Machine learning for developers using python and tensor flow
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Machine learning has been all over the news recently from beating humans at Go to diagnosing heart failure. I decided to learn what all the fuss was about and soon found that these amazing results were being achieved by Deep Learning Neural Networks and you can build them yourself even if you have no background in Data Science. In this talk I’ll explain what Machine Learning is and show you how to build a Deep Learning Neural Network from scratch to perform simple hand writing recognition using Keras and TensorFlow. It’s not as hard as you think!
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11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Apps Break Data
How information systems are built or acquired puts information, which is what they should be about, in a secondary place. Our language adapted accordingly, and we no longer talk about information systems but applications. Applications evolved in a way to break data into diverse fragments, tightly coupled with applications and expensive to integrate. The result is technical debt, which is re-paid by taking even bigger "loans", resulting in an ever-increasing technical debt. Software engineering and procurement practices work in sync with market forces to maintain this trend. This talk demonstrates how natural this situation is. The question is: can something be done to reverse the trend?
GNSS spoofing via SDR (Criptored Talks 2024)
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
Must Know Postgres Extension for DBA and Developer during Migration
Mydbops Opensource Database Meetup 16
Topic: Must-Know PostgreSQL Extensions for Developers and DBAs During Migration
Speaker: Deepak Mahto, Founder of DataCloudGaze Consulting
Date & Time: 8th June | 10 AM - 1 PM IST
Venue: Bangalore International Centre, Bangalore
Abstract: Discover how PostgreSQL extensions can be your secret weapon! This talk explores how key extensions enhance database capabilities and streamline the migration process for users moving from other relational databases like Oracle.
Key Takeaways:
* Learn about crucial extensions like oracle_fdw, pgtt, and pg_audit that ease migration complexities.
* Gain valuable strategies for implementing these extensions in PostgreSQL to achieve license freedom.
* Discover how these key extensions can empower both developers and DBAs during the migration process.
* Don't miss this chance to gain practical knowledge from an industry expert and stay updated on the latest open-source database trends.
Mydbops Managed Services specializes in taking the pain out of database management while optimizing performance. Since 2015, we have been providing top-notch support and assistance for the top three open-source databases: MySQL, MongoDB, and PostgreSQL.
Our team offers a wide range of services, including assistance, support, consulting, 24/7 operations, and expertise in all relevant technologies. We help organizations improve their database's performance, scalability, efficiency, and availability.
Contact us: info@mydbops.com
Visit: https://www.mydbops.com/
Follow us on LinkedIn: https://in.linkedin.com/company/mydbops
For more details and updates, please follow up the below links.
Meetup Page : https://www.meetup.com/mydbops-databa...
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Machine learning for developers using python and tensor flow
- 1. Machine Learning for Developers Using Python and Tensor Flow Keith Harrison https://twitter.com/KeithDHar
- 5. Linear Regression •Need advanced stats/maths knowledge •Feature engineering •Domain knowledge •Hard to train •Brittle
- 7. Number Image Recogniser Algorithm imageOfNumberOne = [[ 0, 0, 1, 0, 0], [ 0, 0, 1, 0, 0], [ 0, 0, 1, 0, 0], [ 0, 0, 1, 0, 0], [ 0, 0, 1, 0, 0]] imageOfNumberSeven = [[ 0, 1, 1, 1, 0], [ 0, 0, 0, 1, 0], [ 0, 0, 0, 1, 0], [ 0, 0, 0, 1, 0], [ 0, 0, 0, 1, 0]] def getNumberFromImageV1(imageArray): return 0 def getNumberFromImageV2(imageArray): sumOfArray = sum2dArray(imageArray) if(sumOfArray == 7): return 7 if(sumOfArray == 5): return 1 return 0 def sum2dArray(imageArray): sumOfArray = 0 for row in range (len(imageArray)): for col in range(len(imageArray[0])): sumOfArray = sumOfArray + imageArray[row][col] return sumOfArray;
- 10. 80s style Neural Network Demo 1 => [0, 1, 0, 0, 0, 0, 0, 0, 0, 0] One Hot Encoding 3 => [0, 0, 0, 1, 0, 0, 0, 0, 0, 0] 5 => [0, 0, 0, 0, 0, 1, 0, 0, 0, 0]
- 11. How did it learn? 1. Calculate loss: • Expected: 5, Actual: 1 • Loss = 5-1 = 4 2. Modify Weights and Biases to reduce Loss to 0
- 12. Gradient Decent
- 14. How did we do? World Record: 99.79% Source: http://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results
- 15. What’s going on? World Record: 99.79%
- 18. Links 3Blue1Brown, Neural Networks https://youtu.be/aircAruvnKk Fast AI http://www.fast.ai Computerphile - Generative Adversarial Networks (GANs) https://youtu.be/Sw9r8CL98N0 Image Kernals http://setosa.io/ev/image-kernels/ Amazon Sagemaker https://aws.amazon.com/sagemaker/
Editor's Notes
- The hype
- statistical methods, chapter at the end on deep learning
- free web based course, videos, samples code, forums 10 hours a week for 7 weeks models for computer vision, natural language processing, and recommendation systems Explains everything from scratch
- https://hackernoon.com/generative-adversarial-networks-a-deep-learning-architecture-4253b6d12347