master thesis deep learning pdf

This thesis report is structured into five chapters. Chapter 2 provides a theoretical expla-nation of machine learning theory. Chapter three reviews four of the most popular machine learning theories: decision trees, artificial neural networks, support vector machines and k-Nearest-Neighbor classification.

Deep Learning can be summed up as a sub eld of Machine Learning studying statical models called deep neural networks. The latter are able to learn complex and hierarchical ... This master's thesis introduces a number of contributions to di erent aspects of visual recognition. However our work is focused on classifying images and recognizing ...

Irfan Aziz: Deep Learning: An Overview of Convolutional Neural Network M.Sc Thesis Tampere University Master Degree Programme in Computational Big Data Analytics April 2020 In the last two decades, deep learning, an area of machine learning has made exponential progress and breakthroughs.

The main objective of this master thesis is to study the state of deep learning tools. We will present a comparative study of deep learning framew orks (e.g., T ensorflow, PyT orch, MX-

Abstract. Deep learning has become part of many state-of-the-art systems in multiple disciplines (specially in computer vision and speech processing). In this thesis Convolutional Neural Networks are used to solve the problem of recognizing people in images, both for verification and identification. Two different architectures, AlexNet and ...

Stinson, Derek L. M.S.E.C.E., Purdue University, May 2020. Deep Learning with Go. Major Professor: Zina Ben Miled. Current research in deep learning is primarily focused on using Python as a sup-port language. Go, an emerging language, that has many bene ts including native support for concurrency has seen a rise in adoption over the past few ...

context [4]. In this thesis, the term will be used in a more narrow sense, as proposed in Skansi's book Introdution to Deep Learning [5]. There, it refers to deep artificial neural networks as a subfield of machine learning. In this chapter, the crucial concepts for understanding deep learning are provided, following the description in [3].

View a PDF of the paper titled Master's Thesis : Deep Learning for Visual Recognition, by R\'emi Cad\`ene and 2 other authors. View PDF Abstract: The goal of our research is to develop methods advancing automatic visual recognition. In order to predict the unique or multiple labels associated to an image, we study different kind of Deep Neural ...

The new model family introduced in this thesis is summarized under the term Recursive Deep Learning. The models in this family are variations and extensions of unsupervised and supervised recursive neural networks (RNNs) which generalize deep and feature learning ideas to hierarchical structures. The RNN models of this thesis

explanation of Parkinson's disease, followed by overviews of machine learning, deep learning, related work and finally PD diagnosis problems. 2.1 Parkinson's disease (PD) 2.1.1 Overview PD is one of the diseases that happens when cells stop working properly or damage happens in a part of the brain called the substantia nigra pars compacta.

Step 3: Extracting features using a CNN. The proposed approach consists of 4 steps: Step 1: Locating the main face in the image. Step 2: Frontalizing the found face. Step 3: Extracting features using a CNN. Step 4: Performing comparison with stored ones. Goal: Look for the bounding box of the most likely face.

You will research, categorize and rank common optimization parameters for deep learning with special focus on architectures commonly used in natural language processing. To evaluate your findings, you conduct a hyper-parameter search on a given network architecture to maximize its performance. For running the search, you will research and ...

chine learning algorithms. In this master thesis, we investigate the potential of deep learning methods, a particular machine learning family, to help researchers using brain imaging to address issues concerning Alzheimer's disease. This manuscript is divided into six chapters :

Master Thesis Analysis of Explainable Artificial Intelligence on Time Series Data Author: Supervisors: ... The Deep Learning models that have been successfully used in the time series models are especially Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) [56].

Avito Loops has significant experience in text data mining and has already developed two text classifiers: one based on entity recognition, pattern matching and voting, the other based on machine learning and decision trees. This project's challenge was to develop a new classifier based on Deep Learning.

3 j Deep Learning 3.1 j Overview The buzzword Deep Learning is used to describe a trending topic in machine learning which has also gained a lot of public attention due to its role in solving a number of spectacular tasks, e.g. AlphaGO [28] beating the human GO champion in 2016. Deep Learning refers to the training of Arti cial Neural Networks ...

The objective of this thesis was to study the application of deep learning in image classification using convolutional neural networks. The Python programming language with the TensorFlow framework and Google Colaboratory hardware were used for the thesis. Models were chosen from available ones online and adjusted by the author.

the groundwork for this thesis. The internship had a duration of 5 months and the sixth month has been dedicated to writing the thesis. It has been a great opportunity to work with the newest technologies and research an interesting and hot topic of deep learning. The research question has been formulated by me together with Niels Ten Dijke ...

This Master Thesis provides a summary overview on the use of current deep learning-based object detection methods for the analysis of medical images, in particular from microscopic tissue sections. An accentuating peculiarity of medical image analysis and likewise a pronounced challenge, arises from the fact, that, datasets from patients are ...

Master's thesis, 63 pages Tampere University Master's Degree Programme in Computational Big Data Analytics ... Deep learning is a specific method of machine learning that incorporates neural networks or other structures in successive layers to learn from data in an iterative manner and it uses hierarchical neural networks to learn from a ...

First, we created four univariate first aut oregressive time series x1, x2, x3, x4where we use 0.1 as coefficient. for the AR model where each o f them contain 3000 timestamps. Additionally, we ...

This thesis considers deep learning theories of brain function, and in particular biologically plausible deep learning. The idea is to treat a standard deep network as a high-level model of a neural circuit (e.g., the visual stream), adding biological constraints to some clearly artificial features. Two big questions are possible. First,

di erent techniques, such as deep learning, to deal with complex syntax and semantics. Therefore, this thesis will also investigate the impact of active learning on sentiment analysis. The active learning system was incorporated into an existing data management system during the project execution at Philips Research.