Suggestions:
1) For best quality, download the PDF before viewing.
2) Open at least two windows: One for the Youtube video, one for the screencast (link below), and optionally one for the slides themselves.
3) The Youtube video is shown on the first page of the slide deck, for slides, just skip to page 2.
Screencast: http://youtu.be/VoL7JKJmr2I
Video recording: http://youtu.be/CJRvb8zxRdE (Thanks to Al Friedrich!)
In this talk, we take Deep Learning to task with real world data puzzles to solve.
Data:
- Higgs binary classification dataset (10M rows, 29 cols)
- MNIST 10-class dataset
- Weather categorical dataset
- eBay text classification dataset (8500 cols, 500k rows, 467 classes)
- ECG heartbeat anomaly detection
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Synthetic dialogue generation with Deep LearningS N
A walkthrough of a Deep Learning based technique which would generate TV scripts using Recurrent Neural Network. The model will generate a completely new TV script for a scene, after being training from a dataset. One will learn the concepts around RNN, NLP and various deep learning techniques.
Technologies to be used:
Python 3, Jupyter, TensorFlow
Source code: https://github.com/syednasar/talks/tree/master/synthetic-dialog
Caffe (Convolutional Architecture for Fast Feature Embedding) is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and by community contributors.
Caffe’s expressive architecture encourages application and innovation. Models and optimization are defined by configuration without hard-coding. Switch between CPU and GPU by setting a single flag to train on a GPU machine then deploy to commodity clusters or mobile devices.Caffe’s extensible code fosters active development. In Caffe’s first year, it has been forked by over 1,000 developers and had many significant changes contributed back. Thanks to these contributors the framework tracks the state-of-the-art in both code and models.Speed makes Caffe perfect for research experiments and industry deployment. Caffe can processover 60M images per day with a single NVIDIA K40 GPU*. That’s 1 ms/image for inference and 4 ms/image for learning. We believe that Caffe is the fastest convnet implementation available.Caffe already powers academic research projects, startup prototypes, and even large-scale industrial applications in vision, speech, and multimedia. Join our community of brewers on the caffe-users group and Github.
This tutorial is designed to equip researchers and developers with the tools and know-how needed to incorporate deep learning into their work. Both the ideas and implementation of state-of-the-art deep learning models will be presented. While deep learning and deep features have recently achieved strong results in many tasks, a common framework and shared models are needed to advance further research and applications and reduce the barrier to entry. To this end we present the Caffe framework, public reference models, and working examples for deep learning. Join our tour from the 1989 LeNet for digit recognition to today’s top ILSVRC14 vision models. Follow along with do-it-yourself code notebooks. While focusing on vision, general techniques are covered.
This is a presentation I gave as a short overview of LSTMs. The slides are accompanied by two examples which apply LSTMs to Time Series data. Examples were implemented using Keras. See links in slide pack.
Language translation with Deep Learning (RNN) with TensorFlowS N
The author is going to take you into the realm of Recurrent Neural Network (RNN). He will be training a sequence to sequence model on a dataset of English and French sentences that can translate new (unseen) sentences from English to French.
This will be a walkthrough of an end to end technique to train a Deep RNN model. You will learn to build various components necessary to build a Sequence-to-Sequence model.
You will learn about the fundamentals of Deep Learning, mainly RNN, concepts that will be required in this solution. A familiarity of Deep Learning concepts would be handy, but most of the concepts used in this example will be covered during the demo.
Technologies to be used:
Python, Jupyter, TensorFlow, FloydHub
Source code: https://github.com/syednasar/deeplearning/blob/master/language-translation/dlnd_language_translation.ipynb
...
Suggestions:
1) For best quality, download the PDF before viewing.
2) Open at least two windows: One for the Youtube video, one for the screencast (link below), and optionally one for the slides themselves.
3) The Youtube video is shown on the first page of the slide deck, for slides, just skip to page 2.
Screencast: http://youtu.be/VoL7JKJmr2I
Video recording: http://youtu.be/CJRvb8zxRdE (Thanks to Al Friedrich!)
In this talk, we take Deep Learning to task with real world data puzzles to solve.
