�ݺ�ߣshows by User: AlexConway2

�ݺ�ߣshows by User: AlexConway2 / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: AlexConway2 / Fri, 11 Oct 2019 07:49:51 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: AlexConway2 PyConZA 2019 Keynote - Deep Neural Networks for Video Applications /slideshow/pyconza-2019-keynote-deep-neural-networks-for-video-applications/180945502 deepneuralnetworksforvideopyconza2019alexkeynote-191011074952
�ݺ�ߣs from my PyConZA 2019 Keynote on "Deep Neural Networks for Video Applications" Don't be afraid of A.I. ... git clone a relevant function (deep learning model), fine-tune it for your use case if required and use it to build cool things! I also do consulting if you get stuck or need help @@@ numberboost.com :P "Most CCTV video cameras exist as a sort of time machine for insurance purposes. Deep neural networks make it easy to convert video into actionable data which can be used to trigger real-time anomaly alerts and optimize complex business processes. In addition to commercial applications, deep learning can be used to analyze large amounts of video recorded from the point of view of animals to study complex behavior patterns impossible to otherwise analyze. This talk will present some theory of deep neural networks for video applications as well as academic research and several applied real-world industrial examples, with code examples in python." Note: links are hard to click in �ݺ�ߣShare but are clickable if you download PDF :) #deeplearning #machinelearning #deeplearningforvideo #convolutionalneuralnetworks #recurrentneuralnetworks #centroidtracking #objectdetection #deepfakes #poseestimation #videomachinelearning #numberboost ]]>
�ݺ�ߣs from my PyConZA 2019 Keynote on "Deep Neural Networks for Video Applications" Don't be afraid of A.I. ... git clone a relevant function (deep learning model), fine-tune it for your use case if required and use it to build cool things! I also do consulting if you get stuck or need help @@@ numberboost.com :P "Most CCTV video cameras exist as a sort of time machine for insurance purposes. Deep neural networks make it easy to convert video into actionable data which can be used to trigger real-time anomaly alerts and optimize complex business processes. In addition to commercial applications, deep learning can be used to analyze large amounts of video recorded from the point of view of animals to study complex behavior patterns impossible to otherwise analyze. This talk will present some theory of deep neural networks for video applications as well as academic research and several applied real-world industrial examples, with code examples in python." Note: links are hard to click in �ݺ�ߣShare but are clickable if you download PDF :) #deeplearning #machinelearning #deeplearningforvideo #convolutionalneuralnetworks #recurrentneuralnetworks #centroidtracking #objectdetection #deepfakes #poseestimation #videomachinelearning #numberboost ]]> Fri, 11 Oct 2019 07:49:51 GMT /slideshow/pyconza-2019-keynote-deep-neural-networks-for-video-applications/180945502 AlexConway2@slideshare.net(AlexConway2) PyConZA 2019 Keynote - Deep Neural Networks for Video Applications AlexConway2 �ݺ�ߣs from my PyConZA 2019 Keynote on "Deep Neural Networks for Video Applications" Don't be afraid of A.I. ... git clone a relevant function (deep learning model), fine-tune it for your use case if required and use it to build cool things! I also do consulting if you get stuck or need help @@@ numberboost.com :P "Most CCTV video cameras exist as a sort of time machine for insurance purposes. Deep neural networks make it easy to convert video into actionable data which can be used to trigger real-time anomaly alerts and optimize complex business processes. In addition to commercial applications, deep learning can be used to analyze large amounts of video recorded from the point of view of animals to study complex behavior patterns impossible to otherwise analyze. This talk will present some theory of deep neural networks for video applications as well as academic research and several applied real-world industrial examples, with code examples in python." Note: links are hard to click in �ݺ�ߣShare but are clickable if you download PDF :) #deeplearning #machinelearning #deeplearningforvideo #convolutionalneuralnetworks #recurrentneuralnetworks #centroidtracking #objectdetection #deepfakes #poseestimation #videomachinelearning #numberboost <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/deepneuralnetworksforvideopyconza2019alexkeynote-191011074952-thumbnail.jpg?width=120&height=120&fit=bounds" /> �ݺ�ߣs from my PyConZA 2019 Keynote on "Deep Neural Networks for Video Applications" Don't be afraid of A.I. ... git clone a relevant function (deep learning model), fine-tune it for your use case if required and use it to build cool things! I also do consulting if you get stuck or need help @@@ numberboost.com :P "Most CCTV video cameras exist as a sort of time machine for insurance purposes. Deep neural networks make it easy to convert video into actionable data which can be used to trigger real-time anomaly alerts and optimize complex business processes. In addition to commercial applications, deep learning can be used to analyze large amounts of video recorded from the point of view of animals to study complex behavior patterns impossible to otherwise analyze. This talk will present some theory of deep neural networks for video applications as well as academic research and several applied real-world industrial examples, with code examples in python." Note: links are hard to click in �ݺ�ߣShare but are clickable if you download PDF :) #deeplearning #machinelearning #deeplearningforvideo #convolutionalneuralnetworks #recurrentneuralnetworks #centroidtracking #objectdetection #deepfakes #poseestimation #videomachinelearning #numberboost

