- Autoencoders are artificial neural networks capable of learning efficient representations of unlabeled input data through dimensionality reduction without supervision.
- They are useful for dimensionality reduction, feature extraction, unsupervised pre-training of deep neural networks, and generating new data as a generative model.
- Constraining an autoencoder during training, such as by limiting the size of its internal representation or adding noise to inputs, forces it to learn patterns in the data in order to reconstruct the inputs.
An Autoencoder is a type of Artificial Neural Network used to learn efficient data codings in an unsupervised manner. The aim of an autoencoder is to learn a representation (encoding) for a set of data, typically for dimensionality reduction, by training the network to ignore signal “noise.”
Slides for my talk discussing research on the Evolutionary Neural Network Encoder of Shenanigans (ENNEoS), a proof-of-concept encoder for shellcode. The software uses genetic algorithms to evolve neural networks that contain the shellcode and output it on demand so that it can be executed in memory.
MachinaFiesta: A Vision into Machine Learning 🚀GDSCNiT
🕵️♂️ Embark on an exhilarating journey into the realm of Machine learning and Generative AI with MachinaFiesta! 🚀. Join us for MachinaFiesta, a two-hour event exploring the fascinating world of machine learning and generative AI where you can Vision, Innovate and learn new technologies.
Slide contets:
🎤 Brief introduction to the agenda and speakers of the event
🌐 Get to know the importance and future prospects of machine learning
🧠 Interactive session on core machine learning concepts
🚀 Exploration of cutting-edge generative AI advancements
🤖 Introduction to Gemini, the open-source factual language model
🤔Discussion on Gemini's capabilities and potential applications in research and development
An Autoencoder is a type of Artificial Neural Network used to learn efficient data codings in an unsupervised manner. The aim of an autoencoder is to learn a representation (encoding) for a set of data, typically for dimensionality reduction, by training the network to ignore signal “noise.”
Slides for my talk discussing research on the Evolutionary Neural Network Encoder of Shenanigans (ENNEoS), a proof-of-concept encoder for shellcode. The software uses genetic algorithms to evolve neural networks that contain the shellcode and output it on demand so that it can be executed in memory.
MachinaFiesta: A Vision into Machine Learning 🚀GDSCNiT
🕵️♂️ Embark on an exhilarating journey into the realm of Machine learning and Generative AI with MachinaFiesta! 🚀. Join us for MachinaFiesta, a two-hour event exploring the fascinating world of machine learning and generative AI where you can Vision, Innovate and learn new technologies.
Slide contets:
🎤 Brief introduction to the agenda and speakers of the event
🌐 Get to know the importance and future prospects of machine learning
🧠 Interactive session on core machine learning concepts
🚀 Exploration of cutting-edge generative AI advancements
🤖 Introduction to Gemini, the open-source factual language model
🤔Discussion on Gemini's capabilities and potential applications in research and development
CSSC × GDSC: Intro to Machine Learning!
Aaron Shah and Manav Bhojak on October 5, 2023
🤖 Join us for an exciting ML Workshop! 🚀 Dive into the world of Machine Learning, where we'll unravel the mysteries of CNNs, RNNs, Transformers, and more. 🤯
Get ready to embark on a journey of discovery! We'll begin with an easy-to-follow introduction to the fascinating realm of ML. 📚
🛠️ In our hands-on session, we'll walk you through setting up your environment. No tech hurdles here! 🌐
🔍 Then, we'll get down to the nitty-gritty, guiding you through our starter code for a thrilling hands-on example. Together, we'll explore the power of ML in action! 💡
In this talk I'll discuss how we can combine the power of PostgreSQL with TensorFlow to perform data analysis. By using the pl/python3 procedural language we can integrate machine learning libraries such as TensorFlow with PostgreSQL, opening the door for powerful data analytics combining SQL with AI. Typical use-cases might involve regression analysis to find relationships in an existing dataset and to predict results based on new inputs, or to analyse time series data and extrapolate future data taking into account general trends and seasonal variability whilst ignoring noise. Python is an ideal language for building custom systems to do this kind of work as it gives us access to a rich ecosystem of libraries such as Pandas and Numpy, in addition to TensorFlow itself.