Data:
- Higgs binary classification dataset (10M rows, 29 cols)
- MNIST 10-class dataset
- Weather categorical dataset
- eBay text classification dataset (8500 cols, 500k rows, 467 classes)
- ECG heartbeat anomaly detection
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Synthetic dialogue generation with Deep LearningS N
A walkthrough of a Deep Learning based technique which would generate TV scripts using Recurrent Neural Network. The model will generate a completely new TV script for a scene, after being training from a dataset. One will learn the concepts around RNN, NLP and various deep learning techniques.
Technologies to be used:
Python 3, Jupyter, TensorFlow
Source code: https://github.com/syednasar/talks/tree/master/synthetic-dialog
Caffe (Convolutional Architecture for Fast Feature Embedding) is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and by community contributors.
Caffe’s expressive architecture encourages application and innovation. Models and optimization are defined by configuration without hard-coding. Switch between CPU and GPU by setting a single flag to train on a GPU machine then deploy to commodity clusters or mobile devices.Caffe’s extensible code fosters active development. In Caffe’s first year, it has been forked by over 1,000 developers and had many significant changes contributed back. Thanks to these contributors the framework tracks the state-of-the-art in both code and models.Speed makes Caffe perfect for research experiments and industry deployment. Caffe can processover 60M images per day with a single NVIDIA K40 GPU*. That’s 1 ms/image for inference and 4 ms/image for learning. We believe that Caffe is the fastest convnet implementation available.Caffe already powers academic research projects, startup prototypes, and even large-scale industrial applications in vision, speech, and multimedia. Join our community of brewers on the caffe-users group and Github.
This tutorial is designed to equip researchers and developers with the tools and know-how needed to incorporate deep learning into their work. Both the ideas and implementation of state-of-the-art deep learning models will be presented. While deep learning and deep features have recently achieved strong results in many tasks, a common framework and shared models are needed to advance further research and applications and reduce the barrier to entry. To this end we present the Caffe framework, public reference models, and working examples for deep learning. Join our tour from the 1989 LeNet for digit recognition to today’s top ILSVRC14 vision models. Follow along with do-it-yourself code notebooks. While focusing on vision, general techniques are covered.
This is a presentation I gave as a short overview of LSTMs. The slides are accompanied by two examples which apply LSTMs to Time Series data. Examples were implemented using Keras. See links in slide pack.
Language translation with Deep Learning (RNN) with TensorFlowS N
The author is going to take you into the realm of Recurrent Neural Network (RNN). He will be training a sequence to sequence model on a dataset of English and French sentences that can translate new (unseen) sentences from English to French.
This will be a walkthrough of an end to end technique to train a Deep RNN model. You will learn to build various components necessary to build a Sequence-to-Sequence model.
You will learn about the fundamentals of Deep Learning, mainly RNN, concepts that will be required in this solution. A familiarity of Deep Learning concepts would be handy, but most of the concepts used in this example will be covered during the demo.
Technologies to be used:
Python, Jupyter, TensorFlow, FloydHub
Source code: https://github.com/syednasar/deeplearning/blob/master/language-translation/dlnd_language_translation.ipynb
...
Modeling Electronic Health Records with Recurrent Neural NetworksJosh Patterson
Time series data is increasingly ubiquitous. This trend is especially obvious in health and wellness, with both the adoption of electronic health record (EHR) systems in hospitals and clinics and the proliferation of wearable sensors. In 2009, intensive care units in the United States treated nearly 55,000 patients per day, generating digital-health databases containing millions of individual measurements, most of those forming time series. In the first quarter of 2015 alone, over 11 million health-related wearables were shipped by vendors. Recording hundreds of measurements per day per user, these devices are fueling a health time series data explosion. As a result, we will need ever more sophisticated tools to unlock the true value of this data to improve the lives of patients worldwide.
Deep learning, specifically with recurrent neural networks (RNNs), has emerged as a central tool in a variety of complex temporal-modeling problems, such as speech recognition. However, RNNs are also among the most challenging models to work with, particularly outside the domains where they are widely applied. Josh Patterson, David Kale, and Zachary Lipton bring the open source deep learning library DL4J to bear on the challenge of analyzing clinical time series using RNNs. DL4J provides a reliable, efficient implementation of many deep learning models embedded within an enterprise-ready open source data ecosystem (e.g., Hadoop and Spark), making it well suited to complex clinical data. Josh, David, and Zachary offer an overview of deep learning and RNNs and explain how they are implemented in DL4J. They then demonstrate a workflow example that uses a pipeline based on DL4J and Canova to prepare publicly available clinical data from PhysioNet and apply the DL4J RNN.
