SlideShare a Scribd company logo

Python for Image Understanding: Deep Learning with Convolutional Neural Nets

•
•
Roelof Pieters
Roelof Pieters

Talk given at PyData 2015 London. June 21, 2015. Practical approach to how to train your deep neural net for images.

Presentations & Public Speaking
1 of 100
Download to read offline
for Image Understanding: Deep Learning with Convolutional Neural Nets Roelof Pieters PhD candidate at KTH & Data Science consultant at Graph Technologies  @graphific London 2015roelof@graph-technologies.com
What is Deep Learning?
“Deep learning is a set of algorithms in machine learning that attempt to learn in multiple levels, corresponding to different levels of abstraction.” (much debated definition) A Definition
• A host of statistical machine learning techniques • Enables the automatic learning of feature hierarchies • Generally based on artificial neural networks A typology Deep learning is:
Manually designed features are often over-specified, incomplete and take a long time to design and validate Learned features are easy to adapt, fast to learn Deep learning provides a very flexible, (possibly?) universal, learnable framework for representing world, visual and linguistic information. Deep learning can learn unsupervised (from raw text/ audio/images/whatever content) and supervised (with specific labels like positive/negative)
 Summary by Richard Socher. Old vs new school?
No More Handcrafted Features ! 6
“Brain”-like: Feature Hierarchies
input layer output layer hidden layers Feature Hierarchies: Vision
input layer output layer hidden layers Feature Hierarchies: Audio
cars elephants chairs Feature Hierarchies: And so on…
History: audio recognition chart by Clarifai
History: image recognition chart by Clarifai
History: image recognition Krizhevsky et al. ImageNet Classification with Deep Convolutional Neural Networks, ILSVRC2010
16 Karpathy, A., Fei Fei, L. (2015) 
 Deep Visual-Semantic Alignments for Generating Image Descriptions Image-Text: Joint Visual Semantic embeddings
Video Scene Detection
http://googleresearch.blogspot.co.uk/2015/06/inceptionism-going-deeper-into-neural.html
http://googleresearch.blogspot.co.uk/2015/06/inceptionism-going-deeper-into-neural.html
How does Deep Learning work?
Activation Functions Sigmoid
Activation Functions Tanh
Activation Functions ReLU often approximated by just
optimization strategies
So what’s there for Python?
python has a wide range of deep learning-related libraries available Deep Learning with Python Low level High level (efficient gpu-powered math) (computer-vision oriented DL framework,
 model-zoo, prototxt model definitions) 
 pythonification ongoing! (wrapper for theano, yaml, experiment-oriented) (theano-wrapper, models in python code, 
 abstracts theano away) (theano-extension, models in python code, 
 theano not hidden) and of course:
python has a wide range of deep learning-related libraries available Deep Learning with Python Low level High level deeplearning.net/software/theano caffe.berkeleyvision.org deeplearning.net/software/pylearn2 keras.io lasagne.readthedocs.org/en/latest and of course:
python has a wide range of deep learning-related libraries available Deep Learning with Python Low level High level deeplearning.net/software/theano caffe.berkeleyvision.org deeplearning.net/software/pylearn2 keras.io lasagne.readthedocs.org/en/latest and of course: we will use lasagne in our examples
Doing Deep Learning?
1. Preprocess the data 2. Choose architecture 3. Train 4. Optimize/Regularize 5. Tips/Tricks Training a (deep) Neural Network
1. Preprocess the data 2. Choose architecture 3. Train 4. Optimize/Regularize 5. Tips/Tricks Training a (deep) Neural Network
• Mean subtraction • Normalization • PCA and Whitening 1. Preprocess the data
• Mean subtraction • Normalization • PCA and Whitening 1. Preprocess the data: Normalization (mean image visualised of cifar-10)
• Mean subtraction • Normalization • PCA and Whitening 1. Preprocess the data: PCA & Whitening
1. Preprocess the data: PCA & Whitening
1. Preprocess the data, the right way Warning: • compute preprocessing statistics on training data • apply on all (training / validation / test) data
1. Preprocess the data 2. Choose architecture 3. Train 4. Optimize/Regularize 5. Tips/Tricks Training a (deep) Neural Network
• Deep Belief Network (DBN) • Convolutional Net (CNN) • Recurrent Net (RNN) 2. Choosing the right architecture
• Deep Belief Network (DBN) • Convolutional Net (CNN) • Recurrent Net (RNN) 2. Choosing the right architecture
• Deep Belief Network (DBN) • Convolutional Net (CNN) • Recurrent Net (RNN) 2. Choosing the right architecture
• Deep Belief Network (DBN) • Convolutional Net (CNN) • Recurrent Net (RNN) 2. Choosing the right architecture
Convolutional Neural Net Pbird Psunset Pdog Pcat
Convolutional Neural Net
DrawCNN: visualizing the units' connections Agrawal, et al. Analyzing the performance of multilayer neural networks for object recognition. ECCV, 2014 Szegedy, et al. Intriguing properties of neural networks.arXiv preprint arXiv:1312.6199, 2013 Zeiler, M. et al. Visualizing and Understanding Convolutional Networks, ECCV 2014
1. Preprocess the data 2. Choose architecture 3. Train 4. Optimize/Regularize 5. Tips/Tricks Training a (deep) Neural Network
1. Preprocess the data 2. Choose architecture 3. Train (Code Finally!) 4. Optimize/Regularize 5. Tips/Tricks Training a (deep) Neural Network
Training a (deep) Neural Network (…) layer definitions layer
 parameters
Training a (deep) Neural Network hyper
 parameters
Training a (deep) Neural Network Lift off!
Training a (deep) Neural Network 1. Preprocess the data 2. Choose architecture 3. Train 4. Optimize/Regularize 5. Tips/Tricks Debug
Debug Training: Visualize Loss Curve
Debug Training: Visualize Loss Curve
Debug Training: Visualize Loss Curve This looks (too) linear: lower your learning rate! This looks (too) wide: increase your batch size!
Debug Training: Visualize Accuracy big gap: overfitting: regularize! no gap: underfitting (increase model size)
Debug Training: Visualize Weights (usually: first layer) complete mess, doesn't get past the random initialisation
Debug Training: Visualize Weights (usually: first layer) better, but still “noisy” weights mostly solvable by stronger regularisation
Debug Training: Visualize Weights (usually: first layer) good: now operates as “edge detector”
Training a (deep) Neural Network 1. Preprocess the data 2. Choose architecture 3. Train 4. Optimize/Regularize 5. Tips/Tricks Debug
• Tweak Hyperparameters / Architecture • Data Augmentation • Dropout • Batch Normalization Optimize / Regularize
• Tweak Hyperparameters / Architecture • Data Augmentation • Dropout • Batch Normalization Optimize / Regularize
• Grid search won't work on your millions + parameters • Random Search? Mwah… • Bayesian Optimization: Yeh baby! • Spearmint: https://github.com/HIPS/Spearmint • Hypergrad: https://github.com/HIPS/hypergrad Choosing Hyperparameters
• Tweak Hyperparameters / Architecture • Data Augmentation • Dropout • Batch Normalization Overfitting
Data Augmentation
Data Augmentation http://benanne.github.io/2015/03/17/plankton.html (realtime data augmentation at Kaggle’s #1 National Data Science Bowl
 ≋ Deep Sea ≋ team) rotation: random with angle between 0° and 360° (uniform) translation: random with shift between -10 and 10 pixels (uniform) rescaling: random with scale factor between 1/1.6 and 1.6 (log- uniform) flipping: yes or no (bernoulli) shearing: random with angle between -20° and 20° (uniform) stretching: random with stretch factor between 1/1.3 and 1.3 (log- uniform)
• Tweak Hyperparameters / Architecture • Data Augmentation • Dropout • Batch Normalization Optimize / Regularize
Dropout as Regularization (naively trained net) Overfits !
Dropout as Regularization (naively trained net) Overfits ! Dropout!
Dropout as Regularization (naively trained net) Overfits ! Dropout!
Dropout as Regularization (naively trained net) (net with dropout) less strongly overfitted & 
 can run for more epochs higher accuracy
Overfitting • Tweak Hyperparameters / Architecture • Data Augmentation • Dropout • Batch Normalization
•Normalize the activations in each layer within a minibatch •Learn the mean and variance of each layer as parameters Batch Normalization as regularization Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift 
 S Ioffe and C Szegedy (2015) 85% 50% 15%
Training a (deep) Neural Network 1. Preprocess the data 2. Choose architecture 3. Train 4. Optimize/Regularize 5. Further Tips & Tricks to improve Model Accuracy Debug
• Ensembles • Finetuning pre-trained/earlier-trained net • Sticking extracted layer features in another classifier (ie SVM) Other “Tricks”
• Ensembles • Finetuning pre-trained/earlier-trained net • Sticking extracted layer features in another classifier (ie SVM) Other “Tricks”
Ensembles • majority vote when hard predictions (ie classes) • average vote for soft predictions (continious scale) • make sure classifiers are uncorrelated • cross validate ensemble weights (by grid search, or rank average) • stacked • blending
Ensembles (10 similar nets with varying hyperparameters on same tiny-imagenet dataset)
avg: 0.3647 Ensembles (10 similar nets with varying hyperparameters on same tiny-imagenet dataset)
predict by mean of all: 0.4244 avg: 0.3647 Ensembles (10 similar nets with varying hyperparameters on same tiny-imagenet dataset)
(10 similar nets with varying hyperparameters on same tiny-imagenet dataset) predict by mean of all: 0.4244 leave out model9: 0.4259 avg: 0.3647 Ensembles
(10 similar nets with varying hyperparameters on same tiny-imagenet dataset) 0.4259 0.4244 0.3647 Ensembles
danielnouri.org/notes/2014/12/17/using-convolutional-neural-nets-to-detect-facial-keypoints-tutorial/ (ensembling specialist nets by Daniel Nouri, Kaggle facial keypoint tutorial) Ensembles “Specialists” Ensemble
https://www.kaggle.com/c/facial-keypoints-detection/ try it yourself :) 3 similar nets trained on the same data but with different hyper parameters.
 RMSE’s: •2,08449 •2,04575 •2.01565 together: 
 1.93397 disclaimer: Kaggle is not real life, people…
Machine learning systems can easily be fooled but beware… / no free lunch:
Saliency Maps K. Simonyan, A. Vedaldi, A. Zisserman , "Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps", ICLR Workshop 2014 first we predict on a pixel level
Fooling ConvNets Szegedy, Christian, et al. "Intriguing properties of neural networks." arXiv preprint, 2013. Nguyen, Anh, Jason Yosinski, and Jeff Clune. "Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images." arXiv preprint then we do our “magic”
Fooling ConvNets Szegedy, Christian, et al. "Intriguing properties of neural networks." arXiv preprint, 2013. Nguyen, Anh, Jason Yosinski, and Jeff Clune. "Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images." arXiv preprint then we do our “magic”
Failing ConvNets
Failing ConvNets “Suddenly, a leopard print sofa appears”, rocknrollnerd.github.io
Failing ConvNets “Suddenly, a leopard print sofa appears”, rocknrollnerd.github.io
Failing ConvNets “Suddenly, a leopard print sofa appears”, rocknrollnerd.github.io
Failing ConvNets
thanks for listening ;) questions? or find me @graphific
• Computer Vision: 
 Fei-Fei Li & Andrej Karpathy, Stanford course “Convolutional Neural Networks for Visual Recognition”
 http://vision.stanford.edu/teaching/cs231n • Natural Language Processing:
 Richard Socher, Stanford course “Deep Learning for Natural Language Processing”,
 http://cs224d.stanford.edu/ • Neural Nets:
 Geoffrey Hinton, Coursera/Toronto, “Neural Networks for Machine Learning"
 https://www.coursera.org/course/neuralnets Wanna Play? • Bunch of tutorials: 
 http://deeplearning.net/tutorial/ • Book:
 Yoshua Bengio, et al, “Deep Learning”
 http://www.iro.umontreal.ca/~bengioy/dlbook/ • UFLDL Tutorial
 http://deeplearning.stanford.edu/tutorial/ • Reading Lists:
 http://deeplearning.net/reading-list/ 
 http://memkite.com/deep-learning-bibliography/ • Podcast
 Talking Machines, http://www.thetalkingmachines.com/

