( 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
( 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
The term Machine Learning was coined by Arthur Samuel in 1959, an american pioneer in the field of computer gaming and artificial intelligence and stated that “ it gives computers the ability to learn without being explicitly programmed” And in 1997, Tom Mitchell gave a “ well-Posed” mathematical and relational definition that “ A Computer Program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E”.
Machine learning is needed for tasks that are too complex for humans to code directly. So instead, we provide a large amount of data to a machine learning algorithm and let the algorithm work it out by exploring that data and searching for a model that will achieve what the programmers have set it out to achieve.
( 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
The term Machine Learning was coined by Arthur Samuel in 1959, an american pioneer in the field of computer gaming and artificial intelligence and stated that “ it gives computers the ability to learn without being explicitly programmed” And in 1997, Tom Mitchell gave a “ well-Posed” mathematical and relational definition that “ A Computer Program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E”.
Machine learning is needed for tasks that are too complex for humans to code directly. So instead, we provide a large amount of data to a machine learning algorithm and let the algorithm work it out by exploring that data and searching for a model that will achieve what the programmers have set it out to achieve.
Semi supervised learning machine learning made simpleDevansh16
Video: https://youtu.be/65RV3O4UR3w
Semi-Supervised Learning is a technique that combines the benefits of supervised learning (performance, intuitiveness) with the ability to use cheap unlabeled data (unsupervised learning). With all the cheap data available, Semi Supervised Learning will get bigger in the coming months. This episode of Machine Learning Made Simple will go into SSL, how it works, transduction vs induction, the assumptions SSL algorithms make, and how SSL compares to human learning.
About Machine Learning Made Simple:
Machine Learning Made Simple is a playlist that aims to break down complex Machine Learning and AI topics into digestible videos. With this playlist, you can dive head first into the world of ML implementation and/or research. Feel free to drop any feedback you might have down below.
This talk is about how we applied deep learning techinques to achieve state-of-the-art results in various NLP tasks like sentiment analysis and aspect identification, and how we deployed these models at Flipkart
An introduction to immediate-reward reinforcement learning. Covers introductions, motivation, challenges with full RL, contextual bandits, policy evaluation, and architectural considerations.
This presentation is the part of the webinar conducted by CloudxLab. This was the free session on Machine Learning.
Cloudxlab conducts such webinars very frequently and to make sure you never miss the future webinar update, please see the 'Events' section at CloudxLab.com
Machine Learning for Designers - UX Camp SwitzerlandMemi Beltrame
Artificial intelligence is more and more becoming the core of digital products. Designing for Products based on AI requires Designers to know about Machine Learning.
This talk is an easy walk through the most important elements of Machine Learning. It looks at the fundamental principles of using practical examples. It showcases applications of the different types of Machine Learning. The use-cases range from text categorization to image recognition, on to speech analysis. The goal is to show what is important for designers and why.
Semi supervised learning machine learning made simpleDevansh16
Video: https://youtu.be/65RV3O4UR3w
Semi-Supervised Learning is a technique that combines the benefits of supervised learning (performance, intuitiveness) with the ability to use cheap unlabeled data (unsupervised learning). With all the cheap data available, Semi Supervised Learning will get bigger in the coming months. This episode of Machine Learning Made Simple will go into SSL, how it works, transduction vs induction, the assumptions SSL algorithms make, and how SSL compares to human learning.
About Machine Learning Made Simple:
Machine Learning Made Simple is a playlist that aims to break down complex Machine Learning and AI topics into digestible videos. With this playlist, you can dive head first into the world of ML implementation and/or research. Feel free to drop any feedback you might have down below.
This talk is about how we applied deep learning techinques to achieve state-of-the-art results in various NLP tasks like sentiment analysis and aspect identification, and how we deployed these models at Flipkart
An introduction to immediate-reward reinforcement learning. Covers introductions, motivation, challenges with full RL, contextual bandits, policy evaluation, and architectural considerations.
This presentation is the part of the webinar conducted by CloudxLab. This was the free session on Machine Learning.
