An autoencoder is an artificial neural network that is trained to copy its input to its output. It consists of an encoder that compresses the input into a lower-dimensional latent-space encoding, and a decoder that reconstructs the output from this encoding. Autoencoders are useful for dimensionality reduction, feature learning, and generative modeling. When constrained by limiting the latent space or adding noise, autoencoders are forced to learn efficient representations of the input data. For example, a linear autoencoder trained with mean squared error performs principal component analysis.
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.”
Autoencoders Tutorial | Autoencoders In Deep Learning | Tensorflow Training |...Edureka!
** AI & Deep Learning with Tensorflow Training: https://www.edureka.co/ai-deep-learning-with-tensorflow **
This Edureka tutorial of "Autoencoders Tutorial" provides you with a brief introduction about autoencoders and how they compress unsupervised data. You will get detailed information on the different types of Autoencoders with the code for each type. You will see the various applications and types of autoencoders used in deep learning for dimentionality reduction.
This tutorial covers the following topics:
1. Why do we need Autoencoders?
2. What are Autoencoders?
3. Properties of Autoencoders
4. Autoencoders Training & Architecture
5. Types of Autoencoders
6. Applications of Autoencoders
Check out our Deep Learning blog series: https://bit.ly/2xVIMe1
Check out our complete Youtube playlist here: https://bit.ly/2OhZEpz
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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.”
Autoencoders Tutorial | Autoencoders In Deep Learning | Tensorflow Training |...Edureka!
** AI & Deep Learning with Tensorflow Training: https://www.edureka.co/ai-deep-learning-with-tensorflow **
This Edureka tutorial of "Autoencoders Tutorial" provides you with a brief introduction about autoencoders and how they compress unsupervised data. You will get detailed information on the different types of Autoencoders with the code for each type. You will see the various applications and types of autoencoders used in deep learning for dimentionality reduction.
This tutorial covers the following topics:
1. Why do we need Autoencoders?
2. What are Autoencoders?
3. Properties of Autoencoders
4. Autoencoders Training & Architecture
5. Types of Autoencoders
6. Applications of Autoencoders
Check out our Deep Learning blog series: https://bit.ly/2xVIMe1
Check out our complete Youtube playlist here: https://bit.ly/2OhZEpz
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
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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).
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session.
basics of GAN neural network
GAN is a advanced tech in area of neural networks which will help to generate new data . This new data will be developed based over the past experiences and raw data.
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.
▸ Machine Learning / Deep Learning models require to set the value of many hyperparameters
▸ Common examples: regularization coefficients, dropout rate, or number of neurons per layer in a Neural Network
▸ Instead of relying on some "expert advice", this presentation shows how to automatically find optimal hyperparameters
▸ Exhaustive Search, Monte Carlo Search, Bayesian Optimization, and Evolutionary Algorithms are explained with concrete examples
GANs are the new hottest topic in the ML arena; however, they present a challenge for the researchers and the engineers alike. Their design, and most importantly, the code implementation has been causing headaches to the ML practitioners, especially when moving to production.
Starting from the very basic of what a GAN is, passing trough Tensorflow implementation, using the most cutting-edge APIs available in the framework, and finally, production-ready serving at scale using Google Cloud ML Engine.
Slides for the talk: https://www.pycon.it/conference/talks/deep-diving-into-gans-form-theory-to-production
Github repo: https://github.com/zurutech/gans-from-theory-to-production
Uncertain Knowledge and Reasoning in Artificial IntelligenceExperfy
Learn how to take informed decisions based on probabilities and expert knowledge
Understand and explore one of the most exciting advances in AI in the last decades.
Many hands-on examples, including Python code.
Check it out: https://www.experfy.com/training/courses/uncertain-knowledge-and-reasoning-in-artificial-intelligence
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).
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session.
basics of GAN neural network
GAN is a advanced tech in area of neural networks which will help to generate new data . This new data will be developed based over the past experiences and raw data.
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.