Recommended

Deep Learning - Convolutional Neural Networks
Deep Learning - Convolutional Neural NetworksDeep Learning - Convolutional Neural Networks
Deep Learning - Convolutional Neural Networks
Christian Perone
 
Intro to Deep Learning for Computer Vision
Intro to Deep Learning for Computer VisionIntro to Deep Learning for Computer Vision
Intro to Deep Learning for Computer Vision
Christoph Körner
 
The Deep Learning Glossary
The Deep Learning GlossaryThe Deep Learning Glossary
The Deep Learning Glossary
NVIDIA
 
Introduction to Deep learning
Introduction to Deep learningIntroduction to Deep learning
Introduction to Deep learning
leopauly
 
Convolutional neural network
Convolutional neural network Convolutional neural network
Convolutional neural network
Yan Xu
 
Introduction to Deep Learning, Keras, and TensorFlow
Introduction to Deep Learning, Keras, and TensorFlowIntroduction to Deep Learning, Keras, and TensorFlow
Introduction to Deep Learning, Keras, and TensorFlow
Sri Ambati
 
TensorFlow
TensorFlowTensorFlow
TensorFlow
Sang-Houn Choi
 
Neural networks and deep learning
Neural networks and deep learningNeural networks and deep learning
Neural networks and deep learning
Jörgen Sandig
 

Recommended

Deep Learning - Convolutional Neural Networks
Deep Learning - Convolutional Neural NetworksDeep Learning - Convolutional Neural Networks
Deep Learning - Convolutional Neural Networks
Christian Perone
 
Intro to Deep Learning for Computer Vision
Intro to Deep Learning for Computer VisionIntro to Deep Learning for Computer Vision
Intro to Deep Learning for Computer Vision
Christoph Körner
 
The Deep Learning Glossary
The Deep Learning GlossaryThe Deep Learning Glossary
The Deep Learning Glossary
NVIDIA
 
Introduction to Deep learning
Introduction to Deep learningIntroduction to Deep learning
Introduction to Deep learning
leopauly
 