Distributed machine learning 101 using apache spark from a browser devoxx.b...Andy Petrella
A 3 hours session introducing the concept of Machine Learning and Distributed Computing.
It includes many examples running in notebooks of experience run on data exploring models like LM, RF, K-Means, Deep Learning.
CSSC × GDSC: Intro to Machine Learning!
Aaron Shah and Manav Bhojak on October 5, 2023
🤖 Join us for an exciting ML Workshop! 🚀 Dive into the world of Machine Learning, where we'll unravel the mysteries of CNNs, RNNs, Transformers, and more. 🤯
Get ready to embark on a journey of discovery! We'll begin with an easy-to-follow introduction to the fascinating realm of ML. 📚
🛠️ In our hands-on session, we'll walk you through setting up your environment. No tech hurdles here! 🌐
🔍 Then, we'll get down to the nitty-gritty, guiding you through our starter code for a thrilling hands-on example. Together, we'll explore the power of ML in action! 💡
In this talk I'll discuss how we can combine the power of PostgreSQL with TensorFlow to perform data analysis. By using the pl/python3 procedural language we can integrate machine learning libraries such as TensorFlow with PostgreSQL, opening the door for powerful data analytics combining SQL with AI. Typical use-cases might involve regression analysis to find relationships in an existing dataset and to predict results based on new inputs, or to analyse time series data and extrapolate future data taking into account general trends and seasonal variability whilst ignoring noise. Python is an ideal language for building custom systems to do this kind of work as it gives us access to a rich ecosystem of libraries such as Pandas and Numpy, in addition to TensorFlow itself.
Distributed machine learning 101 using apache spark from a browser devoxx.b...Andy Petrella
A 3 hours session introducing the concept of Machine Learning and Distributed Computing.
It includes many examples running in notebooks of experience run on data exploring models like LM, RF, K-Means, Deep Learning.
APNIC Foundation, presented by Ellisha Heppner at the PNG DNS Forum 2024APNIC
Ellisha Heppner, Grant Management Lead, presented an update on APNIC Foundation to the PNG DNS Forum held from 6 to 10 May, 2024 in Port Moresby, Papua New Guinea.
# Internet Security: Safeguarding Your Digital World
In the contemporary digital age, the internet is a cornerstone of our daily lives. It connects us to vast amounts of information, provides platforms for communication, enables commerce, and offers endless entertainment. However, with these conveniences come significant security challenges. Internet security is essential to protect our digital identities, sensitive data, and overall online experience. This comprehensive guide explores the multifaceted world of internet security, providing insights into its importance, common threats, and effective strategies to safeguard your digital world.
## Understanding Internet Security
Internet security encompasses the measures and protocols used to protect information, devices, and networks from unauthorized access, attacks, and damage. It involves a wide range of practices designed to safeguard data confidentiality, integrity, and availability. Effective internet security is crucial for individuals, businesses, and governments alike, as cyber threats continue to evolve in complexity and scale.
### Key Components of Internet Security
1. **Confidentiality**: Ensuring that information is accessible only to those authorized to access it.
2. **Integrity**: Protecting information from being altered or tampered with by unauthorized parties.
3. **Availability**: Ensuring that authorized users have reliable access to information and resources when needed.
## Common Internet Security Threats
Cyber threats are numerous and constantly evolving. Understanding these threats is the first step in protecting against them. Some of the most common internet security threats include:
### Malware
Malware, or malicious software, is designed to harm, exploit, or otherwise compromise a device, network, or service. Common types of malware include:
- **Viruses**: Programs that attach themselves to legitimate software and replicate, spreading to other programs and files.
- **Worms**: Standalone malware that replicates itself to spread to other computers.
- **Trojan Horses**: Malicious software disguised as legitimate software.
- **Ransomware**: Malware that encrypts a user's files and demands a ransom for the decryption key.