Cloudxlab conducts such webinars very frequently and to make sure you never miss the future webinar update, please see the 'Events' section at CloudxLab.com
Machine Learning for Designers - UX Camp SwitzerlandMemi Beltrame
Artificial intelligence is more and more becoming the core of digital products. Designing for Products based on AI requires Designers to know about Machine Learning.
This talk is an easy walk through the most important elements of Machine Learning. It looks at the fundamental principles of using practical examples. It showcases applications of the different types of Machine Learning. The use-cases range from text categorization to image recognition, on to speech analysis. The goal is to show what is important for designers and why.
Artificial intelligence is more and more becoming the core of digital products. Designing for Products based on AI requires Designers to know about Machine Learning.
This talk is an easy walk through the most important elements of Machine Learning. It looks at the fundamental principles of using practical examples. It showcases applications of the different types of Machine Learning. The use-cases range from text categorization to image recognition, on to speech analysis. The goal is to show what is important for designers and why.
Machine Learning for Designers - UX ScotlandMemi Beltrame
Artificial intelligence is more and more becoming the core of digital products. Designing for Products based on AI requires Designers to know about Machine Learning.
This talk is an easy walk through the most important elements of Machine Learning. It looks at the fundamental principles of using practical examples. It showcases applications of the different types of Machine Learning. The use-cases range from text categorization to image recognition, on to speech analysis. The goal is to show what is important for designers and why.
IgmGuru takes great pride in introducing the well-curated deep learning with tensorflow training in which industry leaders and academia has been consulted while preparing this course.
IgmGuru takes great pride in introducing the well-curated Deep Learning with TensorFlow course in which industry leaders and academia has been consulted while preparing this course. IgmGuru is very enthusiastic about the Deep Learning with TensorFlow course as we will go through some of the famous use cases and prepare the learners to face industry-related challenges. Deep Learning is loosely inspired by the ways humans process information and then communicate through our own biological neural networks. These learning algorithms are able to process vast amounts of data to build meaningful relationships amongst them.
IgmGuru takes great pride in introducing the well-curated deep learning with tensorflow course in which industry leaders and academia has been consulted while preparing this course. IgmGuru is very enthusiastic about the Deep Learning with TensorFlow course as we will go through some of the famous use cases and prepare the learners to face industry-related challenges. Deep Learning is loosely inspired by the ways humans process information and then communicate through our own biological neural networks. These learning algorithms are able to process vast amounts of data to build meaningful relationships amongst them.
https://www.igmguru.com/machine-learning-ai/deep-learning-tensorflow-training/
In This Data Science course ( Graduate Program ) I will focus on understanding business intelligence systems and helping future managers use and understand analytics, Business Intelligence emphasizing the applications and implementations behind the concepts. a solid foundation of BI that is reinforced with hands-on practice. The course is also designed as an introduction to programming and statistics for students from many different majors. It teaches practical techniques that apply across many disciplines and also serves as the technical foundation for more advanced courses in data science, statistics, and computer science.
How To Optimize Your Tech Recruiting Stack
Patrick Christell, Senior Sourcer at Hire4ce, meets all the qualifications of “MASTER.”
We’re talking a Full-Lifecycle Recruiter, Project Manager and Agile sourcing pod-builder with seven-plus years of progressive experience recruiting for technology companies across the boards.
He also has a rather impressive tech stack, which is what this is all about.
Patrick is here to give you 60-minutes of training and live Q&A that will help you learn to recruit top talent.
In this webinar we will cover:
- How to search.
Tools like Hiretual, Seekout, AmazingHiring (and their plusses and minuses).
The difference between searching for senior-level engineers, how to know if you are on a purple squirrel hunt, and what to with a BONUS live demo that iterates a single string.
- How to run a sourcing pod.
Learn how Patrick creates his own CRM that can do outreach and reporting
- How to understand tech without being a techie.
What a software stack even is, understanding how it fits together, learning what each part of the stack technologies are associated with.
- How to engage talent.
Why a mixture of broad spectrum outreach and personalized outreach is best.
What cadence works best in 2019.
Why only using inmails screws you, and how to leverage the phone even if you hate using it (TextNow).
Nobody’s got time for a floppy stack.
Let Patrick show you how to build in functionality and results.