Convolutional neural network
Convolutional neural network Convolutional neural network
Convolutional neural network
Yan Xu
 
Introduction to Deep Learning, Keras, and TensorFlow
Introduction to Deep Learning, Keras, and TensorFlowIntroduction to Deep Learning, Keras, and TensorFlow
Introduction to Deep Learning, Keras, and TensorFlow
Sri Ambati
 
TensorFlow
TensorFlowTensorFlow
TensorFlow
Sang-Houn Choi
 
Neural networks and deep learning
Neural networks and deep learningNeural networks and deep learning
Neural networks and deep learning
Jörgen Sandig
 
Convolutional neural network in practice
Convolutional neural network in practiceConvolutional neural network in practice
Convolutional neural network in practice
남주 김
 
Deep learning ppt
Deep learning pptDeep learning ppt
Deep learning ppt
BalneSridevi
 
Convolutional Neural Network (CNN) - image recognition
Convolutional Neural Network (CNN) - image recognitionConvolutional Neural Network (CNN) - image recognition
Convolutional Neural Network (CNN) - image recognition
YUNG-KUEI CHEN
 
What is Deep Learning | Deep Learning Simplified | Deep Learning Tutorial | E...
What is Deep Learning | Deep Learning Simplified | Deep Learning Tutorial | E...What is Deep Learning | Deep Learning Simplified | Deep Learning Tutorial | E...
What is Deep Learning | Deep Learning Simplified | Deep Learning Tutorial | E...
Edureka!
 
Convolutional Neural Networks (CNN)
Convolutional Neural Networks (CNN)Convolutional Neural Networks (CNN)
Convolutional Neural Networks (CNN)
Gaurav Mittal
 
Tutorial on Deep Learning
Tutorial on Deep LearningTutorial on Deep Learning
Tutorial on Deep Learning
inside-BigData.com
 
An introduction to Deep Learning
An introduction to Deep LearningAn introduction to Deep Learning
An introduction to Deep Learning
Julien SIMON
 
Convolutional Neural Network
Convolutional Neural NetworkConvolutional Neural Network
Convolutional Neural Network
Vignesh Suresh
 
Applying your Convolutional Neural Networks
Applying your Convolutional Neural NetworksApplying your Convolutional Neural Networks
Applying your Convolutional Neural Networks
Databricks
 
Deep Learning: Application & Opportunity
Deep Learning: Application & OpportunityDeep Learning: Application & Opportunity
Deep Learning: Application & Opportunity
iTrain
 
Few shot learning/ one shot learning/ machine learning
Few shot learning/ one shot learning/ machine learningFew shot learning/ one shot learning/ machine learning
Few shot learning/ one shot learning/ machine learning
ﺁﺻﻒ ﻋﻠﯽ ﻣﯿﺮ
 
Recurrent neural networks rnn
Recurrent neural networks rnnRecurrent neural networks rnn
Recurrent neural networks rnn
Kuppusamy P
 
Intro to deep learning
Intro to deep learning Intro to deep learning
Intro to deep learning
David Voyles
 
Introduction to CNN
Introduction to CNNIntroduction to CNN
Introduction to CNN
Shuai Zhang
 
Facenet - Paper Review
Facenet - Paper ReviewFacenet - Paper Review
Facenet - Paper Review
Tess Ferrandez
 
GPU and Deep learning best practices
GPU and Deep learning best practicesGPU and Deep learning best practices
GPU and Deep learning best practices
Lior Sidi
 
Deep learning - A Visual Introduction
Deep learning - A Visual IntroductionDeep learning - A Visual Introduction
Deep learning - A Visual Introduction
Lukas Masuch
 
Graph neural networks overview
Graph neural networks overviewGraph neural networks overview
Graph neural networks overview
Rodion Kiryukhin
 
PyTorch Introduction
PyTorch IntroductionPyTorch Introduction
PyTorch Introduction
Yash Kawdiya
 
Convolutional neural network
Convolutional neural networkConvolutional neural network
Convolutional neural network
Ferdous ahmed
 
Deep neural networks
Deep neural networksDeep neural networks
Deep neural networks
Si Haem
 
Deep learning - Conceptual understanding and applications
Deep learning - Conceptual understanding and applicationsDeep learning - Conceptual understanding and applications
Deep learning - Conceptual understanding and applications
Buhwan Jeong
 

More Related Content

What's hot

Convolutional neural network in practice
Convolutional neural network in practiceConvolutional neural network in practice
Convolutional neural network in practice
남주 김
 
Deep learning ppt
Deep learning pptDeep learning ppt
Deep learning ppt
BalneSridevi
 
Convolutional Neural Network (CNN) - image recognition
Convolutional Neural Network (CNN) - image recognitionConvolutional Neural Network (CNN) - image recognition
Convolutional Neural Network (CNN) - image recognition
YUNG-KUEI CHEN
 
What is Deep Learning | Deep Learning Simplified | Deep Learning Tutorial | E...
What is Deep Learning | Deep Learning Simplified | Deep Learning Tutorial | E...What is Deep Learning | Deep Learning Simplified | Deep Learning Tutorial | E...
What is Deep Learning | Deep Learning Simplified | Deep Learning Tutorial | E...
Edureka!
 
Convolutional Neural Networks (CNN)
Convolutional Neural Networks (CNN)Convolutional Neural Networks (CNN)
Convolutional Neural Networks (CNN)
Gaurav Mittal
 
Tutorial on Deep Learning
Tutorial on Deep LearningTutorial on Deep Learning
Tutorial on Deep Learning
inside-BigData.com
 
An introduction to Deep Learning
An introduction to Deep LearningAn introduction to Deep Learning
An introduction to Deep Learning
Julien SIMON
 
Convolutional Neural Network
Convolutional Neural NetworkConvolutional Neural Network
Convolutional Neural Network
Vignesh Suresh
 
Applying your Convolutional Neural Networks
Applying your Convolutional Neural NetworksApplying your Convolutional Neural Networks
Applying your Convolutional Neural Networks
Databricks
 
Deep Learning: Application & Opportunity
Deep Learning: Application & OpportunityDeep Learning: Application & Opportunity
Deep Learning: Application & Opportunity
iTrain
 
Few shot learning/ one shot learning/ machine learning
Few shot learning/ one shot learning/ machine learningFew shot learning/ one shot learning/ machine learning
Few shot learning/ one shot learning/ machine learning
ﺁﺻﻒ ﻋﻠﯽ ﻣﯿﺮ
 
Recurrent neural networks rnn
Recurrent neural networks rnnRecurrent neural networks rnn
Recurrent neural networks rnn
Kuppusamy P
 
Intro to deep learning
Intro to deep learning Intro to deep learning
Intro to deep learning
David Voyles
 
Introduction to CNN
Introduction to CNNIntroduction to CNN
Introduction to CNN
Shuai Zhang
 
Facenet - Paper Review
Facenet - Paper ReviewFacenet - Paper Review
Facenet - Paper Review
Tess Ferrandez
 
GPU and Deep learning best practices
GPU and Deep learning best practicesGPU and Deep learning best practices
GPU and Deep learning best practices
Lior Sidi
 
