�ݺ�ߣshows by User: bix883

�ݺ�ߣshows by User: bix883 / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: bix883 / Fri, 08 Nov 2019 16:22:07 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: bix883 IBM Prague ai - real life experiences in engaging customers and do business - v001 to pdf compressed /slideshow/ibm-prague-ai-real-life-experiences-in-engaging-customers-and-do-business-v001-to-pdf-compressed/191730191 20191024ibmprague-ai-reallifeexperiencesinengagingcustomersanddobusiness-v001topdfcompressed-191108162207
Real Life Experiences in Engaging Customers and Doing Business My Presentation at the IBM TechU University in Prague 2019 This presentation is about selling Artificial Intelligence Solutions in a market thet is still not completely aware of the new possibilities.]]>
Real Life Experiences in Engaging Customers and Doing Business My Presentation at the IBM TechU University in Prague 2019 This presentation is about selling Artificial Intelligence Solutions in a market thet is still not completely aware of the new possibilities.]]> Fri, 08 Nov 2019 16:22:07 GMT /slideshow/ibm-prague-ai-real-life-experiences-in-engaging-customers-and-do-business-v001-to-pdf-compressed/191730191 bix883@slideshare.net(bix883) IBM Prague ai - real life experiences in engaging customers and do business - v001 to pdf compressed bix883 Real Life Experiences in Engaging Customers and Doing Business My Presentation at the IBM TechU University in Prague 2019 This presentation is about selling Artificial Intelligence Solutions in a market thet is still not completely aware of the new possibilities. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/20191024ibmprague-ai-reallifeexperiencesinengagingcustomersanddobusiness-v001topdfcompressed-191108162207-thumbnail.jpg?width=120&height=120&fit=bounds" /> Real Life Experiences in Engaging Customers and Doing Business My Presentation at the IBM TechU University in Prague 2019 This presentation is about selling Artificial Intelligence Solutions in a market thet is still not completely aware of the new possibilities.

IBM Prague ai - real life experiences in engaging customers and do business - v001 to pdf compressed from Enrico Busto

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0 20181210 Super Resolution /slideshow/20181210-super-resolution/146232510 20181210-superresolution-190517083701
First test of SuperResolution Algorithms]]>
First test of SuperResolution Algorithms]]> Fri, 17 May 2019 08:37:01 GMT /slideshow/20181210-super-resolution/146232510 bix883@slideshare.net(bix883) 20181210 Super Resolution bix883 First test of SuperResolution Algorithms <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/20181210-superresolution-190517083701-thumbnail.jpg?width=120&height=120&fit=bounds" /> First test of SuperResolution Algorithms