Introduction to deep learning @ Startup.ML by Andres RodriguezIntel Nervana
Deep learning is unlocking tremendous economic value across various market sectors. Individual data scientists can draw from several open source frameworks and basic hardware resources during the very initial investigative phases but quickly require significant hardware and software resources to build and deploy production models. Intel offers various software and hardware to support a diversity of workloads and user needs. Intel Nervana delivers a competitive deep learning platform to make it easy for data scientists to start from the iterative, investigatory phase and take models all the way to deployment. This platform is designed for speed and scale, and serves as a catalyst for all types of organizations to benefit from the full potential of deep learning. Example of supported applications include but not limited to automotive speech interfaces, image search, language translation, agricultural robotics and genomics, financial document summarization, and finding anomalies in IoT data.
Introduction to Deep Learning with Pythonindico data
A presentation by Alec Radford, Head of Research at indico Data Solutions, on deep learning with Python's Theano library.
The emphasis of the presentation is high performance computing, natural language processing (using recurrent neural nets), and large scale learning with GPUs.
Video of the talk available here: https://www.youtube.com/watch?v=S75EdAcXHKk
Talk given at PYCON Stockholm 2015
Intro to Deep Learning + taking pretrained imagenet network, extracting features, and RBM on top = 97 Accuracy after 1 hour (!) of training (in top 10% of kaggle cat vs dog competition)
Details of Lazy Deep Learning for Images Recognition in ZZ Photo appPAY2 YOU
В докладе представлена тема глубокого обучения (Deep Learning) для распознавания изображений. Рассматриваются практические аспекты обучения глубоких сверточных сетей на GPU, обсуждается личный опыт портирования обученных нейросетей в приложение на основе библиотеки OpenCV, проводится сравнение полученного детектора домашних животных на основе подхода Lazy Deep Learning с детектором Виолы-Джонса.
Докладчики: Артем Чернодуб – эксперт в области искусственных нейронных сетей и систем искусственного интеллекта. В 2007 году закончил Московский физико-технический институт. Руководит направлением Computer Vision в компании ZZ Wolf, а также по совместительству работает научным сотрудником в Институте проблем математических машин и систем НАНУ.
Юрий Пащенко – специалист в области систем машинного зрения и машинного обучения, магистр НТУУ «Киевский Политехнический Институт», факультет прикладной математики (2014). Работает в компании ZZ Wolf на должности R&D Engineer.
End-to-end speech recognition in Neon presented by Anthony Ndirango and Tyler Lee
Modern automatic speech recognition systems incorporate tremendous amount of expert knowledge and a wide array of machine learning techniques. The promise of deep learning is to strip away much of this complexity in favor of the flexibility of neural networks. We will describe our efforts in implementing end-to-end speech recognition in neon by combining convolutional and recurrent neural networks to create an acoustic model followed by a graph-based decoding scheme. These types of models are trained to go directly from raw waveforms to transcribed speech without requiring any kind of explicit forced alignment. We will also discuss additional challenges that must be overcome to produce state-of-the-art results.
Startup.Ml: Using neon for NLP and Localization Applications Intel Nervana
Speaker: Arjun Bansal, co-founder of Nervana Systems
Arjun Bansal’s workshop focused on neon, an open-source python based deep learning framework that has been build from the ground up for speed and ease of use. The workshop highlights how to use neon, build Recurrent Recurrent Neural Networks to generate and analyze text, and build Convolutional Autoencoders to generate images and to localize objects. Arjun also demoed the integration of neon with the Nervana cloud (in private beta) for multi-GPU training of deep networks.