Deep learning - A Visual Introduction
Deep learning - A Visual IntroductionDeep learning - A Visual Introduction
Deep learning - A Visual Introduction
Lukas Masuch
 
Graph neural networks overview
Graph neural networks overviewGraph neural networks overview
Graph neural networks overview
Rodion Kiryukhin
 
PyTorch Introduction
PyTorch IntroductionPyTorch Introduction
PyTorch Introduction
Yash Kawdiya
 
Convolutional neural network
Convolutional neural networkConvolutional neural network
Convolutional neural network
Ferdous ahmed
 

What's hot (20)

Convolutional neural network in practice
Convolutional neural network in practiceConvolutional neural network in practice
Convolutional neural network in practice
 
Deep learning ppt
Deep learning pptDeep learning ppt
Deep learning ppt
 
Convolutional Neural Network (CNN) - image recognition
Convolutional Neural Network (CNN) - image recognitionConvolutional Neural Network (CNN) - image recognition
Convolutional Neural Network (CNN) - image recognition
 
What is Deep Learning | Deep Learning Simplified | Deep Learning Tutorial | E...
What is Deep Learning | Deep Learning Simplified | Deep Learning Tutorial | E...What is Deep Learning | Deep Learning Simplified | Deep Learning Tutorial | E...
What is Deep Learning | Deep Learning Simplified | Deep Learning Tutorial | E...
 
Convolutional Neural Networks (CNN)
Convolutional Neural Networks (CNN)Convolutional Neural Networks (CNN)
Convolutional Neural Networks (CNN)
 
Tutorial on Deep Learning
Tutorial on Deep LearningTutorial on Deep Learning
Tutorial on Deep Learning
 
An introduction to Deep Learning
An introduction to Deep LearningAn introduction to Deep Learning
An introduction to Deep Learning
 
Convolutional Neural Network
Convolutional Neural NetworkConvolutional Neural Network
Convolutional Neural Network
 
Applying your Convolutional Neural Networks
Applying your Convolutional Neural NetworksApplying your Convolutional Neural Networks
Applying your Convolutional Neural Networks
 
Deep Learning: Application & Opportunity
Deep Learning: Application & OpportunityDeep Learning: Application & Opportunity
Deep Learning: Application & Opportunity
 
Few shot learning/ one shot learning/ machine learning
Few shot learning/ one shot learning/ machine learningFew shot learning/ one shot learning/ machine learning
Few shot learning/ one shot learning/ machine learning
 
Recurrent neural networks rnn
Recurrent neural networks rnnRecurrent neural networks rnn
Recurrent neural networks rnn
 
Intro to deep learning
Intro to deep learning Intro to deep learning
Intro to deep learning
 
Introduction to CNN
Introduction to CNNIntroduction to CNN
Introduction to CNN
 
Facenet - Paper Review
Facenet - Paper ReviewFacenet - Paper Review
Facenet - Paper Review
 
GPU and Deep learning best practices
GPU and Deep learning best practicesGPU and Deep learning best practices
GPU and Deep learning best practices
 
Deep learning - A Visual Introduction
Deep learning - A Visual IntroductionDeep learning - A Visual Introduction
Deep learning - A Visual Introduction
 
Graph neural networks overview
Graph neural networks overviewGraph neural networks overview
Graph neural networks overview
 
PyTorch Introduction
PyTorch IntroductionPyTorch Introduction
PyTorch Introduction
 
Convolutional neural network
Convolutional neural networkConvolutional neural network
Convolutional neural network
 

Viewers also liked

Deep neural networks
Deep neural networksDeep neural networks
Deep neural networks
Si Haem
 
Deep learning - Conceptual understanding and applications
Deep learning - Conceptual understanding and applicationsDeep learning - Conceptual understanding and applications
Deep learning - Conceptual understanding and applications
Buhwan Jeong
 
P 02 internal_data_first_2017_04_22_v6
P 02 internal_data_first_2017_04_22_v6P 02 internal_data_first_2017_04_22_v6
P 02 internal_data_first_2017_04_22_v6
Vishwa Kolla
 
P 02 ta_in_uw_transformation_2017_06_13_v5
P 02 ta_in_uw_transformation_2017_06_13_v5P 02 ta_in_uw_transformation_2017_06_13_v5
P 02 ta_in_uw_transformation_2017_06_13_v5
Vishwa Kolla
 
Tracxn Research — Enterprise Collaboration Landscape, November 2016
Tracxn Research — Enterprise Collaboration Landscape, November 2016Tracxn Research — Enterprise Collaboration Landscape, November 2016
Tracxn Research — Enterprise Collaboration Landscape, November 2016
Tracxn
 
The Essentials of PowerPoint Color Theme
The Essentials of PowerPoint Color ThemeThe Essentials of PowerPoint Color Theme
The Essentials of PowerPoint Color Theme
24Slides
 
P 03 ml_demystified_2017_05_02_v7
P 03 ml_demystified_2017_05_02_v7P 03 ml_demystified_2017_05_02_v7
P 03 ml_demystified_2017_05_02_v7
Vishwa Kolla
 
Deep Learning for Sales Professionals
Deep Learning for Sales ProfessionalsDeep Learning for Sales Professionals
Deep Learning for Sales Professionals
Miguel González-Fierro
 
Introduction to Machine Learning for Heathcare, Finance, Energy, and Commerce...
Introduction to Machine Learning for Heathcare, Finance, Energy, and Commerce...Introduction to Machine Learning for Heathcare, Finance, Energy, and Commerce...
Introduction to Machine Learning for Heathcare, Finance, Energy, and Commerce...
Sam Putnam [Deep Learning]
 
P 01 paw_methods_2017_10_30_v4
P 01 paw_methods_2017_10_30_v4P 01 paw_methods_2017_10_30_v4
P 01 paw_methods_2017_10_30_v4
Vishwa Kolla
 
Artificial Intelligence (AI) for Financial Services
Artificial Intelligence (AI) for Financial Services Artificial Intelligence (AI) for Financial Services
Artificial Intelligence (AI) for Financial Services
NVIDIA
 
14 Startups Leading the Artificial Intelligence (AI) Revolution
14 Startups Leading the Artificial Intelligence (AI) Revolution14 Startups Leading the Artificial Intelligence (AI) Revolution
14 Startups Leading the Artificial Intelligence (AI) Revolution
NVIDIA
 
Transform your Business with AI, Deep Learning and Machine Learning
Transform your Business with AI, Deep Learning and Machine LearningTransform your Business with AI, Deep Learning and Machine Learning
Transform your Business with AI, Deep Learning and Machine Learning
Sri Ambati
 
The Spring of AI : Leveraging Collective Disruptor Insights
The Spring of AI : Leveraging Collective Disruptor InsightsThe Spring of AI : Leveraging Collective Disruptor Insights
The Spring of AI : Leveraging Collective Disruptor Insights
Zinnov
 
[DSC 2016] 系列活動：李宏毅 / 一天搞懂深度學習
[DSC 2016] 系列活動：李宏毅 / 一天搞懂深度學習[DSC 2016] 系列活動：李宏毅 / 一天搞懂深度學習
[DSC 2016] 系列活動：李宏毅 / 一天搞懂深度學習
台灣資料科學年會
 