20181210 Super Resolution from Enrico Busto

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0 Master's Thesis - inverse reinforcement learning for autonomous driving /slideshow/masters-thesis-inverse-reinforcement-learning-for-autonomous-driving/139491322 thesis22-inversereinforcementlearningforautonomousdriving-190404042557
Reinforcement Learning [1] (RL) is an emerging field of Artificial Intelligence (AI) that is giving extraordinary results in different applications. One of such applications is Autonomous Driving, but to apply RL to this task an accurate choice of the reward function is needed.To overcome this issue, one solution is to infer the reward function applying Machine Learning (ML) techniques to some examples provided by experts. For example, a driver can show how to do a specific maneuver and a ML algorithm extract the objective function maximized by the driver behaviour.This method is known as Inverse Reinforcement Learning [2] (IRL). The thesis will deepen the theory behind inverse reinforcement learning to analyze the possible applications of this approach to autonomous driving [3] in a simulated environment [4, 5, 6].]]>
Reinforcement Learning [1] (RL) is an emerging field of Artificial Intelligence (AI) that is giving extraordinary results in different applications. One of such applications is Autonomous Driving, but to apply RL to this task an accurate choice of the reward function is needed.To overcome this issue, one solution is to infer the reward function applying Machine Learning (ML) techniques to some examples provided by experts. For example, a driver can show how to do a specific maneuver and a ML algorithm extract the objective function maximized by the driver behaviour.This method is known as Inverse Reinforcement Learning [2] (IRL). The thesis will deepen the theory behind inverse reinforcement learning to analyze the possible applications of this approach to autonomous driving [3] in a simulated environment [4, 5, 6].]]> Thu, 04 Apr 2019 04:25:56 GMT /slideshow/masters-thesis-inverse-reinforcement-learning-for-autonomous-driving/139491322 bix883@slideshare.net(bix883) Master's Thesis - inverse reinforcement learning for autonomous driving bix883 Reinforcement Learning [1] (RL) is an emerging field of Artificial Intelligence (AI) that is giving extraordinary results in different applications. One of such applications is Autonomous Driving, but to apply RL to this task an accurate choice of the reward function is needed.To overcome this issue, one solution is to infer the reward function applying Machine Learning (ML) techniques to some examples provided by experts. For example, a driver can show how to do a specific maneuver and a ML algorithm extract the objective function maximized by the driver behaviour.This method is known as Inverse Reinforcement Learning [2] (IRL). The thesis will deepen the theory behind inverse reinforcement learning to analyze the possible applications of this approach to autonomous driving [3] in a simulated environment [4, 5, 6]. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/thesis22-inversereinforcementlearningforautonomousdriving-190404042557-thumbnail.jpg?width=120&height=120&fit=bounds" /> Reinforcement Learning [1] (RL) is an emerging field of Artificial Intelligence (AI) that is giving extraordinary results in different applications. One of such applications is Autonomous Driving, but to apply RL to this task an accurate choice of the reward function is needed.To overcome this issue, one solution is to infer the reward function applying Machine Learning (ML) techniques to some examples provided by experts. For example, a driver can show how to do a specific maneuver and a ML algorithm extract the objective function maximized by the driver behaviour.This method is known as Inverse Reinforcement Learning [2] (IRL). The thesis will deepen the theory behind inverse reinforcement learning to analyze the possible applications of this approach to autonomous driving [3] in a simulated environment [4, 5, 6].

Master's Thesis - inverse reinforcement learning for autonomous driving from Enrico Busto