PyConZA 2019 Keynote - Deep Neural Networks for Video Applications from Alex Conway

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0 Deep Neural Networks for Video Applications at the Edge /slideshow/deep-neural-networks-for-video-applications-at-the-edge/146246340 deeplearningforvideooslomachinelearningnumberboost-190517115424
�ݺ�ߣs from my talk about deep learning for video applications and edge computing on 16 May 2019 at the Oslo Machine Learning Meetup on sponsored by InMeta! More details about the event here: https://www.meetup.com/Oslo-Maskinlaering/events/261318845/ I covered the following topics: * Neural network crash course * Convolutional neural networks * Recurrent neural networks * Object detection * Counting people getting in/out transport using deep learning * Edge computing * Creating labelled training datasets using sebenz.ai ]]>
�ݺ�ߣs from my talk about deep learning for video applications and edge computing on 16 May 2019 at the Oslo Machine Learning Meetup on sponsored by InMeta! More details about the event here: https://www.meetup.com/Oslo-Maskinlaering/events/261318845/ I covered the following topics: * Neural network crash course * Convolutional neural networks * Recurrent neural networks * Object detection * Counting people getting in/out transport using deep learning * Edge computing * Creating labelled training datasets using sebenz.ai ]]> Fri, 17 May 2019 11:54:24 GMT /slideshow/deep-neural-networks-for-video-applications-at-the-edge/146246340 AlexConway2@slideshare.net(AlexConway2) Deep Neural Networks for Video Applications at the Edge AlexConway2 �ݺ�ߣs from my talk about deep learning for video applications and edge computing on 16 May 2019 at the Oslo Machine Learning Meetup on sponsored by InMeta! More details about the event here: https://www.meetup.com/Oslo-Maskinlaering/events/261318845/ I covered the following topics: * Neural network crash course * Convolutional neural networks * Recurrent neural networks * Object detection * Counting people getting in/out transport using deep learning * Edge computing * Creating labelled training datasets using sebenz.ai <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/deeplearningforvideooslomachinelearningnumberboost-190517115424-thumbnail.jpg?width=120&height=120&fit=bounds" /> �ݺ�ߣs from my talk about deep learning for video applications and edge computing on 16 May 2019 at the Oslo Machine Learning Meetup on sponsored by InMeta! More details about the event here: https://www.meetup.com/Oslo-Maskinlaering/events/261318845/ I covered the following topics: * Neural network crash course * Convolutional neural networks * Recurrent neural networks * Object detection * Counting people getting in/out transport using deep learning * Edge computing * Creating labelled training datasets using sebenz.ai