- **Spyware**: Software that secretly monitors and collects user information.
### Phishing
Phishing is a social engineering attack that aims to steal sensitive information such as usernames, passwords, and credit card details. Attackers often masquerade as trusted entities in email or other communication channels, tricking victims into providing their information.
### Man-in-the-Middle (MitM) Attacks
MitM attacks occur when an attacker intercepts and potentially alters communication between two parties without their knowledge. This can lead to the unauthorized acquisition of sensitive information.
### Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) Attacks
Bridging the Digital Gap Brad Spiegel Macon, GA Initiative.pptxBrad Spiegel Macon GA
Brad Spiegel Macon GA’s journey exemplifies the profound impact that one individual can have on their community. Through his unwavering dedication to digital inclusion, he’s not only bridging the gap in Macon but also setting an example for others to follow.
This 7-second Brain Wave Ritual Attracts Money To You.!nirahealhty
Discover the power of a simple 7-second brain wave ritual that can attract wealth and abundance into your life. By tapping into specific brain frequencies, this technique helps you manifest financial success effortlessly. Ready to transform your financial future? Try this powerful ritual and start attracting money today!
1.Wireless Communication System_Wireless communication is a broad term that i...JeyaPerumal1
Wireless communication involves the transmission of information over a distance without the help of wires, cables or any other forms of electrical conductors.
Wireless communication is a broad term that incorporates all procedures and forms of connecting and communicating between two or more devices using a wireless signal through wireless communication technologies and devices.
Features of Wireless Communication
The evolution of wireless technology has brought many advancements with its effective features.
The transmitted distance can be anywhere between a few meters (for example, a television's remote control) and thousands of kilometers (for example, radio communication).
Wireless communication can be used for cellular telephony, wireless access to the internet, wireless home networking, and so on.
Multi-cluster Kubernetes Networking- Patterns, Projects and GuidelinesSanjeev Rampal
Talk presented at Kubernetes Community Day, New York, May 2024.
Technical summary of Multi-Cluster Kubernetes Networking architectures with focus on 4 key topics.
1) Key patterns for Multi-cluster architectures
2) Architectural comparison of several OSS/ CNCF projects to address these patterns
3) Evolution trends for the APIs of these projects
4) Some design recommendations & guidelines for adopting/ deploying these solutions.
2. ● Autoencoders are
○ Artificial neural networks
○ Capable of learning efficient representations of the input data, called
codings, without any supervision
○ The training set is unlabeled.
● These codings typically have a much lower dimensionality than the input
data, making autoencoders useful for dimensionality reduction
Autoencoders
Autoencoders
3. Why use Autoencoders?
● Useful for dimensionality reduction
● Autoencoders act as powerful feature detectors,
● And they can be used for unsupervised pre-training of deep neural
networks
● Lastly, they are capable of randomly generating new data that looks very
similar to the training data; this is called a generative model
Autoencoders
Autoencoders
4. Autoencoders
Why use Autoencoders?
For example, we could train an autoencoder on pictures of faces, and it
would then be able to generate new faces
Autoencoders
5. ● Surprisingly, autoencoders work by simply learning to copy their inputs
to their outputs
● This may sound like a trivial task, but we will see that constraining the
network in various ways can make it rather difficult
Autoencoders
Autoencoders
6. Autoencoders
Autoencoders
For example
● You can limit the size of the internal representation, or you can add noise
to the inputs and train the network to recover the original inputs.
● These constraints prevent the autoencoder from trivially copying the
inputs directly to the outputs, which forces it to learn efficient ways of
representing the data
● In short, the codings are byproducts of the autoencoder’s attempt to
learn the identity function under some constraints
7. What we’ll learn ?
● We will explain in more depth how autoencoders work
● What types of constraints can be imposed
● And how to implement them using TensorFlow, whether it is for
○ Dimensionality reduction,
○ Feature extraction,
○ Insupervised pretraining,
○ Or as generative models
Autoencoders
Autoencoders
8. Let’s try to understand
“why constraining an
autoencoder during training pushes it to discover and exploit
patterns in the data”
With an example
Autoencoders
Efficient Data Representations
9. Which of the following number sequences do you find the easiest
to memorize?