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0 Master's Thesis - deep genomics: harnessing the power of deep neural networks in the analysis of biomolecular data /bix883/masters-thesis-deep-genomics-harnessing-the-power-of-deep-neural-networks-in-the-analysis-of-biomolecular-data thesis18-deepgenomicsharnessingthepowerofdeepneuralnetworksintheanalysisofbiomoleculardata-190404041941
The human genome project [1], an international scientific research project with the goal of determining the sequence of nucleotide base pairs that make up human DNA, lasted roughly 15 years and cost $5 billion (adjusted for inflation). With the recent advances in genome sequencing technology, that cost has now reduced to a few hundreds dollars [2] and can be done overnight. Being able to access this kind of information may have a deep impact on the way complex diseases are treated: physicians will shift from general-purpose treatments to specific ones, tailored on the individual patient’s genomic features.This approach is referred to as precision medicine. There are however several caveats: first of all, due to the nature of the problem, knowledge of both the biomedical and the computer science domain are required in order to correctly approach it; second, unlike more classical scenarios such as image classification or object detection, it is much more difficult to determine the accuracy of the system due to the complex and multifactorial nature of complex diseases such as cancer and neurodegenerative diseases. Moreover, a black box kind of solution is unlikely to be of any use, due to legal and ethical reasons: interpretability of the model is crucial more than ever. The goal of this thesis is to explore the possibilities and the limits of techniques based on deep neural networks for the analysis of biomolecular data, experimenting with publicly available datasets.]]>
The human genome project [1], an international scientific research project with the goal of determining the sequence of nucleotide base pairs that make up human DNA, lasted roughly 15 years and cost $5 billion (adjusted for inflation). With the recent advances in genome sequencing technology, that cost has now reduced to a few hundreds dollars [2] and can be done overnight. Being able to access this kind of information may have a deep impact on the way complex diseases are treated: physicians will shift from general-purpose treatments to specific ones, tailored on the individual patient’s genomic features.This approach is referred to as precision medicine. There are however several caveats: first of all, due to the nature of the problem, knowledge of both the biomedical and the computer science domain are required in order to correctly approach it; second, unlike more classical scenarios such as image classification or object detection, it is much more difficult to determine the accuracy of the system due to the complex and multifactorial nature of complex diseases such as cancer and neurodegenerative diseases. Moreover, a black box kind of solution is unlikely to be of any use, due to legal and ethical reasons: interpretability of the model is crucial more than ever. The goal of this thesis is to explore the possibilities and the limits of techniques based on deep neural networks for the analysis of biomolecular data, experimenting with publicly available datasets.]]> Thu, 04 Apr 2019 04:19:41 GMT /bix883/masters-thesis-deep-genomics-harnessing-the-power-of-deep-neural-networks-in-the-analysis-of-biomolecular-data bix883@slideshare.net(bix883) Master's Thesis - deep genomics: harnessing the power of deep neural networks in the analysis of biomolecular data bix883 The human genome project [1], an international scientific research project with the goal of determining the sequence of nucleotide base pairs that make up human DNA, lasted roughly 15 years and cost $5 billion (adjusted for inflation). With the recent advances in genome sequencing technology, that cost has now reduced to a few hundreds dollars [2] and can be done overnight. Being able to access this kind of information may have a deep impact on the way complex diseases are treated: physicians will shift from general-purpose treatments to specific ones, tailored on the individual patient’s genomic features.This approach is referred to as precision medicine. There are however several caveats: first of all, due to the nature of the problem, knowledge of both the biomedical and the computer science domain are required in order to correctly approach it; second, unlike more classical scenarios such as image classification or object detection, it is much more difficult to determine the accuracy of the system due to the complex and multifactorial nature of complex diseases such as cancer and neurodegenerative diseases. Moreover, a black box kind of solution is unlikely to be of any use, due to legal and ethical reasons: interpretability of the model is crucial more than ever. The goal of this thesis is to explore the possibilities and the limits of techniques based on deep neural networks for the analysis of biomolecular data, experimenting with publicly available datasets. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/thesis18-deepgenomicsharnessingthepowerofdeepneuralnetworksintheanalysisofbiomoleculardata-190404041941-thumbnail.jpg?width=120&height=120&fit=bounds" /> The human genome project [1], an international scientific research project with the goal of determining the sequence of nucleotide base pairs that make up human DNA, lasted roughly 15 years and cost $5 billion (adjusted for inflation). With the recent advances in genome sequencing technology, that cost has now reduced to a few hundreds dollars [2] and can be done overnight. Being able to access this kind of information may have a deep impact on the way complex diseases are treated: physicians will shift from general-purpose treatments to specific ones, tailored on the individual patient’s genomic features.This approach is referred to as precision medicine. There are however several caveats: first of all, due to the nature of the problem, knowledge of both the biomedical and the computer science domain are required in order to correctly approach it; second, unlike more classical scenarios such as image classification or object detection, it is much more difficult to determine the accuracy of the system due to the complex and multifactorial nature of complex diseases such as cancer and neurodegenerative diseases. Moreover, a black box kind of solution is unlikely to be of any use, due to legal and ethical reasons: interpretability of the model is crucial more than ever. The goal of this thesis is to explore the possibilities and the limits of techniques based on deep neural networks for the analysis of biomolecular data, experimenting with publicly available datasets.

Master's Thesis - deep genomics: harnessing the power of deep neural networks in the analysis of biomolecular data from Enrico Busto