Deep Neural Networks for Video Applications at the Edge from Alex Conway

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0 Machine Learning Tokyo - Deep Neural Networks for Video - NumberBoost /slideshow/machine-learning-tokyo-deep-neural-networks-for-video-numberboost/137018342 numberboostdl4videotokyo-190318180110
�ݺ�ߣs from a talk I gave at the Machine Learning Tokyo meetup group on 20190318. More info here: https://www.meetup.com/Machine-Learning-Tokyo/events/259467268/ Feel free to reach out if you ever need to build a computer vision system or need data labelled to train machine learning models :) www.numberboost.com]]>
�ݺ�ߣs from a talk I gave at the Machine Learning Tokyo meetup group on 20190318. More info here: https://www.meetup.com/Machine-Learning-Tokyo/events/259467268/ Feel free to reach out if you ever need to build a computer vision system or need data labelled to train machine learning models :) www.numberboost.com]]> Mon, 18 Mar 2019 18:01:10 GMT /slideshow/machine-learning-tokyo-deep-neural-networks-for-video-numberboost/137018342 AlexConway2@slideshare.net(AlexConway2) Machine Learning Tokyo - Deep Neural Networks for Video - NumberBoost AlexConway2 �ݺ�ߣs from a talk I gave at the Machine Learning Tokyo meetup group on 20190318. More info here: https://www.meetup.com/Machine-Learning-Tokyo/events/259467268/ Feel free to reach out if you ever need to build a computer vision system or need data labelled to train machine learning models :) www.numberboost.com <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/numberboostdl4videotokyo-190318180110-thumbnail.jpg?width=120&height=120&fit=bounds" /> �ݺ�ߣs from a talk I gave at the Machine Learning Tokyo meetup group on 20190318. More info here: https://www.meetup.com/Machine-Learning-Tokyo/events/259467268/ Feel free to reach out if you ever need to build a computer vision system or need data labelled to train machine learning models :) www.numberboost.com

Machine Learning Tokyo - Deep Neural Networks for Video - NumberBoost from Alex Conway

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0 Deep Neural Networks for Computer Vision /slideshow/deep-neural-networks-for-computer-vision/133666138 numberboostdl4video20190226jhbai-190228081821
�ݺ�ߣs from talk I gave on deep learning and computer vision for surveillance and other video analytics at the Johannesburg AI Meetup on 20190226]]>
�ݺ�ߣs from talk I gave on deep learning and computer vision for surveillance and other video analytics at the Johannesburg AI Meetup on 20190226]]> Thu, 28 Feb 2019 08:18:21 GMT /slideshow/deep-neural-networks-for-computer-vision/133666138 AlexConway2@slideshare.net(AlexConway2) Deep Neural Networks for Computer Vision AlexConway2 �ݺ�ߣs from talk I gave on deep learning and computer vision for surveillance and other video analytics at the Johannesburg AI Meetup on 20190226 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/numberboostdl4video20190226jhbai-190228081821-thumbnail.jpg?width=120&height=120&fit=bounds" /> �ݺ�ߣs from talk I gave on deep learning and computer vision for surveillance and other video analytics at the Johannesburg AI Meetup on 20190226

Deep Neural Networks for Computer Vision from Alex Conway

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0 Deep Learning for Computer Vision - PyconDE 2017 /slideshow/deep-learning-for-computer-vision-pyconde-2017/81184773 pyconde17-171025091553
�ݺ�ߣs from my talk on "Deep Learning for Computer Vision" at PyConDE 2017 in Karlsruhe, Germany. ]]>
�ݺ�ߣs from my talk on "Deep Learning for Computer Vision" at PyConDE 2017 in Karlsruhe, Germany. ]]> Wed, 25 Oct 2017 09:15:53 GMT /slideshow/deep-learning-for-computer-vision-pyconde-2017/81184773 AlexConway2@slideshare.net(AlexConway2) Deep Learning for Computer Vision - PyconDE 2017 AlexConway2 �ݺ�ߣs from my talk on "Deep Learning for Computer Vision" at PyConDE 2017 in Karlsruhe, Germany. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/pyconde17-171025091553-thumbnail.jpg?width=120&height=120&fit=bounds" /> �ݺ�ߣs from my talk on "Deep Learning for Computer Vision" at PyConDE 2017 in Karlsruhe, Germany.