MICROLEARNING FOR TRANSFORMATION, NOT INFORMATION TRANSFERHuman Capital Media
The modern employee has 1% of their week to focus on training. What can they do with that roughly 24 minutes a week? Turns out, a lot. Armed with digestible and easily accessible microlearning experiences, we can create meaningful changes in behavior across our organizations. Along the way, we can help elevate the role of L&D from order takers to change makers.
Join Alex Khurgin, Director of Learning Innovation at Grovo, as he explains the importance of leveraging microlearning when training modern employees and how to create a microlearning strategy of your own to meet the needs of your audience and goals of your company.
In this session, you’ll learn to:
Overcome the three misconceptions that block most L&D initiatives from being successful
Create microlearning experiences that capture attention, motivate action, and make learninstick
Prove and report on behavior change, not meaningless learning metrics
In this Python Machine Learning Tutorial, Machine Learning also termed ML. It is a subset of AI (Artificial Intelligence) and aims to grants computers the ability to learn by making use of statistical techniques. It deals with algorithms that can look at data to learn from it and make predictions.
Artificial intelligence is more and more becoming the core of digital products. Designing for Products based on AI requires Designers to know about Machine Learning.
This talk is an easy walk through the most important elements of Machine Learning. It looks at the fundamental principles of using practical examples. It showcases applications of the different types of Machine Learning. The use-cases range from text categorization to image recognition, on to speech analysis. The goal is to show what is important for designers and why.
Similar to Introduction to Deep Learning | CloudxLab (20)
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?
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
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
In this tutorial, we will learn the the following topics -
+ Linear SVM Classification
+ Soft Margin Classification
+ Nonlinear SVM Classification
+ Polynomial Kernel
+ Adding Similarity Features
+ Gaussian RBF Kernel
+ Computational Complexity
+ SVM Regression
Introduction to Linux | Big Data Hadoop Spark Tutorial | CloudxLabCloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2wLh5aF
This CloudxLab Introduction to Linux helps you to understand Linux in detail. Below are the topics covered in this tutorial:
1) Linux Overview
2) Linux Components - The Programs, The Kernel, The Shell
3) Overview of Linux File System
4) Connect to Linux Console
5) Linux - Quick Start Commands
6) Overview of Linux File System
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/
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
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
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.
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.
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.
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.
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.
2. Welcome to first session on Deep Learning
While other are joining, Please enroll for the free lab. This is needed for the hands on session.
Open CloudxLab.com
Also, please introduce yourself using the chat window and use the Q/A window for asking questions.
11. TensorFlow
Getting Started with free Lab
1. Open CloudxLab
2. If already Enrolled, go to step 5
3. Else Click on "Start Free Lab"
a. And Complete the process of enrollment
b. You might have sign using credit card or college id
4. Go to MyLab
5. Open Jupyter
20. Deep Learning
How About Automating it?
● So, the program learnt to play
○ Mario
○ And Other games
○ Without any programming
21. Deep Learning
Question
To make this program learn any other games such as PacMan we will have to
1. Write new rules as per the game
2. Just hook it to new game and let it play for a while
22. Deep Learning
Question
To make this program learn any other games such as PacMan we will have to
1. Write new rules as per the game
2. Just hook it to new game and let it play for a while
31. Deep Learning
Collect Data - IOT
Phone & Devices
Cheaper, faster and smaller
Connectivity
Wifi, 4G, NFC, GPS
32. Deep Learning
Process Data - Parallel Computing
• Groups of networked computers
• Interact with each other
• To achieve a common goal
Distributed
33. Deep Learning
Process Data - Parallel Computing
• Many processors or Cores
• Perform tasks and interact using
• Memory or bus
Memory
Processor Processor Processor
Multi Core + GPGPU (General Purpose Graphics Processing Units)
34. Deep Learning
Process Data - Parallel Computing
MULTI CORE GPGPU DISTRIBUTED
CAN HANDLE HUGE DATA?
(DISK READ INTENSIVE)
REALLY FAST
COMMUNICATION BETWEEN
CPUS
GREAT FOR MATHS/GRAPHICS?