Deep Learning - The Past, Present and Future of Artificial Intelligence
Deep Learning - The Past, Present and Future of Artificial IntelligenceDeep Learning - The Past, Present and Future of Artificial Intelligence
Deep Learning - The Past, Present and Future of Artificial Intelligence
Lukas Masuch
 
How to Become a Thought Leader in Your Niche
How to Become a Thought Leader in Your NicheHow to Become a Thought Leader in Your Niche
How to Become a Thought Leader in Your Niche
Leslie Samuel
 

Viewers also liked (17)

Deep neural networks
Deep neural networksDeep neural networks
Deep neural networks
 
Deep learning - Conceptual understanding and applications
Deep learning - Conceptual understanding and applicationsDeep learning - Conceptual understanding and applications
Deep learning - Conceptual understanding and applications
 
P 02 internal_data_first_2017_04_22_v6
P 02 internal_data_first_2017_04_22_v6P 02 internal_data_first_2017_04_22_v6
P 02 internal_data_first_2017_04_22_v6
 
P 02 ta_in_uw_transformation_2017_06_13_v5
P 02 ta_in_uw_transformation_2017_06_13_v5P 02 ta_in_uw_transformation_2017_06_13_v5
P 02 ta_in_uw_transformation_2017_06_13_v5
 
Tracxn Research — Enterprise Collaboration Landscape, November 2016
Tracxn Research — Enterprise Collaboration Landscape, November 2016Tracxn Research — Enterprise Collaboration Landscape, November 2016
Tracxn Research — Enterprise Collaboration Landscape, November 2016
 
The Essentials of PowerPoint Color Theme
The Essentials of PowerPoint Color ThemeThe Essentials of PowerPoint Color Theme
The Essentials of PowerPoint Color Theme
 
P 03 ml_demystified_2017_05_02_v7
P 03 ml_demystified_2017_05_02_v7P 03 ml_demystified_2017_05_02_v7
P 03 ml_demystified_2017_05_02_v7
 
Deep Learning for Sales Professionals
Deep Learning for Sales ProfessionalsDeep Learning for Sales Professionals
Deep Learning for Sales Professionals
 
Introduction to Machine Learning for Heathcare, Finance, Energy, and Commerce...
Introduction to Machine Learning for Heathcare, Finance, Energy, and Commerce...Introduction to Machine Learning for Heathcare, Finance, Energy, and Commerce...
Introduction to Machine Learning for Heathcare, Finance, Energy, and Commerce...
 
P 01 paw_methods_2017_10_30_v4
P 01 paw_methods_2017_10_30_v4P 01 paw_methods_2017_10_30_v4
P 01 paw_methods_2017_10_30_v4
 
Artificial Intelligence (AI) for Financial Services
Artificial Intelligence (AI) for Financial Services Artificial Intelligence (AI) for Financial Services
Artificial Intelligence (AI) for Financial Services
 
14 Startups Leading the Artificial Intelligence (AI) Revolution
14 Startups Leading the Artificial Intelligence (AI) Revolution14 Startups Leading the Artificial Intelligence (AI) Revolution
14 Startups Leading the Artificial Intelligence (AI) Revolution
 
Transform your Business with AI, Deep Learning and Machine Learning
Transform your Business with AI, Deep Learning and Machine LearningTransform your Business with AI, Deep Learning and Machine Learning
Transform your Business with AI, Deep Learning and Machine Learning
 
The Spring of AI : Leveraging Collective Disruptor Insights
The Spring of AI : Leveraging Collective Disruptor InsightsThe Spring of AI : Leveraging Collective Disruptor Insights
The Spring of AI : Leveraging Collective Disruptor Insights
 
[DSC 2016] 系列活動：李宏毅 / 一天搞懂深度學習
[DSC 2016] 系列活動：李宏毅 / 一天搞懂深度學習[DSC 2016] 系列活動：李宏毅 / 一天搞懂深度學習
[DSC 2016] 系列活動：李宏毅 / 一天搞懂深度學習
 
Deep Learning - The Past, Present and Future of Artificial Intelligence
Deep Learning - The Past, Present and Future of Artificial IntelligenceDeep Learning - The Past, Present and Future of Artificial Intelligence
Deep Learning - The Past, Present and Future of Artificial Intelligence
 
How to Become a Thought Leader in Your Niche
How to Become a Thought Leader in Your NicheHow to Become a Thought Leader in Your Niche
How to Become a Thought Leader in Your Niche
 

Similar to Python for Image Understanding: Deep Learning with Convolutional Neural Nets

Georgia Tech cse6242 - Intro to Deep Learning and DL4J
Georgia Tech cse6242 - Intro to Deep Learning and DL4JGeorgia Tech cse6242 - Intro to Deep Learning and DL4J
Georgia Tech cse6242 - Intro to Deep Learning and DL4J
Josh Patterson
 
Don't Start from Scratch: Transfer Learning for Novel Computer Vision Problem...
Don't Start from Scratch: Transfer Learning for Novel Computer Vision Problem...Don't Start from Scratch: Transfer Learning for Novel Computer Vision Problem...
Don't Start from Scratch: Transfer Learning for Novel Computer Vision Problem...
StampedeCon
 
Deep learning - a primer
Deep learning - a primerDeep learning - a primer
Deep learning - a primer
Uwe Friedrichsen
 
Deep learning - a primer
Deep learning - a primerDeep learning - a primer
Deep learning - a primer
Shirin Elsinghorst
 
DEF CON 24 - Clarence Chio - machine duping 101
DEF CON 24 - Clarence Chio - machine duping 101DEF CON 24 - Clarence Chio - machine duping 101
DEF CON 24 - Clarence Chio - machine duping 101
Felipe Prado
 
Introduction to deep learning
Introduction to deep learningIntroduction to deep learning
Introduction to deep learning
Abhishek Bhandwaldar
 
NLP and Deep Learning for non_experts
NLP and Deep Learning for non_expertsNLP and Deep Learning for non_experts
NLP and Deep Learning for non_experts
Sanghamitra Deb
 
Week3-Deep Neural Network (DNN).pptx
Week3-Deep Neural Network (DNN).pptxWeek3-Deep Neural Network (DNN).pptx
Week3-Deep Neural Network (DNN).pptx
fahmi324663
 
Deep learning introduction
Deep learning introductionDeep learning introduction
Deep learning introduction
Adwait Bhave
 
Machine Duping 101: Pwning Deep Learning Systems
Machine Duping 101: Pwning Deep Learning SystemsMachine Duping 101: Pwning Deep Learning Systems
Machine Duping 101: Pwning Deep Learning Systems
Clarence Chio
 
Troubleshooting Deep Neural Networks - Full Stack Deep Learning
Troubleshooting Deep Neural Networks - Full Stack Deep LearningTroubleshooting Deep Neural Networks - Full Stack Deep Learning
Troubleshooting Deep Neural Networks - Full Stack Deep Learning
Sergey Karayev
 
17_00-Dima-Panchenko-cnn-tips-and-tricks.pptx
17_00-Dima-Panchenko-cnn-tips-and-tricks.pptx17_00-Dima-Panchenko-cnn-tips-and-tricks.pptx
17_00-Dima-Panchenko-cnn-tips-and-tricks.pptx
MahmoudAbuGhali
 
