The document discusses convolutional neural networks (CNNs). It begins with an introduction and overview of CNN components like convolution, ReLU, and pooling layers. Convolution layers apply filters to input images to extract features, ReLU introduces non-linearity, and pooling layers reduce dimensionality. CNNs are well-suited for image data since they can incorporate spatial relationships. The document provides an example of building a CNN using TensorFlow to classify handwritten digits from the MNIST dataset.
In machine learning, a convolutional neural network is a class of deep, feed-forward artificial neural networks that have successfully been applied fpr analyzing visual imagery.
The presentation is made on CNN's which is explained using the image classification problem, the presentation was prepared in perspective of understanding computer vision and its applications. I tried to explain the CNN in the most simple way possible as for my understanding. This presentation helps the beginners of CNN to have a brief idea about the architecture and different layers in the architecture of CNN with the example. Please do refer the references in the last slide for a better idea on working of CNN. In this presentation, I have also discussed the different types of CNN(not all) and the applications of Computer Vision.
A comprehensive tutorial on Convolutional Neural Networks (CNN) which talks about the motivation behind CNNs and Deep Learning in general, followed by a description of the various components involved in a typical CNN layer. It explains the theory involved with the different variants used in practice and also, gives a big picture of the whole network by putting everything together.
Next, there's a discussion of the various state-of-the-art frameworks being used to implement CNNs to tackle real-world classification and regression problems.
Finally, the implementation of the CNNs is demonstrated by implementing the paper 'Age ang Gender Classification Using Convolutional Neural Networks' by Hassner (2015).
In machine learning, a convolutional neural network is a class of deep, feed-forward artificial neural networks that have successfully been applied fpr analyzing visual imagery.
The presentation is made on CNN's which is explained using the image classification problem, the presentation was prepared in perspective of understanding computer vision and its applications. I tried to explain the CNN in the most simple way possible as for my understanding. This presentation helps the beginners of CNN to have a brief idea about the architecture and different layers in the architecture of CNN with the example. Please do refer the references in the last slide for a better idea on working of CNN. In this presentation, I have also discussed the different types of CNN(not all) and the applications of Computer Vision.
A comprehensive tutorial on Convolutional Neural Networks (CNN) which talks about the motivation behind CNNs and Deep Learning in general, followed by a description of the various components involved in a typical CNN layer. It explains the theory involved with the different variants used in practice and also, gives a big picture of the whole network by putting everything together.
Next, there's a discussion of the various state-of-the-art frameworks being used to implement CNNs to tackle real-world classification and regression problems.
Finally, the implementation of the CNNs is demonstrated by implementing the paper 'Age ang Gender Classification Using Convolutional Neural Networks' by Hassner (2015).
In this presentation we discuss the convolution operation, the architecture of a convolution neural network, different layers such as pooling etc. This presentation draws heavily from A Karpathy's Stanford Course CS 231n
image classification is a common problem in Artificial Intelligence , we used CIFR10 data set and tried a lot of methods to reach a high test accuracy like neural networks and Transfer learning techniques .
you can view the source code and the papers we read on github : https://github.com/Asma-Hawari/Machine-Learning-Project-
Convolutional neural network (CNN / ConvNet's) is a part of Computer Vision. Machine Learning Algorithm. Image Classification, Image Detection, Digit Recognition, and many more. https://technoelearn.com .
Convolutional Neural Network - CNN | How CNN Works | Deep Learning Course | S...Simplilearn
This presentation on Convolutional neural network tutorial (CNN) will help you understand what is a convolutional neural network, hoe CNN recognizes images, what are layers in the convolutional neural network and at the end, you will see a use case implementation using CNN. CNN is a feed forward neural network that is generally used to analyze visual images by processing data with grid like topology. A CNN is also known as a "ConvNet". Convolutional networks can also perform optical character recognition to digitize text and make natural-language processing possible on analog and hand-written documents. CNNs can also be applied to sound when it is represented visually as a spectrogram. Now, lets deep dive into this presentation to understand what is CNN and how do they actually work.