▸ Machine Learning / Deep Learning models require to set the value of many hyperparameters
▸ Common examples: regularization coefficients, dropout rate, or number of neurons per layer in a Neural Network
▸ Instead of relying on some "expert advice", this presentation shows how to automatically find optimal hyperparameters
▸ Exhaustive Search, Monte Carlo Search, Bayesian Optimization, and Evolutionary Algorithms are explained with concrete examples
GANs are the new hottest topic in the ML arena; however, they present a challenge for the researchers and the engineers alike. Their design, and most importantly, the code implementation has been causing headaches to the ML practitioners, especially when moving to production.
Starting from the very basic of what a GAN is, passing trough Tensorflow implementation, using the most cutting-edge APIs available in the framework, and finally, production-ready serving at scale using Google Cloud ML Engine.
Slides for the talk: https://www.pycon.it/conference/talks/deep-diving-into-gans-form-theory-to-production
Github repo: https://github.com/zurutech/gans-from-theory-to-production
Uncertain Knowledge and Reasoning in Artificial IntelligenceExperfy
Learn how to take informed decisions based on probabilities and expert knowledge
Understand and explore one of the most exciting advances in AI in the last decades.
Many hands-on examples, including Python code.
Check it out: https://www.experfy.com/training/courses/uncertain-knowledge-and-reasoning-in-artificial-intelligence
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
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.
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! 💡
Understanding computer vision with Deep LearningCloudxLab
Computer vision is a branch of computer science which deals with recognising objects, people and identifying patterns in visuals. It is basically analogous to the vision of an animal.
Topics covered:
1. Overview of Machine Learning
2. Basics of Deep Learning
3. What is computer vision and its use-cases?
4. Various algorithms used in Computer Vision (mostly CNN)
5. Live hands-on demo of either Auto Cameraman or Face recognition system
6. What next?
( Machine Learning & Deep Learning Specialization Training: https://goo.gl/5u2RiS )
This CloudxLab Reinforcement Learning tutorial helps you to understand Reinforcement Learning in detail. Below are the topics covered in this tutorial:
1) What is Reinforcement?
2) Reinforcement Learning an Introduction
3) Reinforcement Learning Example
4) Learning to Optimize Rewards
5) Policy Search - Brute Force Approach, Genetic Algorithms and Optimization Techniques
6) OpenAI Gym
7) The Credit Assignment Problem
8) Inverse Reinforcement Learning
9) Playing Atari with Deep Reinforcement Learning
10) Policy Gradients
11) Markov Decision Processes
Apache Spark - Key Value RDD - Transformations | Big Data Hadoop Spark Tutori...CloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2sm5Ekd
This CloudxLab Key-Value RDD Transformations tutorial helps you to understand Key-Value RDD transformations in detail. Below are the topics covered in this tutorial:
1) Transformations on Key-Value Pair RDD - keys(), values(), groupByKey(), combineByKey(), sortByKey(), subtractByKey(), join(), leftOuterJoin(), rightOuterJoin(), cogroup(), countByKey() and lookup()
Advanced Spark Programming - Part 2 | Big Data Hadoop Spark Tutorial | CloudxLabCloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2kyRTuW
This CloudxLab Advanced Spark Programming tutorial helps you to understand Advanced Spark Programming in detail. Below are the topics covered in this slide:
1) Shared Variables - Accumulators & Broadcast Variables
2) Accumulators and Fault Tolerance
3) Custom Accumulators - Version 1.x & Version 2.x
4) Examples of Broadcast Variables
5) Key Performance Considerations - Level of Parallelism
6) Serialization Format - Kryo
7) Memory Management
8) Hardware Provisioning
Apache Spark - Dataframes & Spark SQL - Part 2 | Big Data Hadoop Spark Tutori...CloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2sm9c61
This CloudxLab Introduction to Spark SQL & DataFrames tutorial helps you to understand Spark SQL & DataFrames in detail. Below are the topics covered in this slide:
1) Loading XML
2) What is RPC - Remote Process Call
3) Loading AVRO
4) Data Sources - Parquet
5) Creating DataFrames From Hive Table
6) Setting up Distributed SQL Engine