Separating Hype from Reality in Deep Learning with Sameer Farooqui
Separating Hype from Reality in Deep Learning with Sameer Farooqui Separating Hype from Reality in Deep Learning with Sameer Farooqui
Separating Hype from Reality in Deep Learning with Sameer Farooqui
Databricks
 
Phx dl meetup
Phx dl meetupPhx dl meetup
Phx dl meetup
James Sirota
 
How to win data science competitions with Deep Learning
How to win data science competitions with Deep LearningHow to win data science competitions with Deep Learning
How to win data science competitions with Deep Learning
Sri Ambati
 
Synthetic dialogue generation with Deep Learning
Synthetic dialogue generation with Deep LearningSynthetic dialogue generation with Deep Learning
Synthetic dialogue generation with Deep Learning
S N
 
EssentialsOfMachineLearning.pdf
EssentialsOfMachineLearning.pdfEssentialsOfMachineLearning.pdf
EssentialsOfMachineLearning.pdf
Ankita Tiwari
 
Deep learning tutorial 9/2019
Deep learning tutorial 9/2019Deep learning tutorial 9/2019
Deep learning tutorial 9/2019
Amr Rashed
 
Deep Learning Tutorial
Deep Learning TutorialDeep Learning Tutorial
Deep Learning Tutorial
Amr Rashed
 
MDEC Data Matters Series: machine learning and Deep Learning, A Primer
MDEC Data Matters Series: machine learning and Deep Learning, A PrimerMDEC Data Matters Series: machine learning and Deep Learning, A Primer
MDEC Data Matters Series: machine learning and Deep Learning, A Primer
Poo Kuan Hoong
 

Similar to Python for Image Understanding: Deep Learning with Convolutional Neural Nets (20)

Georgia Tech cse6242 - Intro to Deep Learning and DL4J
Georgia Tech cse6242 - Intro to Deep Learning and DL4JGeorgia Tech cse6242 - Intro to Deep Learning and DL4J
Georgia Tech cse6242 - Intro to Deep Learning and DL4J
 
Don't Start from Scratch: Transfer Learning for Novel Computer Vision Problem...
Don't Start from Scratch: Transfer Learning for Novel Computer Vision Problem...Don't Start from Scratch: Transfer Learning for Novel Computer Vision Problem...
Don't Start from Scratch: Transfer Learning for Novel Computer Vision Problem...
 
Deep learning - a primer
Deep learning - a primerDeep learning - a primer
Deep learning - a primer
 
Deep learning - a primer
Deep learning - a primerDeep learning - a primer
Deep learning - a primer
 
DEF CON 24 - Clarence Chio - machine duping 101
DEF CON 24 - Clarence Chio - machine duping 101DEF CON 24 - Clarence Chio - machine duping 101
DEF CON 24 - Clarence Chio - machine duping 101
 
Introduction to deep learning
Introduction to deep learningIntroduction to deep learning
Introduction to deep learning
 
NLP and Deep Learning for non_experts
NLP and Deep Learning for non_expertsNLP and Deep Learning for non_experts
NLP and Deep Learning for non_experts
 
Week3-Deep Neural Network (DNN).pptx
Week3-Deep Neural Network (DNN).pptxWeek3-Deep Neural Network (DNN).pptx
Week3-Deep Neural Network (DNN).pptx
 
Deep learning introduction
Deep learning introductionDeep learning introduction
Deep learning introduction
 
Machine Duping 101: Pwning Deep Learning Systems
Machine Duping 101: Pwning Deep Learning SystemsMachine Duping 101: Pwning Deep Learning Systems
Machine Duping 101: Pwning Deep Learning Systems
 
Troubleshooting Deep Neural Networks - Full Stack Deep Learning
Troubleshooting Deep Neural Networks - Full Stack Deep LearningTroubleshooting Deep Neural Networks - Full Stack Deep Learning
Troubleshooting Deep Neural Networks - Full Stack Deep Learning
 
17_00-Dima-Panchenko-cnn-tips-and-tricks.pptx
17_00-Dima-Panchenko-cnn-tips-and-tricks.pptx17_00-Dima-Panchenko-cnn-tips-and-tricks.pptx
17_00-Dima-Panchenko-cnn-tips-and-tricks.pptx
 
Separating Hype from Reality in Deep Learning with Sameer Farooqui
Separating Hype from Reality in Deep Learning with Sameer Farooqui Separating Hype from Reality in Deep Learning with Sameer Farooqui
Separating Hype from Reality in Deep Learning with Sameer Farooqui
 
Phx dl meetup
Phx dl meetupPhx dl meetup
Phx dl meetup
 
How to win data science competitions with Deep Learning
How to win data science competitions with Deep LearningHow to win data science competitions with Deep Learning
How to win data science competitions with Deep Learning
 
Synthetic dialogue generation with Deep Learning
Synthetic dialogue generation with Deep LearningSynthetic dialogue generation with Deep Learning
Synthetic dialogue generation with Deep Learning
 
EssentialsOfMachineLearning.pdf
EssentialsOfMachineLearning.pdfEssentialsOfMachineLearning.pdf
EssentialsOfMachineLearning.pdf
 
Deep learning tutorial 9/2019
Deep learning tutorial 9/2019Deep learning tutorial 9/2019
Deep learning tutorial 9/2019
 
Deep Learning Tutorial
Deep Learning TutorialDeep Learning Tutorial
Deep Learning Tutorial
 
MDEC Data Matters Series: machine learning and Deep Learning, A Primer
MDEC Data Matters Series: machine learning and Deep Learning, A PrimerMDEC Data Matters Series: machine learning and Deep Learning, A Primer
MDEC Data Matters Series: machine learning and Deep Learning, A Primer
 

More from Roelof Pieters

Speculations in anthropology and tech for an uncertain future
Speculations in anthropology and tech for an uncertain futureSpeculations in anthropology and tech for an uncertain future
Speculations in anthropology and tech for an uncertain future
Roelof Pieters
 
AI assisted creativity
AI assisted creativity AI assisted creativity
AI assisted creativity
Roelof Pieters
 
Creativity and AI: 
Deep Neural Nets "Going Wild"
Creativity and AI: 
Deep Neural Nets "Going Wild"Creativity and AI: 
Deep Neural Nets "Going Wild"
Creativity and AI: 
Deep Neural Nets "Going Wild"
Roelof Pieters
 
Deep Neural Networks 
that talk (Back)… with style
Deep Neural Networks 
that talk (Back)… with styleDeep Neural Networks 
that talk (Back)… with style
Deep Neural Networks 
that talk (Back)… with style
Roelof Pieters
 
Building a Deep Learning (Dream) Machine
Building a Deep Learning (Dream) MachineBuilding a Deep Learning (Dream) Machine
Building a Deep Learning (Dream) Machine
Roelof Pieters
 
Multi-modal embeddings: from discriminative to generative models and creative ai
Multi-modal embeddings: from discriminative to generative models and creative aiMulti-modal embeddings: from discriminative to generative models and creative ai
Multi-modal embeddings: from discriminative to generative models and creative ai
Roelof Pieters
 
Deep learning for natural language embeddings
Deep learning for natural language embeddingsDeep learning for natural language embeddings
Deep learning for natural language embeddings
Roelof Pieters
 