Modeling Electronic Health Records with Recurrent Neural NetworksJosh Patterson
Time series data is increasingly ubiquitous. This trend is especially obvious in health and wellness, with both the adoption of electronic health record (EHR) systems in hospitals and clinics and the proliferation of wearable sensors. In 2009, intensive care units in the United States treated nearly 55,000 patients per day, generating digital-health databases containing millions of individual measurements, most of those forming time series. In the first quarter of 2015 alone, over 11 million health-related wearables were shipped by vendors. Recording hundreds of measurements per day per user, these devices are fueling a health time series data explosion. As a result, we will need ever more sophisticated tools to unlock the true value of this data to improve the lives of patients worldwide.
Deep learning, specifically with recurrent neural networks (RNNs), has emerged as a central tool in a variety of complex temporal-modeling problems, such as speech recognition. However, RNNs are also among the most challenging models to work with, particularly outside the domains where they are widely applied. Josh Patterson, David Kale, and Zachary Lipton bring the open source deep learning library DL4J to bear on the challenge of analyzing clinical time series using RNNs. DL4J provides a reliable, efficient implementation of many deep learning models embedded within an enterprise-ready open source data ecosystem (e.g., Hadoop and Spark), making it well suited to complex clinical data. Josh, David, and Zachary offer an overview of deep learning and RNNs and explain how they are implemented in DL4J. They then demonstrate a workflow example that uses a pipeline based on DL4J and Canova to prepare publicly available clinical data from PhysioNet and apply the DL4J RNN.
Introduction to deep learning @ Startup.ML by Andres RodriguezIntel Nervana
Deep learning is unlocking tremendous economic value across various market sectors. Individual data scientists can draw from several open source frameworks and basic hardware resources during the very initial investigative phases but quickly require significant hardware and software resources to build and deploy production models. Intel offers various software and hardware to support a diversity of workloads and user needs. Intel Nervana delivers a competitive deep learning platform to make it easy for data scientists to start from the iterative, investigatory phase and take models all the way to deployment. This platform is designed for speed and scale, and serves as a catalyst for all types of organizations to benefit from the full potential of deep learning. Example of supported applications include but not limited to automotive speech interfaces, image search, language translation, agricultural robotics and genomics, financial document summarization, and finding anomalies in IoT data.
Introduction to Deep Learning with Pythonindico data
A presentation by Alec Radford, Head of Research at indico Data Solutions, on deep learning with Python's Theano library.
The emphasis of the presentation is high performance computing, natural language processing (using recurrent neural nets), and large scale learning with GPUs.
Video of the talk available here: https://www.youtube.com/watch?v=S75EdAcXHKk
Talk given at PYCON Stockholm 2015
Intro to Deep Learning + taking pretrained imagenet network, extracting features, and RBM on top = 97 Accuracy after 1 hour (!) of training (in top 10% of kaggle cat vs dog competition)
Details of Lazy Deep Learning for Images Recognition in ZZ Photo appPAY2 YOU
В докладе представлена тема глубокого обучения (Deep Learning) для распознавания изображений. Рассматриваются практические аспекты обучения глубоких сверточных сетей на GPU, обсуждается личный опыт портирования обученных нейросетей в приложение на основе библиотеки OpenCV, проводится сравнение полученного детектора домашних животных на основе подхода Lazy Deep Learning с детектором Виолы-Джонса.
Докладчики: Артем Чернодуб – эксперт в области искусственных нейронных сетей и систем искусственного интеллекта. В 2007 году закончил Московский физико-технический институт. Руководит направлением Computer Vision в компании ZZ Wolf, а также по совместительству работает научным сотрудником в Институте проблем математических машин и систем НАНУ.
Юрий Пащенко – специалист в области систем машинного зрения и машинного обучения, магистр НТУУ «Киевский Политехнический Институт», факультет прикладной математики (2014). Работает в компании ZZ Wolf на должности R&D Engineer.
End-to-end speech recognition in Neon presented by Anthony Ndirango and Tyler Lee
Modern automatic speech recognition systems incorporate tremendous amount of expert knowledge and a wide array of machine learning techniques. The promise of deep learning is to strip away much of this complexity in favor of the flexibility of neural networks. We will describe our efforts in implementing end-to-end speech recognition in neon by combining convolutional and recurrent neural networks to create an acoustic model followed by a graph-based decoding scheme. These types of models are trained to go directly from raw waveforms to transcribed speech without requiring any kind of explicit forced alignment. We will also discuss additional challenges that must be overcome to produce state-of-the-art results.