Below topics are explained in this CNN presentation(Convolutional Neural Network presentation)
1. Introduction to CNN
2. What is a convolutional neural network?
3. How CNN recognizes images?
4. Layers in convolutional neural network
5. Use case implementation using CNN
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
Learn more at: https://www.simplilearn.com/
What Is Deep Learning? | Introduction to Deep Learning | Deep Learning Tutori...Simplilearn
This Deep Learning Presentation will help you in understanding what is Deep learning, why do we need Deep learning, applications of Deep Learning along with a detailed explanation on Neural Networks and how these Neural Networks work. Deep learning is inspired by the integral function of the human brain specific to artificial neural networks. These networks, which represent the decision-making process of the brain, use complex algorithms that process data in a non-linear way, learning in an unsupervised manner to make choices based on the input. This Deep Learning tutorial is ideal for professionals with beginners to intermediate levels of experience. Now, let us dive deep into this topic and understand what Deep learning actually is.
Below topics are explained in this Deep Learning Presentation:
1. What is Deep Learning?
2. Why do we need Deep Learning?
3. Applications of Deep Learning
4. What is Neural Network?
5. Activation Functions
6. Working of Neural Network
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms.
There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals:
1. Software engineers
2. Data scientists
3. Data analysts
4. Statisticians with an interest in deep learning
Convolutional Neural Networks : Popular Architecturesananth
In this presentation we look at some of the popular architectures, such as ResNet, that have been successfully used for a variety of applications. Starting from the AlexNet and VGG that showed that the deep learning architectures can deliver unprecedented accuracies for Image classification and localization tasks, we review other recent architectures such as ResNet, GoogleNet (Inception) and the more recent SENet that have won ImageNet competitions.
In this presentation we discuss the convolution operation, the architecture of a convolution neural network, different layers such as pooling etc. This presentation draws heavily from A Karpathy's Stanford Course CS 231n
image classification is a common problem in Artificial Intelligence , we used CIFR10 data set and tried a lot of methods to reach a high test accuracy like neural networks and Transfer learning techniques .
you can view the source code and the papers we read on github : https://github.com/Asma-Hawari/Machine-Learning-Project-
Convolutional neural network (CNN / ConvNet's) is a part of Computer Vision. Machine Learning Algorithm. Image Classification, Image Detection, Digit Recognition, and many more. https://technoelearn.com .
Convolutional Neural Network - CNN | How CNN Works | Deep Learning Course | S...Simplilearn
This presentation on Convolutional neural network tutorial (CNN) will help you understand what is a convolutional neural network, hoe CNN recognizes images, what are layers in the convolutional neural network and at the end, you will see a use case implementation using CNN. CNN is a feed forward neural network that is generally used to analyze visual images by processing data with grid like topology. A CNN is also known as a "ConvNet". Convolutional networks can also perform optical character recognition to digitize text and make natural-language processing possible on analog and hand-written documents. CNNs can also be applied to sound when it is represented visually as a spectrogram. Now, lets deep dive into this presentation to understand what is CNN and how do they actually work.
Below topics are explained in this CNN presentation(Convolutional Neural Network presentation)
1. Introduction to CNN
2. What is a convolutional neural network?
3. How CNN recognizes images?
4. Layers in convolutional neural network
5. Use case implementation using CNN
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
Learn more at: https://www.simplilearn.com/
What Is Deep Learning? | Introduction to Deep Learning | Deep Learning Tutori...Simplilearn
This Deep Learning Presentation will help you in understanding what is Deep learning, why do we need Deep learning, applications of Deep Learning along with a detailed explanation on Neural Networks and how these Neural Networks work. Deep learning is inspired by the integral function of the human brain specific to artificial neural networks. These networks, which represent the decision-making process of the brain, use complex algorithms that process data in a non-linear way, learning in an unsupervised manner to make choices based on the input. This Deep Learning tutorial is ideal for professionals with beginners to intermediate levels of experience. Now, let us dive deep into this topic and understand what Deep learning actually is.
Below topics are explained in this Deep Learning Presentation:
1. What is Deep Learning?
2. Why do we need Deep Learning?
3. Applications of Deep Learning
4. What is Neural Network?
5. Activation Functions
6. Working of Neural Network
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms.
There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals:
1. Software engineers
2. Data scientists
3. Data analysts
4. Statisticians with an interest in deep learning
Convolutional Neural Networks : Popular Architecturesananth
In this presentation we look at some of the popular architectures, such as ResNet, that have been successfully used for a variety of applications. Starting from the AlexNet and VGG that showed that the deep learning architectures can deliver unprecedented accuracies for Image classification and localization tasks, we review other recent architectures such as ResNet, GoogleNet (Inception) and the more recent SENet that have won ImageNet competitions.