Multi modal retrieval and generation with deep distributed models
Multi modal retrieval and generation with deep distributed modelsMulti modal retrieval and generation with deep distributed models
Multi modal retrieval and generation with deep distributed models
Roelof Pieters
 
Deep Learning for Natural Language Processing: Word Embeddings
Deep Learning for Natural Language Processing: Word EmbeddingsDeep Learning for Natural Language Processing: Word Embeddings
Deep Learning for Natural Language Processing: Word Embeddings
Roelof Pieters
 
Creative AI & multimodality: looking ahead
Creative AI & multimodality: looking aheadCreative AI & multimodality: looking ahead
Creative AI & multimodality: looking ahead
Roelof Pieters
 
Explore Data: Data Science + Visualization
Explore Data: Data Science + VisualizationExplore Data: Data Science + Visualization
Explore Data: Data Science + Visualization
Roelof Pieters
 
Deep Learning as a Cat/Dog Detector
Deep Learning as a Cat/Dog DetectorDeep Learning as a Cat/Dog Detector
Deep Learning as a Cat/Dog Detector
Roelof Pieters
 
Deep Learning for Information Retrieval
Deep Learning for Information RetrievalDeep Learning for Information Retrieval
Deep Learning for Information Retrieval
Roelof Pieters
 
Graph, Data-science, and Deep Learning
Graph, Data-science, and Deep LearningGraph, Data-science, and Deep Learning
Graph, Data-science, and Deep Learning
Roelof Pieters
 
Deep Learning: a birds eye view
Deep Learning: a birds eye viewDeep Learning: a birds eye view
Deep Learning: a birds eye view
Roelof Pieters
 
Learning to understand phrases by embedding the dictionary
Learning to understand phrases by embedding the dictionaryLearning to understand phrases by embedding the dictionary
Learning to understand phrases by embedding the dictionary
Roelof Pieters
 
Zero shot learning through cross-modal transfer
Zero shot learning through cross-modal transferZero shot learning through cross-modal transfer
Zero shot learning through cross-modal transfer
Roelof Pieters
 
Visual-Semantic Embeddings: some thoughts on Language
Visual-Semantic Embeddings: some thoughts on LanguageVisual-Semantic Embeddings: some thoughts on Language
Visual-Semantic Embeddings: some thoughts on Language
Roelof Pieters
 
Deep Learning, an interactive introduction for NLP-ers
Deep Learning, an interactive introduction for NLP-ersDeep Learning, an interactive introduction for NLP-ers
Deep Learning, an interactive introduction for NLP-ers
Roelof Pieters
 
Deep Learning for NLP: An Introduction to Neural Word Embeddings
Deep Learning for NLP: An Introduction to Neural Word EmbeddingsDeep Learning for NLP: An Introduction to Neural Word Embeddings
Deep Learning for NLP: An Introduction to Neural Word Embeddings
Roelof Pieters
 

More from Roelof Pieters (20)

Speculations in anthropology and tech for an uncertain future
Speculations in anthropology and tech for an uncertain futureSpeculations in anthropology and tech for an uncertain future
Speculations in anthropology and tech for an uncertain future
 
AI assisted creativity
AI assisted creativity AI assisted creativity
AI assisted creativity
 
Creativity and AI: 
Deep Neural Nets "Going Wild"
Creativity and AI: 
Deep Neural Nets "Going Wild"Creativity and AI: 
Deep Neural Nets "Going Wild"
Creativity and AI: 
Deep Neural Nets "Going Wild"
 
Deep Neural Networks 
that talk (Back)… with style
Deep Neural Networks 
that talk (Back)… with styleDeep Neural Networks 
that talk (Back)… with style
Deep Neural Networks 
that talk (Back)… with style
 
Building a Deep Learning (Dream) Machine
Building a Deep Learning (Dream) MachineBuilding a Deep Learning (Dream) Machine
Building a Deep Learning (Dream) Machine
 
Multi-modal embeddings: from discriminative to generative models and creative ai
Multi-modal embeddings: from discriminative to generative models and creative aiMulti-modal embeddings: from discriminative to generative models and creative ai
Multi-modal embeddings: from discriminative to generative models and creative ai
 
Deep learning for natural language embeddings
Deep learning for natural language embeddingsDeep learning for natural language embeddings
Deep learning for natural language embeddings
 
Multi modal retrieval and generation with deep distributed models
Multi modal retrieval and generation with deep distributed modelsMulti modal retrieval and generation with deep distributed models
Multi modal retrieval and generation with deep distributed models
 
Deep Learning for Natural Language Processing: Word Embeddings
Deep Learning for Natural Language Processing: Word EmbeddingsDeep Learning for Natural Language Processing: Word Embeddings
Deep Learning for Natural Language Processing: Word Embeddings
 
Creative AI & multimodality: looking ahead
Creative AI & multimodality: looking aheadCreative AI & multimodality: looking ahead
Creative AI & multimodality: looking ahead
 
Explore Data: Data Science + Visualization
Explore Data: Data Science + VisualizationExplore Data: Data Science + Visualization
Explore Data: Data Science + Visualization
 
Deep Learning as a Cat/Dog Detector
Deep Learning as a Cat/Dog DetectorDeep Learning as a Cat/Dog Detector
Deep Learning as a Cat/Dog Detector
 
Deep Learning for Information Retrieval
Deep Learning for Information RetrievalDeep Learning for Information Retrieval
Deep Learning for Information Retrieval
 
Graph, Data-science, and Deep Learning
Graph, Data-science, and Deep LearningGraph, Data-science, and Deep Learning
Graph, Data-science, and Deep Learning
 
Deep Learning: a birds eye view
Deep Learning: a birds eye viewDeep Learning: a birds eye view
Deep Learning: a birds eye view
 
Learning to understand phrases by embedding the dictionary
Learning to understand phrases by embedding the dictionaryLearning to understand phrases by embedding the dictionary
Learning to understand phrases by embedding the dictionary
 
Zero shot learning through cross-modal transfer
Zero shot learning through cross-modal transferZero shot learning through cross-modal transfer
Zero shot learning through cross-modal transfer
 
Visual-Semantic Embeddings: some thoughts on Language
Visual-Semantic Embeddings: some thoughts on LanguageVisual-Semantic Embeddings: some thoughts on Language
Visual-Semantic Embeddings: some thoughts on Language
 
Deep Learning, an interactive introduction for NLP-ers
Deep Learning, an interactive introduction for NLP-ersDeep Learning, an interactive introduction for NLP-ers
Deep Learning, an interactive introduction for NLP-ers
 
Deep Learning for NLP: An Introduction to Neural Word Embeddings
Deep Learning for NLP: An Introduction to Neural Word EmbeddingsDeep Learning for NLP: An Introduction to Neural Word Embeddings
Deep Learning for NLP: An Introduction to Neural Word Embeddings
 

Recently uploaded

Getting started with Amazon Bedrock Studio and Control Tower
Getting started with Amazon Bedrock Studio and Control TowerGetting started with Amazon Bedrock Studio and Control Tower
Getting started with Amazon Bedrock Studio and Control Tower
Vladimir Samoylov
 