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0 Master's Thesis - comparison of reinforcement learning frameworks /slideshow/masters-thesis-comparison-of-reinforcement-learning-frameworks/139489512 thesis15-comparisonofreinforcementlearningframeworks-190404041205
Reinforcement Learning (RL) is a class of machine learning algorithms in which an agent interacts by trial-and-error in an environment. RL in conjunction with Deep Learning has obtained outstanding results in Atari video games, the Go board-game and a more complex environment like StarCraft II. Recently many open source RL frameworks has been released by software companies in order to easily train and test new RL algorithms. The goal of the thesis is to benchmark the most promising RL frameworks, to study the new algorithms proposed and to evaluate their performance on research environments.]]>
Reinforcement Learning (RL) is a class of machine learning algorithms in which an agent interacts by trial-and-error in an environment. RL in conjunction with Deep Learning has obtained outstanding results in Atari video games, the Go board-game and a more complex environment like StarCraft II. Recently many open source RL frameworks has been released by software companies in order to easily train and test new RL algorithms. The goal of the thesis is to benchmark the most promising RL frameworks, to study the new algorithms proposed and to evaluate their performance on research environments.]]> Thu, 04 Apr 2019 04:12:05 GMT /slideshow/masters-thesis-comparison-of-reinforcement-learning-frameworks/139489512 bix883@slideshare.net(bix883) Master's Thesis - comparison of reinforcement learning frameworks bix883 Reinforcement Learning (RL) is a class of machine learning algorithms in which an agent interacts by trial-and-error in an environment. RL in conjunction with Deep Learning has obtained outstanding results in Atari video games, the Go board-game and a more complex environment like StarCraft II. Recently many open source RL frameworks has been released by software companies in order to easily train and test new RL algorithms. The goal of the thesis is to benchmark the most promising RL frameworks, to study the new algorithms proposed and to evaluate their performance on research environments. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/thesis15-comparisonofreinforcementlearningframeworks-190404041205-thumbnail.jpg?width=120&height=120&fit=bounds" /> Reinforcement Learning (RL) is a class of machine learning algorithms in which an agent interacts by trial-and-error in an environment. RL in conjunction with Deep Learning has obtained outstanding results in Atari video games, the Go board-game and a more complex environment like StarCraft II. Recently many open source RL frameworks has been released by software companies in order to easily train and test new RL algorithms. The goal of the thesis is to benchmark the most promising RL frameworks, to study the new algorithms proposed and to evaluate their performance on research environments.

Master's Thesis - comparison of reinforcement learning frameworks from Enrico Busto

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0 Master's degree thesis testing algorithms for image & video understanding /bix883/masters-degree-thesis-testing-algorithms-for-image-amp-video-understanding thesis08-testingalgorithmsforimagevideounderstanding-190404040147
In the last few years, many algorithms with remarkable effectiveness for Object Detection have been published but still some comparative metrics haven’t been defined. The difficulties in making this comparison arise from the fact that different algorithms are based on different Feature Extractors (VGGs, Residual Networks, etc.), different base resolution and different implementation on specific platforms.]]>
In the last few years, many algorithms with remarkable effectiveness for Object Detection have been published but still some comparative metrics haven’t been defined. The difficulties in making this comparison arise from the fact that different algorithms are based on different Feature Extractors (VGGs, Residual Networks, etc.), different base resolution and different implementation on specific platforms.]]> Thu, 04 Apr 2019 04:01:47 GMT /bix883/masters-degree-thesis-testing-algorithms-for-image-amp-video-understanding bix883@slideshare.net(bix883) Master's degree thesis testing algorithms for image & video understanding bix883 In the last few years, many algorithms with remarkable effectiveness for Object Detection have been published but still some comparative metrics haven’t been defined. The difficulties in making this comparison arise from the fact that different algorithms are based on different Feature Extractors (VGGs, Residual Networks, etc.), different base resolution and different implementation on specific platforms. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/thesis08-testingalgorithmsforimagevideounderstanding-190404040147-thumbnail.jpg?width=120&height=120&fit=bounds" /> In the last few years, many algorithms with remarkable effectiveness for Object Detection have been published but still some comparative metrics haven’t been defined. The difficulties in making this comparison arise from the fact that different algorithms are based on different Feature Extractors (VGGs, Residual Networks, etc.), different base resolution and different implementation on specific platforms.