Deep Learning for Computer Vision - PyconDE 2017 from Alex Conway

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0 PyConZA'17 Deep Learning for Computer Vision /slideshow/pyconza17-deep-learning-for-computer-vision/80525153 pyconza17dl4cvslides-171006092843
�ݺ�ߣs from my talk on deep learning for computer vision at PyConZA on 2017/10/06. Description: The state-of-the-art in image classification has skyrocketed thanks to the development of deep convolutional neural networks and increases in the amount of data and computing power available to train them. The top-5 error rate in the ImageNet competition to predict which of 1000 classes an image belongs to has plummeted from 28% error in 2010 to just 2.25% in 2017 (human level error is around 5%). In addition to being able to classify objects in images (including not hotdogs), deep learning can be used to automatically generate captions for images, convert photos into paintings, detect cancer in pathology slide images, and help self-driving cars ‘see’. The talk will give an overview of the cutting edge and some of the core mathematical concepts and will also include a short code-first tutorial to show how easy it is to get started using deep learning for computer vision in python…]]>
�ݺ�ߣs from my talk on deep learning for computer vision at PyConZA on 2017/10/06. Description: The state-of-the-art in image classification has skyrocketed thanks to the development of deep convolutional neural networks and increases in the amount of data and computing power available to train them. The top-5 error rate in the ImageNet competition to predict which of 1000 classes an image belongs to has plummeted from 28% error in 2010 to just 2.25% in 2017 (human level error is around 5%). In addition to being able to classify objects in images (including not hotdogs), deep learning can be used to automatically generate captions for images, convert photos into paintings, detect cancer in pathology slide images, and help self-driving cars ‘see’. The talk will give an overview of the cutting edge and some of the core mathematical concepts and will also include a short code-first tutorial to show how easy it is to get started using deep learning for computer vision in python…]]> Fri, 06 Oct 2017 09:28:43 GMT /slideshow/pyconza17-deep-learning-for-computer-vision/80525153 AlexConway2@slideshare.net(AlexConway2) PyConZA'17 Deep Learning for Computer Vision AlexConway2 �ݺ�ߣs from my talk on deep learning for computer vision at PyConZA on 2017/10/06. Description: The state-of-the-art in image classification has skyrocketed thanks to the development of deep convolutional neural networks and increases in the amount of data and computing power available to train them. The top-5 error rate in the ImageNet competition to predict which of 1000 classes an image belongs to has plummeted from 28% error in 2010 to just 2.25% in 2017 (human level error is around 5%). In addition to being able to classify objects in images (including not hotdogs), deep learning can be used to automatically generate captions for images, convert photos into paintings, detect cancer in pathology slide images, and help self-driving cars ‘see’. The talk will give an overview of the cutting edge and some of the core mathematical concepts and will also include a short code-first tutorial to show how easy it is to get started using deep learning for computer vision in python… <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/pyconza17dl4cvslides-171006092843-thumbnail.jpg?width=120&height=120&fit=bounds" /> �ݺ�ߣs from my talk on deep learning for computer vision at PyConZA on 2017/10/06. Description: The state-of-the-art in image classification has skyrocketed thanks to the development of deep convolutional neural networks and increases in the amount of data and computing power available to train them. The top-5 error rate in the ImageNet competition to predict which of 1000 classes an image belongs to has plummeted from 28% error in 2010 to just 2.25% in 2017 (human level error is around 5%). In addition to being able to classify objects in images (including not hotdogs), deep learning can be used to automatically generate captions for images, convert photos into paintings, detect cancer in pathology slide images, and help self-driving cars ‘see’. The talk will give an overview of the cutting edge and some of the core mathematical concepts and will also include a short code-first tutorial to show how easy it is to get started using deep learning for computer vision in python…

PyConZA'17 Deep Learning for Computer Vision from Alex Conway

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0 Deep Learning for Computer Vision - ExecutiveML /slideshow/deep-learning-for-computer-vision-executiveml/80016137 dl4cvexemlslides-170921121930
�ݺ�ߣs from my talk about deep learning & computer vision at the Executive-ML conference in Johannesburg on 2017/09/21 :)]]>
�ݺ�ߣs from my talk about deep learning & computer vision at the Executive-ML conference in Johannesburg on 2017/09/21 :)]]> Thu, 21 Sep 2017 12:19:29 GMT /slideshow/deep-learning-for-computer-vision-executiveml/80016137 AlexConway2@slideshare.net(AlexConway2) Deep Learning for Computer Vision - ExecutiveML AlexConway2 �ݺ�ߣs from my talk about deep learning & computer vision at the Executive-ML conference in Johannesburg on 2017/09/21 :) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/dl4cvexemlslides-170921121930-thumbnail.jpg?width=120&height=120&fit=bounds" /> �ݺ�ߣs from my talk about deep learning & computer vision at the Executive-ML conference in Johannesburg on 2017/09/21 :)