Sogang University Machine Learning and Data Mining lab seminar, Neural Networks for newbies and Convolutional Neural Networks. This is prerequisite material to understand deep convolutional architecture.
Convolution codes - Coding/Decoding Tree codes and Trellis codes for multiple...Madhumita Tamhane
In contrast to block codes, Convolution coding scheme has an information frame together with previous m information frames encoded into a single code word frame, hence coupling successive code word frames. Convolution codes are most important Tree codes that satisfy certain additional linearity and time invariance properties. Decoding procedure is mainly devoted to correcting errors in first frame. The effect of these information symbols on subsequent code word frames can be computed and subtracted from subsequent code word frames. Hence in spite of infinitely long code words, computations can be arranged so that the effect of earlier frames, properly decoded, on the current frame is zero.
This slide set on convolutional neural networks is meant to be supplementary material to the slides from Andrej Karpathy's course. In this slide set we explain the motivation for CNN and also describe how to understand CNN coming from a standard feed forward neural networks perspective. For detailed architecture and discussions refer the original slides. I might post more detailed slides later.
With the growth of computer networking, electronic commerce and web services, security networking systems have become very important to protect infomation and networks againts malicious usage or attacks. In this report, it is designed an Intrusion Detection System using two artificial neural networks: one for Intrusion Detection and the another for Attack Classification.
Explores the type of structure learned by Convolutional Neural Networks, the applications where they're most valuable and a number of appropriate mental models for understanding deep learning.
This presentation is Part 2 of my September Lisp NYC presentation on Reinforcement Learning and Artificial Neural Nets. We will continue from where we left off by covering Convolutional Neural Nets (CNN) and Recurrent Neural Nets (RNN) in depth.
Time permitting I also plan on having a few slides on each of the following topics:
1. Generative Adversarial Networks (GANs)
2. Differentiable Neural Computers (DNCs)
3. Deep Reinforcement Learning (DRL)
Some code examples will be provided in Clojure.
After a very brief recap of Part 1 (ANN & RL), we will jump right into CNN and their appropriateness for image recognition. We will start by covering the convolution operator. We will then explain feature maps and pooling operations and then explain the LeNet 5 architecture. The MNIST data will be used to illustrate a fully functioning CNN.
Next we cover Recurrent Neural Nets in depth and describe how they have been used in Natural Language Processing. We will explain why gated networks and LSTM are used in practice.
Please note that some exposure or familiarity with Gradient Descent and Backpropagation will be assumed. These are covered in the first part of the talk for which both video and slides are available online.
A lot of material will be drawn from the new Deep Learning book by Goodfellow & Bengio as well as Michael Nielsen's online book on Neural Networks and Deep Learning as well several other online resources.
Bio
Pierre de Lacaze has over 20 years industry experience with AI and Lisp based technologies. He holds a Bachelor of Science in Applied Mathematics and a Master’s Degree in Computer Science.
https://www.linkedin.com/in/pierre-de-lacaze-b11026b/
Recurrent Neural Networks have shown to be very powerful models as they can propagate context over several time steps. Due to this they can be applied effectively for addressing several problems in Natural Language Processing, such as Language Modelling, Tagging problems, Speech Recognition etc. In this presentation we introduce the basic RNN model and discuss the vanishing gradient problem. We describe LSTM (Long Short Term Memory) and Gated Recurrent Units (GRU). We also discuss Bidirectional RNN with an example. RNN architectures can be considered as deep learning systems where the number of time steps can be considered as the depth of the network. It is also possible to build the RNN with multiple hidden layers, each having recurrent connections from the previous time steps that represent the abstraction both in time and space.
This slides explains how Convolution Neural Networks can be coded using Google TensorFlow.
Video available at : https://www.youtube.com/watch?v=EoysuTMmmMc
Invited talk at Tsinghua University on "Applications of Deep Neural Network". As the tech. lead of deep learning task force at NIO USA INC, I was invited to give this colloquium talk on general applications of deep neural network.
Artificial Intelligence, Machine Learning and Deep LearningSujit Pal
Slides for talk Abhishek Sharma and I gave at the Gennovation tech talks (https://gennovationtalks.com/) at Genesis. The talk was part of outreach for the Deep Learning Enthusiasts meetup group at San Francisco. My part of the talk is covered from slides 19-34.