Startup.Ml: Using neon for NLP and Localization Applications Intel Nervana
Speaker: Arjun Bansal, co-founder of Nervana Systems
Arjun Bansal’s workshop focused on neon, an open-source python based deep learning framework that has been build from the ground up for speed and ease of use. The workshop highlights how to use neon, build Recurrent Recurrent Neural Networks to generate and analyze text, and build Convolutional Autoencoders to generate images and to localize objects. Arjun also demoed the integration of neon with the Nervana cloud (in private beta) for multi-GPU training of deep networks.
Deep Learning - The Past, Present and Future of Artificial IntelligenceLukas Masuch
In the last couple of years, deep learning techniques have transformed the world of artificial intelligence. One by one, the abilities and techniques that humans once imagined were uniquely our own have begun to fall to the onslaught of ever more powerful machines. Deep neural networks are now better than humans at tasks such as face recognition and object recognition. They’ve mastered the ancient game of Go and thrashed the best human players. “The pace of progress in artificial general intelligence is incredible fast” (Elon Musk – CEO Tesla & SpaceX) leading to an AI that “would be either the best or the worst thing ever to happen to humanity” (Stephen Hawking – Physicist).
What sparked this new hype? How is Deep Learning different from previous approaches? Let’s look behind the curtain and unravel the reality. This talk will introduce the core concept of deep learning, explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why “deep learning is probably one of the most exciting things that is happening in the computer industry“ (Jen-Hsun Huang – CEO NVIDIA).
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
Hanjun Dai, PhD Student, School of Computational Science and Engineering, Geo...MLconf
Graph Representation Learning with Deep Embedding Approach:
Graphs are commonly used data structure for representing the real-world relationships, e.g., molecular structure, knowledge graphs, social and communication networks. The effective encoding of graphical information is essential to the success of such applications. In this talk I’ll first describe a general deep learning framework, namely structure2vec, for end to end graph feature representation learning. Then I’ll present the direct application of this model on graph problems on different scales, including community detection and molecule graph classification/regression. We then extend the embedding idea to temporal evolving user-product interaction graph for recommendation. Finally I’ll present our latest work on leveraging the reinforcement learning technique for graph combinatorial optimization, including vertex cover problem for social influence maximization and traveling salesman problem for scheduling management.
Deep Learning with Python: A Complete Practical Course for ResearchersATITAcademy
This course is for academic researchers to move with them step by step from scratch to advanced knowledge in the field of Deep Learning and its related topics that allow them to be able to implement their ideas and research findings. In this course, participants will learn Python and Deep Learning Neural Network from scratch then, based on a systematic learning methodology, will be able to increase their knowledge to a highly-advanced level. This intensive course is the only of its type that provides complete knowledge about almost all the cutting-edge aspects of Deep Learning, which allows the participants to be able to implement any type of related research in any area. Each participant will be worked with individually to start producing a respected project.
Finding the best solution for Image ProcessingTech Triveni
What is beyond using Tensorflow, GPU or TPU to process images seamlessly? Do we have a silver bullet for image processing? Over the years, image processing has picked up a different level of attraction. Everyone can think about its ease of usability because it has become a reality now. We have started seeing how Residual Neural Network architecture is being used for different cases and not only that, how Residual Neural network is being tweaked to solve different problems. Along with tweaking the ResNet, preprocessing is also being improved to support different architecture for this matter.
Everyone has almost become cyborg already with mobile phones in our hands and apparently until human beings bring the AI/ML to the phones completely they are not taking any rest. We are going to see the development of different architecture and algorithms around running AI/ML on low configuration devices.
In this session, we are going to talk about different research papers submitted for these matters and some implementations for the same as well.
This is the slides of my master defense; 17 april 2003
subject: "High capacity neural network optimization problems: study & solutions exploration"
Learning visual representation without human labelKai-Wen Zhao
Self supervised learning (SSL) is one of the most fast-growing research topic in recent years. SSL provides algorithm that directly learn visual representation from data itself rather than human manual labels. From theoretical point of view, SSL explores information theory & the nature of large scale dataset.
From Conventional Machine Learning to Deep Learning and Beyond.pptxChun-Hao Chang
In this slide, Deep Learning are compared with Conventional Learning and the strength of DNN models will be explained.