TOOLS
Hadoop MR,
Apache Spark
Keras,
TensorFlow,
Caffe, Spark
(Exp)
Hadoop MR,
Apache Spark
38. Deep Learning
Intelligence - Spam Filter - Traditional Approach
● Problem is not trivial
○ Program will likely become a long list of complex rules
○ Pretty hard to maintain
● If spammers notice that
○ All their emails containing “4U” are blocked
○ They might start writing “For U” instead
○ If spammers keep working around spam filter, we will need to keep writing
new rules forever
Problems?
40. Deep Learning
Intelligence - Spam Filter - Deep Learning Approach
● A spam filter based on Machine Learning techniques automatically learns
○ Which words and phrases are good predictors of spam
○ By detecting unusually frequent patterns of words
● The program will be
○ Much shorter
○ Easier to maintain
○ Most likely more accurate than traditional approach
41. Deep Learning
Intelligence - Spam Filter - Deep Learning Approach
● Unlike traditional approach, Deep Learning techniques automatically notice that
○ “For U” has become unusually frequent in spam flagged by users and
○ It starts flagging them without our intervention
42. Deep Learning
Intelligence - Spam Filter - Deep Learning Approach
Can help humans learn
● Deep Learning algorithms can be inspected to see what they have learned
● Spam filter after enough training
○ Reveals combinations of words that it believes are best predictors of spam
○ May reveal unsuspected correlations or new trend and
○ Lead to a better understanding of the problem for humans
44. Deep Learning
Deep Learning - Artificial Neural Network(ANN)
Computing systems inspired by the biological neural networks that constitute animal
brains.
45. Deep Learning
Deep Learning - Artificial Neural Network(ANN)
• Learn (progressively improve
performance)
• To do tasks by considering examples
• Generally without task-specific
programming
• Example: Based on image - cat or no
cat?
57. Deep Learning
Machine Learning - Types
Human Supervision?
Supervised
Machine Learning
Unsupervised
Reinforcement
How they generalize?
Learn Incrementally?
59. Deep Learning
Machine Learning - Types
Human Supervision?
Supervised
Machine Learning
Unsupervised
Reinforcement
Classification
Regression
How they generalize?
Learn Incrementally?
60. Deep Learning
Machine Learning - Supervised Learning
Classification
● The training data we feed to the algorithm includes
○ The desired solutions, called labels
● Classification of spam filter is a supervised learning task
61. Deep Learning
Machine Learning - Supervised Learning
Classification
● Spam filter
○ Is trained with many example emails called training data.
○ Each email in the training data contains the label if it is spam or ham(not spam)
○ Models then learns to classify new emails if they are spam or ham
Classify new email as
Ham or Spam
63. Deep Learning
Machine Learning - Supervised Learning
Regression
● Predict price of the car
○ Given a set of features called predictors such as
○ Mileage, age, brand etc
● To train the model
○ We have to give many examples of cars
○ Including their predictors and labels(prices)
64. Deep Learning
Machine Learning - Types
Human Supervision?
Supervised
Machine Learning
Unsupervised
Reinforcement
Classification
Regression
How they generalize?
Learn Incrementally?
65. Deep Learning
Machine Learning - Unsupervised Learning
● The training data is unlabeled
● The system tries to learn without a teacher
66. Deep Learning
Machine Learning - Types
Human Supervision?
Supervised
Machine Learning
Unsupervised
Reinforcement
Classification
Regression
Clustering
How they generalize?
Learn Incrementally?
68. Deep Learning
Machine Learning - Unsupervised Learning
Clustering
● Detect group of similar visitors in blog
○ Notice the training set is unlabeled
● To train the model
○ We just feed the training set to clustering algorithm
○ At no point we tell the algorithm which group a visitor belongs to
○ It find groups without our help
69. Deep Learning
Machine Learning - Unsupervised Learning
Clustering
● It may notice that
○ 40% visitors are comic lovers and read the blog in evening
○ 20% visitors are sci-fi lovers and read the blog during weekends
● This data helps us in targeting our blog posts for each group
70. Deep Learning
Machine Learning - Unsupervised Learning
• In the form of a tree
• Nodes closer to each other are similar
Hierarchical Clustering - Bring similar elements together
71. Deep Learning
Machine Learning - Unsupervised Learning
Anomaly Detection - Detecting unusual credit card transactions to prevent
fraud
72. Deep Learning
Machine Learning - Types
Human Supervision?