Apache Spark - Dataframes & Spark SQL - Part 1 | Big Data Hadoop Spark Tutori...CloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2sf2z6i
This CloudxLab Introduction to Spark SQL & DataFrames tutorial helps you to understand Spark SQL & DataFrames in detail. Below are the topics covered in this slide:
1) Introduction to DataFrames
2) Creating DataFrames from JSON
3) DataFrame Operations
4) Running SQL Queries Programmatically
5) Datasets
6) Inferring the Schema Using Reflection
7) Programmatically Specifying the Schema
Apache Spark - Running on a Cluster | Big Data Hadoop Spark Tutorial | CloudxLabCloudxLab
(Big Data with Hadoop & Spark Training: http://bit.ly/2IUsWca
This CloudxLab Running in a Cluster tutorial helps you to understand running Spark in the cluster in detail. Below are the topics covered in this tutorial:
1) Spark Runtime Architecture
2) Driver Node
3) Scheduling Tasks on Executors
4) Understanding the Architecture
5) Cluster Managers
6) Executors
7) Launching a Program using spark-submit
8) Local Mode & Cluster-Mode
9) Installing Standalone Cluster
10) Cluster Mode - YARN
11) Launching a Program on YARN
12) Cluster Mode - Mesos and AWS EC2
13) Deployment Modes - Client and Cluster
14) Which Cluster Manager to Use?
15) Common flags for spark-submit
Introduction to SparkR | Big Data Hadoop Spark Tutorial | CloudxLabCloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2LCTufA
This CloudxLab Introduction to SparkR tutorial helps you to understand SparkR in detail. Below are the topics covered in this tutorial:
1) SparkR (R on Spark)
2) SparkR DataFrames
3) Launch SparkR
4) Creating DataFrames from Local DataFrames
5) DataFrame Operation
6) Creating DataFrames - From JSON
7) Running SQL Queries from SparkR
Introduction to NoSQL | Big Data Hadoop Spark Tutorial | CloudxLabCloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2kyP2Ct
This CloudxLab Introduction to NoSQL tutorial helps you to understand NoSQL in detail. Below are the topics covered in this slide:
1) Introduction to NoSQL
2) Scaling Out vs Scaling Up
3) ACID - Properties of DB Transactions
4) RDBMS - Story
5) What is NoSQL?
6) Types Of NoSQL Stores
7) CAP Theorem
8) Serialization
9) Column Oriented Database
10) Column Family Oriented DataStore
Introduction to MapReduce - Hadoop Streaming | Big Data Hadoop Spark Tutorial...CloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2sh5b3E
This CloudxLab Hadoop Streaming tutorial helps you to understand Hadoop Streaming in detail. Below are the topics covered in this tutorial:
1) Hadoop Streaming and Why Do We Need it?
2) Writing Streaming Jobs
3) Testing Streaming jobs and Hands-on on CloudxLab
Introduction To TensorFlow | Deep Learning Using TensorFlow | CloudxLabCloudxLab
( Machine Learning & Deep Learning Specialization Training: https://goo.gl/6n3vko )
This CloudxLab TensorFlow tutorial helps you to understand TensorFlow in detail. Below are the topics covered in this tutorial:
1) Why TensorFlow?
2) What are Tensors?
3) What is TensorFlow?
4) Creating your First Graph
5) Linear Regression with TensorFlow
6) Implementing Gradient Descent using TensorFlow
7) Implementing Gradient Descent Using autodiff
8) Implementing Gradient Descent Using an Optimizer
9) Graph Visualization using TensorBoard
10) Name Scopes in TensorFlow
11) Modularity in TensorFlow
12) Sharing Variables in TensorFlow
Introduction to Deep Learning | CloudxLabCloudxLab
( Machine Learning & Deep Learning Specialization Training: https://goo.gl/goQxnL )
This CloudxLab Deep Learning tutorial helps you to understand Deep Learning in detail. Below are the topics covered in this tutorial:
1) What is Deep Learning
2) Deep Learning Applications
3) Artificial Neural Network
4) Deep Learning Neural Networks
5) Deep Learning Frameworks
6) AI vs Machine Learning
In this tutorial, we will learn the the following topics -
+ The Curse of Dimensionality
+ Main Approaches for Dimensionality Reduction
+ PCA - Principal Component Analysis
+ Kernel PCA
+ LLE
+ Other Dimensionality Reduction Techniques
In this tutorial, we will learn the the following topics -
+ Voting Classifiers
+ Bagging and Pasting
+ Random Patches and Random Subspaces
+ Random Forests
+ Boosting
+ Stacking
In this tutorial, we will learn the the following topics -
+ Training and Visualizing a Decision Tree
+ Making Predictions
+ Estimating Class Probabilities
+ The CART Training Algorithm
+ Computational Complexity
+ Gini Impurity or Entropy?