The target audience are people who have the knowledge of Machine Learning or Data Mining but not familiar with Deep Learning.
Similar to Introduction to deep learning in python and Matlab (20)
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
Richard's entangled aventures in wonderlandRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
2. Outline
● Problem Definition
● Motivation
● Training a Regression DNN
● Training a Classification DNN
● Open Source Packages
● Summary + Questions
2
4. Tutorial
● Goal: Detect facial
landmarks on (normal)
face images
● Data set provided by
Dr. Yoshua Bengio
● Tutorial code available:
https://github.com/dnouri/kfkd-tutorial/blob/master/kfkd.py
4
8. Python DL Framework
Wrapper to Lasagne
Theano extension for Deep Learning
Define, optimize, and evaluate mathematical expressions
Efficient Cuda GPU for DNN
8
Low Level
High Level
HW Supports: GPU & CPU
OS: Linux, OS X, Windows
9. Training a Deep Neural Network
1. Data Analysis
2. Architecture Engineering
3. Optimization
4. Training the DNN
9
10. Training a Deep Neural Network
1. Data Analysis
a. Exploration + Validation
b. Pre-Processing
c. Batch and Split
2. Architecture Engineering
3. Optimization
4. Training the DNN
10
11. Data Exploration + Validation
Data:
● 7K gray-scale images of detected faces
● 96x96 pixels per image
● 15 landmarks per image (?)
Data validation:
● Some Landmarks are missing
11
1
15. Training a Deep Neural Network
1. Data Analysis
2. Architecture Engineering
a. Layers Definition
b. Layers Implementation
3. Optimization
4. Training
15
22. Training a Deep Neural Network
1. Data Analysis
2. Architecture Engineering
3. Optimization
a. Back Propagation
b. Objective
c. SGD
d. Updates
e. Convergence Tuning
4. Training the DNN 22
32. Training a Deep Neural Network
1. Data Analysis
2. Architecture Engineering
3. Optimization
4. Training the DNN
a. Fit
b. Fine Tune Pre-Trained
c. Learning Curves
32
42. Outline
● Problem Definition
● Motivation
● Training a regression DNN
● Training a classification DNN
● Improving the DNN
● Open Source Packages
● Summary
42
43. Matlab DL Framework
Open Source CNN Toolbox by
Numerical computing using Parallel Computing Toolbox
Efficient Cuda GPU for DNN
43
Low Level
High Level
HW Supports: GPU & CPU
OS: Linux, OS X, Windows
44. Problem Statement
Classify a, b, …, z images into 26 classes:
44http://www.robots.ox.ac.uk/~vgg/practicals/cnn/
Bonus - OCR:
45. Training a Deep Neural Network
1. Data Analysis
2. Training the DNN
3. Architecture Engineering
4. Optimization
45
65. Beyond Training
1. Training a classification DNN
2. Improving the DNN
a. Analysis Capabilities
b. Augmentation
3. Open Source Packages
4. Summary
65
72. Deal with NaN
1. If in first 100 iterations
a. Learning rate is too high
2. Beyond 100 iterations
a. Gradient explosion
i. Consider gradient clipping
b. Illegal math operation
i. SoftMax: inf/inf
ii. Division by zero by one of your customized layers
72http://russellsstewart.com//notes/0.html
73. The Net Doesn’t Learn Anything
1. Training loss does not reduce after first 100 iterations
a. Reduce the training size to 10 instances (images) to overfit it
i. Achieve 100% training accuracy on a small portion of data
b. Change batch size to 1 to and monitor the error per batch
c. Solve the simplest version of your problem
73
http://russellsstewart.com//notes/0.html
74. Beyond Training
1. Training a classification DNN
2. Improving the DNN
3. Open Source Packages
a. DL Open Source Packages
b. Effort Estimation
4. Summary
74
75. Tips from Other Packages
Torch code organization Caffe’s separation
configuration ↔code
NeuralNet → YAML text format
defining experiment’s configuration
75
76. DL Open Source Packages
76
Caffe & MatConvNet for applications
Torch, TensorFlow and Theano for research on DL
http://fastml.com/torch-vs-theano/
Simple dnnComplex dnn