● 40, 27, 25, 36, 81, 57, 10, 73, 19, 68
● 50, 25, 76, 38, 19, 58, 29, 88, 44, 22, 11, 34, 17, 52, 26, 13, 40, 20
Autoencoders
Efficient Data Representations
10. ● At first glance, it would seem that the first sequence should be easier,
since it is much shorter
● However, if you look carefully at the second sequence, you may notice
that it follows two simple rules:
○ Even numbers are followed by their half,
○ And odd numbers are followed by their triple plus one
This is a famous sequence known as the hailstone sequence
Autoencoders
Efficient Data Representations
11. ● Once you notice this pattern, the second sequence becomes much easier
to memorize than the first because you only need to memorize the two
rules,
○ The first number,
○ And the length of the sequence
Autoencoders
Efficient Data Representations
12. ● Note that if we could quickly and easily memorize very long sequences,
you would not care much about the existence of a pattern in the
second sequence
● You would just learn every number by heart, and that would be that
● It is the fact that it is hard to memorize long sequences that makes
it useful to recognize patterns,
● And hopefully this clarifies why constraining an autoencoder during
training pushes it to discover and exploit patterns in the data
Autoencoders
Efficient Data Representations
13. The relationship between memory, perception, and pattern matching was
famously studied by William Chase and Herbert Simon in the early
1970s
Efficient Data Representations
William Chase
Autoencoders
Herbert Simon
14. Let’s see how their study of chess players is similar to an
Autoencoder
● They observed that expert chess players were able to memorize the
positions of all the pieces in a game by looking at the board for just 5
seconds,
● A task that most people would find impossible
● However, this was only the case when the pieces were placed in realistic
positions from actual games, not when the pieces were placed randomly.
Autoencoders
Efficient Data Representations
15. Let’s see how their study of chess players is similar to an
Autoencoder
● Chess experts don’t have a much better memory than you and I,
● They just see chess patterns more easily thanks to their experience with
the game
● Noticing patterns helps them store information efficiently
Autoencoders
Efficient Data Representations
16. Let’s see how their study of chess players is similar to an
Autoencoder
● Just like the chess players in this memory experiment, an autoencoder
○ looks at the inputs,
○ converts them to an efficient internal representation,
○ and then spits out something that (hopefully) looks very close to the
inputs
Autoencoders
Efficient Data Representations
17. Composition of Autoencoder
An autoencoder is always composed of two parts:
● An encoder or recognition network that converts the inputs to an
internal representation,
● Followed by a decoder or generative network that converts the
internal representation to the outputs
Autoencoders
Efficient Data Representations
19. Composition of Autoencoder
Efficient Data Representations
An autoencoder
typically has the
same architecture as
a Multi-Layer
Perceptron, except that
the number of neurons
in the output layer must
be equal to the
number of inputs
Autoencoders
20. There is just one hidden
layer composed of two
neurons (the encoder),
and one output layer
composed of three
neurons (the decoder)
Composition of Autoencoder
Efficient Data Representations
Autoencoders
21. Composition of Autoencoder
Efficient Data Representations
The outputs are often called
the reconstructions
since the autoencoder tries
to reconstruct the inputs,
and the cost function
contains a reconstruction
loss that penalizes the model
when the reconstructions are
different from the inputs.
Autoencoders
22. Because the internal
representation has a lower
dimensionality than the input
data, it is 2D instead of
3D, the autoencoder is said
to be undercomplete
Composition of Autoencoder
Efficient Data Representations
Autoencoders
23. Composition of Autoencoder
Efficient Data Representations
● An undercomplete
autoencoder cannot
trivially copy its inputs to
the codings, yet it must
find a way to output a
copy of its inputs
● It is forced to learn the
most important features in
the input data and drop
the unimportant ones
Autoencoders