Master's degree thesis testing algorithms for image & video understanding from Enrico Busto

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0 20180509 energy - v001 /slideshow/20180509-energy-v001-137477435/137477435 20180509-energy-v001-190321122202
Energy Management]]>
Energy Management]]> Thu, 21 Mar 2019 12:22:02 GMT /slideshow/20180509-energy-v001-137477435/137477435 bix883@slideshare.net(bix883) 20180509 energy - v001 bix883 Energy Management <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/20180509-energy-v001-190321122202-thumbnail.jpg?width=120&height=120&fit=bounds" /> Energy Management

20180509 energy - v001 from Enrico Busto

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0 Why join the navy - Addfor prsentation /slideshow/why-join-the-navy-addfor-prsentation/134651624 whyjointhenavyv001-staticversiontopdf-190305085623
�ݺ�ߣs presented at the SmartData@PoliTO 2019 event. Politecnico di Torino - Turin - February 28 2019]]>
�ݺ�ߣs presented at the SmartData@PoliTO 2019 event. Politecnico di Torino - Turin - February 28 2019]]> Tue, 05 Mar 2019 08:56:23 GMT /slideshow/why-join-the-navy-addfor-prsentation/134651624 bix883@slideshare.net(bix883) Why join the navy - Addfor prsentation bix883 �ݺ�ߣs presented at the SmartData@PoliTO 2019 event. Politecnico di Torino - Turin - February 28 2019 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/whyjointhenavyv001-staticversiontopdf-190305085623-thumbnail.jpg?width=120&height=120&fit=bounds" /> �ݺ�ߣs presented at the SmartData@PoliTO 2019 event. Politecnico di Torino - Turin - February 28 2019

Why join the navy - Addfor prsentation from Enrico Busto

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0 Meetup IBM Rome October 24th 2018 /slideshow/meetup-ibm-rome-october-24th-2018/120692100 20181024-ibmroma-v002customercopy-181025132330
Artificial Intelligence, Synthetic Dataset Generation, Generative Adversarial Networks, Autonomous Driving]]>
Artificial Intelligence, Synthetic Dataset Generation, Generative Adversarial Networks, Autonomous Driving]]> Thu, 25 Oct 2018 13:23:30 GMT /slideshow/meetup-ibm-rome-october-24th-2018/120692100 bix883@slideshare.net(bix883) Meetup IBM Rome October 24th 2018 bix883 Artificial Intelligence, Synthetic Dataset Generation, Generative Adversarial Networks, Autonomous Driving <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/20181024-ibmroma-v002customercopy-181025132330-thumbnail.jpg?width=120&height=120&fit=bounds" /> Artificial Intelligence, Synthetic Dataset Generation, Generative Adversarial Networks, Autonomous Driving

Meetup IBM Rome October 24th 2018 from Enrico Busto

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0 Ai business innovator v001 /bix883/ai-business-innovator-v001 aibusinessinnovatorv001-180607162418
AI for Business Innovators]]>
AI for Business Innovators]]> Thu, 07 Jun 2018 16:24:18 GMT /bix883/ai-business-innovator-v001 bix883@slideshare.net(bix883) Ai business innovator v001 bix883 AI for Business Innovators <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/aibusinessinnovatorv001-180607162418-thumbnail.jpg?width=120&height=120&fit=bounds" /> AI for Business Innovators

Ai business innovator v001 from Enrico Busto

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0 Imaging automotive 2015 addfor v002 /bix883/imaging-automotive-2015-addfor-v002-90668999 imagingautomotive2015-addforv002-180314171252
Imaging APPLICATIONS]]>
Imaging APPLICATIONS]]> Wed, 14 Mar 2018 17:12:52 GMT /bix883/imaging-automotive-2015-addfor-v002-90668999 bix883@slideshare.net(bix883) Imaging automotive 2015 addfor v002 bix883 Imaging APPLICATIONS <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/imagingautomotive2015-addforv002-180314171252-thumbnail.jpg?width=120&height=120&fit=bounds" /> Imaging APPLICATIONS

Imaging automotive 2015 addfor v002 from Enrico Busto

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0 PaSSED - IBM Power AI - Addfor /slideshow/passed-ibm-power-ai-addfor/80820500 20171006-ibmpassedpowerai-v002-171015055753
AI and Cognitive Computing on IBM Power AI]]>
AI and Cognitive Computing on IBM Power AI]]> Sun, 15 Oct 2017 05:57:53 GMT /slideshow/passed-ibm-power-ai-addfor/80820500 bix883@slideshare.net(bix883) PaSSED - IBM Power AI - Addfor bix883 AI and Cognitive Computing on IBM Power AI <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/20171006-ibmpassedpowerai-v002-171015055753-thumbnail.jpg?width=120&height=120&fit=bounds" /> AI and Cognitive Computing on IBM Power AI