Deep Learning for Computer Vision - ExecutiveML from Alex Conway

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0 Convolutional Neural Networks for Computer vision Applications /slideshow/convolutional-neural-networks-for-computer-vision-applications/79816867 dl4cvslides-170915173401
�ݺ�ߣs from my presentation at the Machine Intelligence Institute of Africa meet-up / IBM Deep Learning Hackathon Info Session. ]]>
�ݺ�ߣs from my presentation at the Machine Intelligence Institute of Africa meet-up / IBM Deep Learning Hackathon Info Session. ]]> Fri, 15 Sep 2017 17:34:00 GMT /slideshow/convolutional-neural-networks-for-computer-vision-applications/79816867 AlexConway2@slideshare.net(AlexConway2) Convolutional Neural Networks for Computer vision Applications AlexConway2 �ݺ�ߣs from my presentation at the Machine Intelligence Institute of Africa meet-up / IBM Deep Learning Hackathon Info Session. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/dl4cvslides-170915173401-thumbnail.jpg?width=120&height=120&fit=bounds" /> �ݺ�ߣs from my presentation at the Machine Intelligence Institute of Africa meet-up / IBM Deep Learning Hackathon Info Session.

Convolutional Neural Networks for Computer vision Applications from Alex Conway

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0 PyDresden 20170824 - Deep Learning for Computer Vision /AlexConway2/py-dresden-dl4cvslides pydresden-dl4cvslides-170824205250
�ݺ�ߣs from my talk at PyDresden The state-of-the-art in image classification has skyrocketed thanks to the development of deep convolutional neural networks and increases in the amount of data and computing power available to train them. The top-5 error rate in the international ImageNet competition to predict which of 1000 classes an image belongs to has plummeted from 28% error in 2010 before deep learning to just 2.25% in 2017 (human level error is around 5%). In addition to being able to classify objects in images (including not hotdogs), deep learning can be used to automatically generate captions for images, convert photos into paintings, detect cancer in pathology slide images, and help self-driving cars ‘see’. The talk will give an overview of the cutting edge in the field and some of the core mathematical concepts behind the models. It will also include a short code-first tutorial to show how easy it is to get started using deep learning for computer vision in python… ]]>
�ݺ�ߣs from my talk at PyDresden The state-of-the-art in image classification has skyrocketed thanks to the development of deep convolutional neural networks and increases in the amount of data and computing power available to train them. The top-5 error rate in the international ImageNet competition to predict which of 1000 classes an image belongs to has plummeted from 28% error in 2010 before deep learning to just 2.25% in 2017 (human level error is around 5%). In addition to being able to classify objects in images (including not hotdogs), deep learning can be used to automatically generate captions for images, convert photos into paintings, detect cancer in pathology slide images, and help self-driving cars ‘see’. The talk will give an overview of the cutting edge in the field and some of the core mathematical concepts behind the models. It will also include a short code-first tutorial to show how easy it is to get started using deep learning for computer vision in python… ]]> Thu, 24 Aug 2017 20:52:50 GMT /AlexConway2/py-dresden-dl4cvslides AlexConway2@slideshare.net(AlexConway2) PyDresden 20170824 - Deep Learning for Computer Vision AlexConway2 �ݺ�ߣs from my talk at PyDresden The state-of-the-art in image classification has skyrocketed thanks to the development of deep convolutional neural networks and increases in the amount of data and computing power available to train them. The top-5 error rate in the international ImageNet competition to predict which of 1000 classes an image belongs to has plummeted from 28% error in 2010 before deep learning to just 2.25% in 2017 (human level error is around 5%). In addition to being able to classify objects in images (including not hotdogs), deep learning can be used to automatically generate captions for images, convert photos into paintings, detect cancer in pathology slide images, and help self-driving cars ‘see’. The talk will give an overview of the cutting edge in the field and some of the core mathematical concepts behind the models. It will also include a short code-first tutorial to show how easy it is to get started using deep learning for computer vision in python… <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/pydresden-dl4cvslides-170824205250-thumbnail.jpg?width=120&height=120&fit=bounds" /> �ݺ�ߣs from my talk at PyDresden The state-of-the-art in image classification has skyrocketed thanks to the development of deep convolutional neural networks and increases in the amount of data and computing power available to train them. The top-5 error rate in the international ImageNet competition to predict which of 1000 classes an image belongs to has plummeted from 28% error in 2010 before deep learning to just 2.25% in 2017 (human level error is around 5%). In addition to being able to classify objects in images (including not hotdogs), deep learning can be used to automatically generate captions for images, convert photos into paintings, detect cancer in pathology slide images, and help self-driving cars ‘see’. The talk will give an overview of the cutting edge in the field and some of the core mathematical concepts behind the models. It will also include a short code-first tutorial to show how easy it is to get started using deep learning for computer vision in python…