+ Regularization Hyperparameters
+ Regression
+ Instability
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
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.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
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.
2. Autoencoders
● 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
3. Autoencoders
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
5. Autoencoders
● 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
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. Autoencoders
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
8. Autoencoders
Let’s try to understand
“why constraining an
autoencoder during training pushes it to discover and exploit
patterns in the data”
With an example
Efficient Data Representations
9. Autoencoders
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
Efficient Data Representations
10. Autoencoders
● 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
Efficient Data Representations
11. Autoencoders
● 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
Efficient Data Representations
12. Autoencoders
● 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
Efficient Data Representations
13. Autoencoders
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 Herbert Simon
14. Autoencoders
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.
Efficient Data Representations
15. Autoencoders
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
Efficient Data Representations
16. Autoencoders
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
Efficient Data Representations
17. Autoencoders
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
Efficient Data Representations
19. Autoencoders
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
20. Autoencoders
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
21. Autoencoders
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.
22. Autoencoders
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
23. Autoencoders
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
25. Autoencoders
PCA with an Undercomplete Linear Autoencoder
If the autoencoder uses only linear activations and the cost function is
the Mean Squared Error (MSE), then it can be shown that it ends up
performing Principal Component Analysis
Now we will build a simple linear autoencoder to perform PCA on a 3D
dataset, projecting it to 2D
26. Autoencoders
>>> import tensorflow as tf
>>> from tensorflow.contrib.layers import fully_connected
>>> n_inputs = 3 # 3D inputs
>>> n_hidden = 2 # 2D codings
>>> n_outputs = n_inputs
>>> learning_rate = 0.01
>>> X = tf.placeholder(tf.float32, shape=[None, n_inputs])
>>> hidden = fully_connected(X, n_hidden, activation_fn=None)
>>> outputs = fully_connected(hidden, n_outputs, activation_fn=None)
>>> reconstruction_loss = tf.reduce_mean(tf.square(outputs - X)) # MSE
>>> optimizer = tf.train.AdamOptimizer(learning_rate)
>>> training_op = optimizer.minimize(reconstruction_loss)
>>> init = tf.global_variables_initializer()
PCA with an Undercomplete Linear Autoencoder
Run it on Notebook
27. Autoencoders
PCA with an Undercomplete Linear Autoencoder
The two things to note in the previous code are are:
● The number of outputs is equal to the number of inputs
● To perform simple PCA, we set activation_fn=None i.e., all neurons
are linear, and the cost function is the MSE.
28. Autoencoders
Now let’s load the dataset, train the model on the training set, and use it to
encode the test set i.e., project it to 2D
>>> X_train, X_test = [...] # load the dataset
>>> n_iterations = 1000
>>> codings = hidden # the output of the hidden layer provides the
codings
>>> with tf.Session() as sess:
init.run()
for iteration in range(n_iterations):
training_op.run(feed_dict={X: X_train}) # no labels
codings_val = codings.eval(feed_dict={X: X_test})
PCA with an Undercomplete Linear Autoencoder
Run it on Notebook
29. Autoencoders
Above figure shows the original 3D dataset (at the left) and the output of
the autoencoder’s hidden layer (i.e., the coding layer, at the right)
PCA with an Undercomplete Linear Autoencoder
30. Autoencoders
PCA with an Undercomplete Linear Autoencoder
As you can see, the autoencoder found the best 2D plane to project the
data onto, preserving as much variance in the data as it could just like PCA