PaSSED - IBM Power AI - Addfor from Enrico Busto

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0 NVIDIA DGX-1 Community-Based Benchmark /slideshow/nvidia-dgx1-communitybased-benchmark/72628946 nvidiadgx-1benchmarkcommunity-based-170227170923
We are opening to the community our benchmarks on the NVIDIA DGX-1 Supercomputer]]>
We are opening to the community our benchmarks on the NVIDIA DGX-1 Supercomputer]]> Mon, 27 Feb 2017 17:09:23 GMT /slideshow/nvidia-dgx1-communitybased-benchmark/72628946 bix883@slideshare.net(bix883) NVIDIA DGX-1 Community-Based Benchmark bix883 We are opening to the community our benchmarks on the NVIDIA DGX-1 Supercomputer <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/nvidiadgx-1benchmarkcommunity-based-170227170923-thumbnail.jpg?width=120&height=120&fit=bounds" /> We are opening to the community our benchmarks on the NVIDIA DGX-1 Supercomputer

NVIDIA DGX-1 Community-Based Benchmark from Enrico Busto

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0 ARTIFICIAL INTELLIGENCE AT WORK /slideshow/artificial-intelligence-at-work/72592110 20170201-aiindustrialapplications-v001pdfversion-170226155135
Artificial Intelligence Industrial Applications: what's available today You will see some examples of Anomaly Detection, Predictive Maintenance, advanced control Systems and Image Understanding in Industrial and Business Environments. For more information contact: it.linkedin.com/in/ebusto]]>
Artificial Intelligence Industrial Applications: what's available today You will see some examples of Anomaly Detection, Predictive Maintenance, advanced control Systems and Image Understanding in Industrial and Business Environments. For more information contact: it.linkedin.com/in/ebusto]]> Sun, 26 Feb 2017 15:51:35 GMT /slideshow/artificial-intelligence-at-work/72592110 bix883@slideshare.net(bix883) ARTIFICIAL INTELLIGENCE AT WORK bix883 Artificial Intelligence Industrial Applications: what's available today You will see some examples of Anomaly Detection, Predictive Maintenance, advanced control Systems and Image Understanding in Industrial and Business Environments. For more information contact: it.linkedin.com/in/ebusto <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/20170201-aiindustrialapplications-v001pdfversion-170226155135-thumbnail.jpg?width=120&height=120&fit=bounds" /> Artificial Intelligence Industrial Applications: what's available today You will see some examples of Anomaly Detection, Predictive Maintenance, advanced control Systems and Image Understanding in Industrial and Business Environments. For more information contact: it.linkedin.com/in/ebusto

ARTIFICIAL INTELLIGENCE AT WORK from Enrico Busto

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0 Performance Traction Control (PTC) /slideshow/performance-traction-control-ptc/62272500 ptc2016-v001slideshare-160522131559
The Performance Traction Control is an algorithm developed by Addfor to maximize the vehicle performance in every driving condition giving the vehicle the maximum available acceleration in exiting turns. For any product details or customer specific questions our highly specialized team of Data Scientists and Engineers are available to answer you questions. For more information visit: www.add-for.com]]>
The Performance Traction Control is an algorithm developed by Addfor to maximize the vehicle performance in every driving condition giving the vehicle the maximum available acceleration in exiting turns. For any product details or customer specific questions our highly specialized team of Data Scientists and Engineers are available to answer you questions. For more information visit: www.add-for.com]]> Sun, 22 May 2016 13:15:59 GMT /slideshow/performance-traction-control-ptc/62272500 bix883@slideshare.net(bix883) Performance Traction Control (PTC) bix883 The Performance Traction Control is an algorithm developed by Addfor to maximize the vehicle performance in every driving condition giving the vehicle the maximum available acceleration in exiting turns. For any product details or customer specific questions our highly specialized team of Data Scientists and Engineers are available to answer you questions. For more information visit: www.add-for.com <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ptc2016-v001slideshare-160522131559-thumbnail.jpg?width=120&height=120&fit=bounds" /> The Performance Traction Control is an algorithm developed by Addfor to maximize the vehicle performance in every driving condition giving the vehicle the maximum available acceleration in exiting turns. For any product details or customer specific questions our highly specialized team of Data Scientists and Engineers are available to answer you questions. For more information visit: www.add-for.com