PyDresden 20170824 - Deep Learning for Computer Vision from Alex Conway

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0 Convolutional Neural Networks for Image Classification (Cape Town Deep Learning Meet-up 20170620) /slideshow/convolutional-neural-networks-for-image-classification-cape-town-deep-learning-meetup-20170620/77145645 ctdlcnntalk20170620-170621151937
�ݺ�ߣs for my talk on: "Convolutional Neural Networks for Image Classification" ...at the Cape Town Deep Learning Meet-up 20170620 https://www.meetup.com/Cape-Town-deep-learning/events/240485642/]]>
�ݺ�ߣs for my talk on: "Convolutional Neural Networks for Image Classification" ...at the Cape Town Deep Learning Meet-up 20170620 https://www.meetup.com/Cape-Town-deep-learning/events/240485642/]]> Wed, 21 Jun 2017 15:19:37 GMT /slideshow/convolutional-neural-networks-for-image-classification-cape-town-deep-learning-meetup-20170620/77145645 AlexConway2@slideshare.net(AlexConway2) Convolutional Neural Networks for Image Classification (Cape Town Deep Learning Meet-up 20170620) AlexConway2 �ݺ�ߣs for my talk on: "Convolutional Neural Networks for Image Classification" ...at the Cape Town Deep Learning Meet-up 20170620 https://www.meetup.com/Cape-Town-deep-learning/events/240485642/ <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ctdlcnntalk20170620-170621151937-thumbnail.jpg?width=120&height=120&fit=bounds" /> �ݺ�ߣs for my talk on: "Convolutional Neural Networks for Image Classification" ...at the Cape Town Deep Learning Meet-up 20170620 https://www.meetup.com/Cape-Town-deep-learning/events/240485642/

Convolutional Neural Networks for Image Classification (Cape Town Deep Learning Meet-up 20170620) from Alex Conway

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0 Machine Learning: A 60 Minute Crash Course - IOPOWWOW 20170526 /AlexConway2/machine-learning-a-60-minute-crash-course-iopowwow-20170526 mlccio20170526-170528193945
Machine Learning: A 60 Minute Crash Course Presented on 20170526 at the IO-POWWOW Meetup in Cape Town https://www.meetup.com/IO-Powwow/events/239505194/ :)]]>
Machine Learning: A 60 Minute Crash Course Presented on 20170526 at the IO-POWWOW Meetup in Cape Town https://www.meetup.com/IO-Powwow/events/239505194/ :)]]> Sun, 28 May 2017 19:39:45 GMT /AlexConway2/machine-learning-a-60-minute-crash-course-iopowwow-20170526 AlexConway2@slideshare.net(AlexConway2) Machine Learning: A 60 Minute Crash Course - IOPOWWOW 20170526 AlexConway2 Machine Learning: A 60 Minute Crash Course Presented on 20170526 at the IO-POWWOW Meetup in Cape Town https://www.meetup.com/IO-Powwow/events/239505194/ :) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/mlccio20170526-170528193945-thumbnail.jpg?width=120&height=120&fit=bounds" /> Machine Learning: A 60 Minute Crash Course Presented on 20170526 at the IO-POWWOW Meetup in Cape Town https://www.meetup.com/IO-Powwow/events/239505194/ :)

Machine Learning: A 60 Minute Crash Course - IOPOWWOW 20170526 from Alex Conway

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