blockchain technology would improve transparency, security, and the privacy of business processes. At AIWORK, we are among the first to combine blockchain and AI and are pioneering the convergence of blockchain and AI with our open-source blockchain ecosystem that is built on an Artificial Intelligence consensus network. The AIWORK ecosystem utilizes AI computing resources that are backed by human experts.

Until around 2015, image tasks such as face recognition were typically done by means of laborious hand coded programs that picked up facial features such as eyebrows and noses. Many OCR or face recognition applications were not using machine learning at all. The rapid acceleration in computing power, and the wide availability of large datasets, GPUs, and deep learning software, meant that around the mid 2010s, convolutional neural networks were able to deliver much better accuracy than the traditional methods and suddenly became the standard for nearly all computer vision related tasks in academia and industry. Now the average smartphone user probably has one or two apps running convolutional neural networks in their pocket, a concept that would have been unthinkable in 2010.

In this paper we propose algorithms that combine modern machine learning practises with theoretical stochastic control principles to solve a range of control problems with high accuracy, scalability with dimension, and low computational cost. There is a natural crossover between machine learning and stochastic control problems, as they both involve searching for features within a data set. For stochastic control the data set is a state process that is influenced by a controlling process that can be chosen with the aim to optimise some objective function. Often the optimal control is a function of the underlying state process, a so-called Markovian control. This is very similar to deep learning, where the control is chosen as the output of a neural network which takes the dataset as input, and some loss function is minimised. It is in this area of crossover that this paper resides. We propose deep learning methods to solve the utility maximisation problem, as an application of a more general stochastic control solver, in a wide range of markets with arbitrary convex control constraints. Dynamic portfolio optimisation has been extensively studied within the field of mathematical finance, see [19, 27] for exposition. A typical drawback of numerical methods for stochastic control problems is that their complexity increases dramatically with increases in the state and control dimensions.

Code generation is crucial to reduce manual software development efforts. Recently, neural techniques have been used to generate source code automatically. While promising, these approaches are evaluated on tasks for generating code in single programming languages. However, in actual development, one programming language is often embedded in another. For example, SQL statements are often embedded as strings in base programming languages such as Python and Java, and JavaScript programs are often embedded in sever-side programming languages, such as PHP, Java, and Python. We call this a turducken-style programming. In this paper, we define a new code generation task: given a natural language comment, this task aims to generate a program in a base language with an embedded language. To our knowledge, this is the first turducken-style code generation task. For this task, we present Lyra: a dataset in Python with embedded SQL. This dataset contains 2,000 carefully annotated database manipulation programs from real usage projects. Each program is paired with both a Chinese comment and an English comment. In our experiment, we adopted Transformer, a state-of-the-art technique, as the baseline. In the best setting, Transformer achieves 0.5% and 1.5% AST exact matching accuracy using Chinese and English comments, respectively. Therefore, we believe that Lyra provides a new challenge for code generation.

Creating virtual humans with embodied, human-like perceptual and actuation constraints has the promise to provide an integrated simulation platform for many scientific and engineering applications. We present Dynamic and Autonomous Simulated Human (DASH), an embodied virtual human that, given natural language commands, performs grasp-and-stack tasks in a physically-simulated cluttered environment solely using its own visual perception, proprioception, and touch, without requiring human motion data. By factoring the DASH system into a vision module, a language module, and manipulation modules of two skill categories, we can mix and match analytical and machine learning techniques for different modules so that DASH is able to not only perform randomly arranged tasks with a high success rate, but also do so under anthropomorphic Figure 1: Our system, dynamic and autonomous simulated constraints and with fluid and diverse motions. The modular design human (DASH), is an embodied virtual human modeled off also favors analysis and extensibility to more complex manipulation of a child. DASH is able to manipulate tabletop objects with a skills.

When patients develop acute respiratory failure, accurately identifying the underlying etiology is essential for determining the best treatment, but it can be challenging to differentiate between common diagnoses in clinical practice. Machine learning models could improve medical diagnosis by augmenting clinical decision making and play a role in the diagnostic evaluation of patients with acute respiratory failure. While machine learning models have been developed to identify common findings on chest radiographs (e.g. pneumonia), augmenting these approaches by also analyzing clinically relevant data from the electronic health record (EHR) could aid in the diagnosis of acute respiratory failure. Machine learning models were trained to predict the cause of acute respiratory failure (pneumonia, heart failure, and/or COPD) using chest radiographs and EHR data from patients within an internal cohort using diagnoses based on physician chart review. Models were also tested on patients in an external cohort using discharge diagnosis codes. A model combining chest radiographs and EHR data outperformed models based on each modality alone for pneumonia and COPD. For pneumonia, the combined model AUROC was 0.79 (0.78-0.79), image model AUROC was 0.73 (0.72-0.75), and EHR model AUROC was 0.73 (0.70-0.76); for COPD, combined: 0.89 (0.83-0.91), image: 0.85 (0.77-0.89), and EHR: 0.80 (0.76-0.84); for heart failure, combined: 0.80 (0.77-0.84), image: 0.77 (0.71-0.81), and EHR: 0.80 (0.75-0.82). In the external cohort, performance was consistent for heart failure and COPD, but declined slightly for pneumonia. Overall, machine learning models combing chest radiographs and EHR data can accurately differentiate between common causes of acute respiratory failure. Further work is needed to determine whether these models could aid clinicians in the diagnosis of acute respiratory failure in clinical settings.