Performance Traction Control (PTC) from Enrico Busto

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0 SideSlip Angle Estimator (SSE) /slideshow/sideslip-angle-estimator-sse/62272417 sse2016-v001slideshare-160522131041
The SideSlip Angle Estimator is an algorithm developed by Addfor to estimate the Drifting Angle of a Vehicle. The SideSlip also known as “Beta” or “Drifting” angle is the angle between the longitudinal axis of the vehicle and its speed direction. This angle cannot be measured with standard sensors but is very important for the modern Traction Control Systems. Addfor has developed an high performance estimator based on the Deep Learning technology that outperforms the commonly known Kalman filtering approach. For more information visit: www.add-for.com]]>
The SideSlip Angle Estimator is an algorithm developed by Addfor to estimate the Drifting Angle of a Vehicle. The SideSlip also known as “Beta” or “Drifting” angle is the angle between the longitudinal axis of the vehicle and its speed direction. This angle cannot be measured with standard sensors but is very important for the modern Traction Control Systems. Addfor has developed an high performance estimator based on the Deep Learning technology that outperforms the commonly known Kalman filtering approach. For more information visit: www.add-for.com]]> Sun, 22 May 2016 13:10:41 GMT /slideshow/sideslip-angle-estimator-sse/62272417 bix883@slideshare.net(bix883) SideSlip Angle Estimator (SSE) bix883 The SideSlip Angle Estimator is an algorithm developed by Addfor to estimate the Drifting Angle of a Vehicle. The SideSlip also known as “Beta” or “Drifting” angle is the angle between the longitudinal axis of the vehicle and its speed direction. This angle cannot be measured with standard sensors but is very important for the modern Traction Control Systems. Addfor has developed an high performance estimator based on the Deep Learning technology that outperforms the commonly known Kalman filtering approach. For more information visit: www.add-for.com <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/sse2016-v001slideshare-160522131041-thumbnail.jpg?width=120&height=120&fit=bounds" /> The SideSlip Angle Estimator is an algorithm developed by Addfor to estimate the Drifting Angle of a Vehicle. The SideSlip also known as “Beta” or “Drifting” angle is the angle between the longitudinal axis of the vehicle and its speed direction. This angle cannot be measured with standard sensors but is very important for the modern Traction Control Systems. Addfor has developed an high performance estimator based on the Deep Learning technology that outperforms the commonly known Kalman filtering approach. For more information visit: www.add-for.com

SideSlip Angle Estimator (SSE) from Enrico Busto

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0 Wiki stage 20151128 - v001 /slideshow/wiki-stage-20151128-v001/55573921 wikistage-20151128-v001-151127104822-lva1-app6891
This is a less - technical, divulgative presentation on Deep Learning and Artificial Intelligence]]>
This is a less - technical, divulgative presentation on Deep Learning and Artificial Intelligence]]> Fri, 27 Nov 2015 10:48:22 GMT /slideshow/wiki-stage-20151128-v001/55573921 bix883@slideshare.net(bix883) Wiki stage 20151128 - v001 bix883 This is a less - technical, divulgative presentation on Deep Learning and Artificial Intelligence <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/wikistage-20151128-v001-151127104822-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds" /> This is a less - technical, divulgative presentation on Deep Learning and Artificial Intelligence

Wiki stage 20151128 - v001 from Enrico Busto

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0 Automotive Virtual Sensors - Motorsport Applications /slideshow/automotive-virtual-sensors-motorsport-applications/54994843 bmwmotorsp2015-v001-151111132851-lva1-app6892
Motorsport and Performance Passenger Cars Applications]]>
Motorsport and Performance Passenger Cars Applications]]> Wed, 11 Nov 2015 13:28:51 GMT /slideshow/automotive-virtual-sensors-motorsport-applications/54994843 bix883@slideshare.net(bix883) Automotive Virtual Sensors - Motorsport Applications bix883 Motorsport and Performance Passenger Cars Applications <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/bmwmotorsp2015-v001-151111132851-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds" /> Motorsport and Performance Passenger Cars Applications