With the explosive growth of online information, recommender system has been an effective tool to overcome information overload and promote sales. In recent years, deep learning's revolutionary advances in speech recognition, image analysis and natural language processing have gained significant attention. Meanwhile, recent studies also demonstrate its efficacy in coping with information retrieval and recommendation tasks. Applying deep learning techniques into recommender system has been gaining momentum due to its state-of-the-art performance. In this talk, I will present recent development of deep learning based recommender models and highlight some future challenges and open issues of this research field.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
3. 1.1 Definition
• CNN is a specualized kind of neural network for processing
data thtat has a known, grid-like topology, such as time-
series(1D grid), image data(2D grid), etc.
• CNN is a supervised deep learning algorithm, it is used in
various fields like speech recognition, image retrieval and
face recognition.
School of Computer Sicience and Engineering
4. 1.1 Definition
• ImageNet Classification with Deep Convolutional Neural Networks
(Cited by 9538, NIPS 2012, Alex Krizhevsky, Ilya Sutskever, Geoffrey
E. Hinton)
• build a CNN, has 60 million parameters and 650,000 neurons,
consists of five convolutional layers.
• Typical CNN is a 5 layer architecture consist of convolution layer,
pooling layer and classification layer.
• Convolution layer: extract the unique features from the input
image
• Pooling layer: reduce the dimensionality
• Generally CNN is trained using back-propagation algorithm
School of Computer Sicience and Engineering
5. 1.2 Motivation
• MLP do not scale well
• MLP ignore pixel correlation
• MLP are not robust to image transformation
School of Computer Sicience and Engineering
multi-layer perceptron
6. 2.1 Why Convolution ?
• preserves the spatial relationship
between pixels by learning image
features using small squares of
input data
• detect small,meaningful features
such as edges with kernels
School of Computer Sicience and Engineering
A 2D convolution example from deep learning book
10. 3 ReLU
• Introducing the Non Linearity
School of Computer Sicience and Engineering
Other non linear
functions such as
tanh or sigmoid can
also be used instead
of ReLU, but ReLU
has been found to
perform better in
most situations
11. 4.1 Motivation of Pooling
• Reduce dimensionality
• In all cases, pooling helps to make the representation become
approximately invariant to small translations of the input.
• local translation can be a very useful property if we care more about
whether some feature is present than exactly where it is.
• Type of Pooling
• Max(works better)
• Average
• Sum
School of Computer Sicience and Engineering
13. 5 Example by Tensorflow
School of Computer Sicience and Engineering
28 * 28
14. 5 Example by Tensorflow
School of Computer Sicience and Engineering
15. 5 Example by Tensorflow
School of Computer Sicience and Engineering
• zero-padding the 28x28x1 image to
32x32x1
• applying 5x5x32 convolution to get
28x28x32
• max-pooling down to 14x14x32 zero-
padding the 14x14x32 to 18x18x32
• applying 5x5x32x64 convolution to get
14x14x64
• max-pooling down to 7x7x64.
16. 5 Example by Tensorflow
School of Computer Sicience and Engineering
For example, when determining whether an image contains a face, we need not know the location
of the eyes with pixel-perfect accuracy, we just need to know that there is an eye on
the left side of the face and an eye on the right side of the face
For example, when determining whether an image contains a face, we need not know the location
of the eyes with pixel-perfect accuracy, we just need to know that there is an eye on
the left side of the face and an eye on the right side of the face
For example, when determining whether an image contains a face, we need not know the location
of the eyes with pixel-perfect accuracy, we just need to know that there is an eye on
the left side of the face and an eye on the right side of the face
For example, when determining whether an image contains a face, we need not know the location
of the eyes with pixel-perfect accuracy, we just need to know that there is an eye on
the left side of the face and an eye on the right side of the face
For example, when determining whether an image contains a face, we need not know the location
of the eyes with pixel-perfect accuracy, we just need to know that there is an eye on
the left side of the face and an eye on the right side of the face
For example, when determining whether an image contains a face, we need not know the location
of the eyes with pixel-perfect accuracy, we just need to know that there is an eye on
the left side of the face and an eye on the right side of the face
For example, when determining whether an image contains a face, we need not know the location
of the eyes with pixel-perfect accuracy, we just need to know that there is an eye on
the left side of the face and an eye on the right side of the face