0x01 - Newton's Third Law: Static vs. Dynamic Abusers
0x01 - Newton's Third Law: Static vs. Dynamic Abusers0x01 - Newton's Third Law: Static vs. Dynamic Abusers
0x01 - Newton's Third Law: Static vs. Dynamic Abusers
OWASP Beja
 
Bitcoin Lightning wallet and tic-tac-toe game XOXO
Bitcoin Lightning wallet and tic-tac-toe game XOXOBitcoin Lightning wallet and tic-tac-toe game XOXO
Bitcoin Lightning wallet and tic-tac-toe game XOXO
Matjaž Lipuš
 
Doctoral Symposium at the 17th IEEE International Conference on Software Test...
Doctoral Symposium at the 17th IEEE International Conference on Software Test...Doctoral Symposium at the 17th IEEE International Conference on Software Test...
Doctoral Symposium at the 17th IEEE International Conference on Software Test...
Sebastiano Panichella
 
Announcement of 18th IEEE International Conference on Software Testing, Verif...
Announcement of 18th IEEE International Conference on Software Testing, Verif...Announcement of 18th IEEE International Conference on Software Testing, Verif...
Announcement of 18th IEEE International Conference on Software Testing, Verif...
Sebastiano Panichella
 
Bonzo subscription_hjjjjjjjj5hhhhhhh_2024.pdf
Bonzo subscription_hjjjjjjjj5hhhhhhh_2024.pdfBonzo subscription_hjjjjjjjj5hhhhhhh_2024.pdf
Bonzo subscription_hjjjjjjjj5hhhhhhh_2024.pdf
khadija278284
 
Acorn Recovery: Restore IT infra within minutes
Acorn Recovery: Restore IT infra within minutesAcorn Recovery: Restore IT infra within minutes
Acorn Recovery: Restore IT infra within minutes
IP ServerOne
 
International Workshop on Artificial Intelligence in Software Testing
International Workshop on Artificial Intelligence in Software TestingInternational Workshop on Artificial Intelligence in Software Testing
International Workshop on Artificial Intelligence in Software Testing
Sebastiano Panichella
 
Eureka, I found it! - Special Libraries Association 2021 Presentation
Eureka, I found it! - Special Libraries Association 2021 PresentationEureka, I found it! - Special Libraries Association 2021 Presentation
Eureka, I found it! - Special Libraries Association 2021 Presentation
Access Innovations, Inc.
 
Competition and Regulation in Professional Services – KLEINER – June 2024 OEC...
Competition and Regulation in Professional Services – KLEINER – June 2024 OEC...Competition and Regulation in Professional Services – KLEINER – June 2024 OEC...
Competition and Regulation in Professional Services – KLEINER – June 2024 OEC...
OECD Directorate for Financial and Enterprise Affairs
 
somanykidsbutsofewfathers-140705000023-phpapp02.pptx
somanykidsbutsofewfathers-140705000023-phpapp02.pptxsomanykidsbutsofewfathers-140705000023-phpapp02.pptx
somanykidsbutsofewfathers-140705000023-phpapp02.pptx
Howard Spence
 
Sharpen existing tools or get a new toolbox? Contemporary cluster initiatives...
Sharpen existing tools or get a new toolbox? Contemporary cluster initiatives...Sharpen existing tools or get a new toolbox? Contemporary cluster initiatives...
Sharpen existing tools or get a new toolbox? Contemporary cluster initiatives...
Orkestra
 
Obesity causes and management and associated medical conditions
Obesity causes and management and associated medical conditionsObesity causes and management and associated medical conditions
Obesity causes and management and associated medical conditions
Faculty of Medicine And Health Sciences
 

Recently uploaded (13)

Getting started with Amazon Bedrock Studio and Control Tower
Getting started with Amazon Bedrock Studio and Control TowerGetting started with Amazon Bedrock Studio and Control Tower
Getting started with Amazon Bedrock Studio and Control Tower
 
0x01 - Newton's Third Law: Static vs. Dynamic Abusers
0x01 - Newton's Third Law: Static vs. Dynamic Abusers0x01 - Newton's Third Law: Static vs. Dynamic Abusers
0x01 - Newton's Third Law: Static vs. Dynamic Abusers
 
Bitcoin Lightning wallet and tic-tac-toe game XOXO
Bitcoin Lightning wallet and tic-tac-toe game XOXOBitcoin Lightning wallet and tic-tac-toe game XOXO
Bitcoin Lightning wallet and tic-tac-toe game XOXO
 
Doctoral Symposium at the 17th IEEE International Conference on Software Test...
Doctoral Symposium at the 17th IEEE International Conference on Software Test...Doctoral Symposium at the 17th IEEE International Conference on Software Test...
Doctoral Symposium at the 17th IEEE International Conference on Software Test...
 
Announcement of 18th IEEE International Conference on Software Testing, Verif...
Announcement of 18th IEEE International Conference on Software Testing, Verif...Announcement of 18th IEEE International Conference on Software Testing, Verif...
Announcement of 18th IEEE International Conference on Software Testing, Verif...
 
Bonzo subscription_hjjjjjjjj5hhhhhhh_2024.pdf
Bonzo subscription_hjjjjjjjj5hhhhhhh_2024.pdfBonzo subscription_hjjjjjjjj5hhhhhhh_2024.pdf
Bonzo subscription_hjjjjjjjj5hhhhhhh_2024.pdf
 
Acorn Recovery: Restore IT infra within minutes
Acorn Recovery: Restore IT infra within minutesAcorn Recovery: Restore IT infra within minutes
Acorn Recovery: Restore IT infra within minutes
 
International Workshop on Artificial Intelligence in Software Testing
International Workshop on Artificial Intelligence in Software TestingInternational Workshop on Artificial Intelligence in Software Testing
International Workshop on Artificial Intelligence in Software Testing
 
Eureka, I found it! - Special Libraries Association 2021 Presentation
Eureka, I found it! - Special Libraries Association 2021 PresentationEureka, I found it! - Special Libraries Association 2021 Presentation
Eureka, I found it! - Special Libraries Association 2021 Presentation
 
Competition and Regulation in Professional Services – KLEINER – June 2024 OEC...
Competition and Regulation in Professional Services – KLEINER – June 2024 OEC...Competition and Regulation in Professional Services – KLEINER – June 2024 OEC...
Competition and Regulation in Professional Services – KLEINER – June 2024 OEC...
 
somanykidsbutsofewfathers-140705000023-phpapp02.pptx
somanykidsbutsofewfathers-140705000023-phpapp02.pptxsomanykidsbutsofewfathers-140705000023-phpapp02.pptx
somanykidsbutsofewfathers-140705000023-phpapp02.pptx
 
Sharpen existing tools or get a new toolbox? Contemporary cluster initiatives...
Sharpen existing tools or get a new toolbox? Contemporary cluster initiatives...Sharpen existing tools or get a new toolbox? Contemporary cluster initiatives...
Sharpen existing tools or get a new toolbox? Contemporary cluster initiatives...
 
Obesity causes and management and associated medical conditions
Obesity causes and management and associated medical conditionsObesity causes and management and associated medical conditions
Obesity causes and management and associated medical conditions
 

Python for Image Understanding: Deep Learning with Convolutional Neural Nets