Automotive Virtual Sensors - Motorsport Applications from Enrico Busto

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0 Imaging automotive 2015 addfor v002 /slideshow/imaging-automotive-2015-addfor-v002/54993218 imagingautomotive2015-addforv002-151111124100-lva1-app6892
We develop custom Image Recognition systems for Aerospace and defence applications. Using algorithms like Deep Convolutional Neural Networks and Regional Convolutional Neural Networks. Our algorithms for Target Recognition and Tracking are designed from the beginning to be run on embedded systems. We target both GPU and FPGA devices. To Train and Validate our algorithms we developed a process to generate photorealistic 3D environments. Those 3D Environments are used to produce realistic video streams of the targets in different environmental conditions (lighting, adverse meteorological conditions, camouflage, point-of-view). The same technology can be used to Train and Test Automotive Vision Systems.]]>
We develop custom Image Recognition systems for Aerospace and defence applications. Using algorithms like Deep Convolutional Neural Networks and Regional Convolutional Neural Networks. Our algorithms for Target Recognition and Tracking are designed from the beginning to be run on embedded systems. We target both GPU and FPGA devices. To Train and Validate our algorithms we developed a process to generate photorealistic 3D environments. Those 3D Environments are used to produce realistic video streams of the targets in different environmental conditions (lighting, adverse meteorological conditions, camouflage, point-of-view). The same technology can be used to Train and Test Automotive Vision Systems.]]> Wed, 11 Nov 2015 12:40:59 GMT /slideshow/imaging-automotive-2015-addfor-v002/54993218 bix883@slideshare.net(bix883) Imaging automotive 2015 addfor v002 bix883 We develop custom Image Recognition systems for Aerospace and defence applications. Using algorithms like Deep Convolutional Neural Networks and Regional Convolutional Neural Networks. Our algorithms for Target Recognition and Tracking are designed from the beginning to be run on embedded systems. We target both GPU and FPGA devices. To Train and Validate our algorithms we developed a process to generate photorealistic 3D environments. Those 3D Environments are used to produce realistic video streams of the targets in different environmental conditions (lighting, adverse meteorological conditions, camouflage, point-of-view). The same technology can be used to Train and Test Automotive Vision Systems. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/imagingautomotive2015-addforv002-151111124100-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds" /> We develop custom Image Recognition systems for Aerospace and defence applications. Using algorithms like Deep Convolutional Neural Networks and Regional Convolutional Neural Networks. Our algorithms for Target Recognition and Tracking are designed from the beginning to be run on embedded systems. We target both GPU and FPGA devices. To Train and Validate our algorithms we developed a process to generate photorealistic 3D environments. Those 3D Environments are used to produce realistic video streams of the targets in different environmental conditions (lighting, adverse meteorological conditions, camouflage, point-of-view). The same technology can be used to Train and Test Automotive Vision Systems.

Imaging automotive 2015 addfor v002 from Enrico Busto

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0 https://cdn.slidesharecdn.com/profile-photo-bix883-48x48.jpg?cb=1725091525 I provide consultancy services to develop Artificial Intelligence solutions, boosting the Skills of your technical team with a tailor-made approach www.add-for.com https://cdn.slidesharecdn.com/ss_thumbnails/20191024ibmprague-ai-reallifeexperiencesinengagingcustomersanddobusiness-v001topdfcompressed-191108162207-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/ibm-prague-ai-real-life-experiences-in-engaging-customers-and-do-business-v001-to-pdf-compressed/191730191 IBM Prague ai - real... https://cdn.slidesharecdn.com/ss_thumbnails/20181210-superresolution-190517083701-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/20181210-super-resolution/146232510 20181210 Super Resolution https://cdn.slidesharecdn.com/ss_thumbnails/thesis22-inversereinforcementlearningforautonomousdriving-190404042557-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/masters-thesis-inverse-reinforcement-learning-for-autonomous-driving/139491322 Master's Thesis - inve...