Supervised
Machine Learning
Unsupervised
Reinforcement
Classification
Regression
Clustering
How they generalize?
Learn Incrementally?
73. Deep Learning
Machine Learning - Types
Human Supervision?
Supervised
Machine Learning
Unsupervised
Reinforcement
Classification
Regression
Clustering
Batch Processing
How they generalize?
Learn Incrementally?
Online
74. Deep Learning
What Is Machine Learning?
Field of study that gives "computers the ability to
learn without being explicitly programmed"
-- Arthur Samuel, 1959
75. Deep Learning
Machine Learning - Gradient Descent
• Instead of trying all lines, go into
the direction yielding better
results
76. Deep Learning
Machine Learning - Gradient Descent
● Imagine yourself blindfolded on the
mountainous terrain
● And you have to find the best lowest
point
● If your last step went higher, you will
go in opposite direction
● Other, you will keep going just faster
77. Deep Learning
What is AI?
Artificial intelligence (AI):
The intelligence exhibited by machines
79. Deep Learning
What is AI?
• The theory and development of
computer systems
• To perform tasks requiring human
intelligence such as
80. Deep Learning
What is AI?
• The theory and development of
computer systems
• To perform tasks requiring human
intelligence such as
• Visual perception
81. Deep Learning
What is AI?
• The theory and development of
computer systems
• To perform tasks requiring human
intelligence such as
• Visual perception
• Speech Recognition
82. Deep Learning
What is AI?
• The theory and development of
computer systems
• To perform tasks requiring human
intelligence such as
• Visual perception
• Speech Recognition
• Decision Making
83. Deep Learning
What is AI?
• The theory and development of
computer systems
• To perform tasks requiring human
intelligence such as
• Visual perception
• Speech Recognition
• Decision Making
• Translation between languages
84. Deep Learning
History - Mythology / Fiction
• In every mythology, there is some
form of mechanical man such talos
from greek mythology.
• In fiction novels, we have Mary
Shelley’s Frankenstein
• We are fascinated by the idea of
creating things which can behave like
human
85. Deep Learning
History - Summer of 1956
• The term artificial intelligence was
coined by
• John McCarthy
• In a workshop at
• Dartmouth College in New
Hampshire
• Along with Marvin Minsky,
Claude Shannon, and Nathaniel
Rochester
86. Deep Learning
Sub-objectives of AI
Artificial
Intelligence
Natural
language
processing
Navigate
Represent
Knowledge
ReasoningPerception
87. Deep Learning
AI - Represent Knowledge
• Understanding and classifying terms or
things in world e.g.
• What is computer?
• What is a thought?
• What is a tool?
• Languages like lisp were created for the
same purpose
88. Deep Learning
AI - Reasoning
• Play puzzle game - Chess, Go, Mario
• Prove Geometry theorems
• Diagnose diseases
89. Deep Learning
AI - Navigate
• How to plan and navigate in the real
world
• How to locate the destination?
• How to pick path?
• How to pick short path?
• How to avoid obstacles?
• How to move?
90. Deep Learning
AI - Natural Language Processing
• How to speak a language
• How to understand a language
• How to make sense out of a sentence
91. Deep Learning
AI - Perception
• How to we see things in the real world
• From sound, sight, touch, smell
92. Deep Learning
AI - Generalised Intelligence
• With these previous building blocks, the
following should emerge:
• Emotional Intelligence
• Creativity
• Reasoning
• Intuition
93. Deep Learning
AI - How to Achieve
Artificial Intelligence
Machine Learning
Rule Based Systems
Expert System
Domain Specific
Computing
Robotics
Deep
Learning
95. Deep Learning
Deep Learning - Reinforcement Learning
● The learning system an agent in this context
○ Observes the environment
○ Selects and performs actions and
○ Get rewards or penalties in return
○ Learns by itself what is the best strategy (policy) to get most reward over time
96. Deep Learning
Deep Learning - Reinforcement Learning
Applications
● Used by robots to learn how to walk
● DeepMind’s AlphaGo
○ Which defeated world champion Lee Sedol at the game of Go