Designing Machine Learning Systems Pdf

Overview of machine learning systems. In november 2016, google announced that it had incorporated its multilingual neural machine translation system into google translate, marking one of the first success stories of deep artificial neural networks in production at scale. 1 according to google, with this update, the quality of translation improved more in a single leap. An open source book compiled by chip huyen. This booklet covers four main steps of designing a machine learning system: Selecting, training, and debugging. Testing, deploying, and maintaining. It comes with links to practical resources that explain each aspect in more.

1 according to google, with this update, the quality of translation improved more in a single leap. An open source book compiled by chip huyen. This booklet covers four main steps of designing a machine learning system: Selecting, training, and debugging. Testing, deploying, and maintaining. It comes with links to practical resources that explain each aspect in more. Designing machine learning systems (chip huyen 2022) machine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders. Unique because they're data dependent, with data varying wildly from one use case to the next. The book of the week from 27 jun 2022 to 01 jul 2022.

In november 2016, google announced that it had incorporated its multilingual neural machine translation system into google translate, marking one of the first success stories of deep artificial neural networks in production at scale. 1 according to google, with this update, the quality of translation improved more in a single leap. An open source book compiled by chip huyen. This booklet covers four main steps of designing a machine learning system: Selecting, training, and debugging. Testing, deploying, and maintaining. It comes with links to practical resources that explain each aspect in more. Designing machine learning systems (chip huyen 2022) machine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders.

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Designing Machine Learning Systems | book summary | Read a book with me



Hi, in this video, I am going to summarize the book Designing Machine Learning Systems by Chip Huyen. This book covers a lot of machine learning system-related information including data formats, how to deal with imbalanced data, how to deal with missing data, how to evaluate models, how to improve model performance, how to monitor and test models in production, how to deal with data shifts, how to do continual learning, and many more. I’m going to summarize and highlight key points of the book, especially the following book chapters:
- Data Engineering Fundamentals
- How to create training data
- Feature Engineering
- Model Development and Offline Evaluation
- Model Deployment and Prediction Service
- Data Distribution Shifts and Monitoring
- Continual Learning and Test in Production
Hope you find this video helpful. Thanks for watching 🤗

📚 Book link 📚
- amzn.to/3mRH7QD

✨ Mentioned in the video✨
- My video on Thompson Sampling: youtube.com/watch?v=TdjOAfk7iVA
- My video on model behavioral testing: youtube.com/watch?v=MecK4Bbw3Fs
- My blog post on PyScript: anaconda.cloud/pyscript-python-in-the-browser

🛠 My gear 🛠
- Tablet (newer version): amzn.to/3bMQ0ZB
- Computer: amzn.to/39sTujd
- Camera: amzn.to/3xx1QOH
- Hue lights: amzn.to/3ba3H4E

🌼 About me 🌼
Sophia Yang is a Senior Data Scientist working at a tech company.

🔔 SUBSCRIBE to my channel: youtube.com/c/SophiaYangDS?sub_confirmation=1

⭐ Stay in touch ⭐
📚 DS/ML Book Club: discord.com/invite/6BremEf9db
▶ YouTube: youtube.com/SophiaYangDS
✍️ Medium: sophiamyang.medium.com
🐦 Twitter: twitter.com/sophiamyang
🤝 Linkedin: linkedin.com/in/sophiamyang/
📧 Email: [email protected]
💚 #datascience #machinelearning

Machine Learning System Design (YouTube Recommendation System)



As an excellent Machine Learning System Design example, I am going through the following paper:

"Recommending What Video to Watch Next: A Multitask Ranking System" by Google Inc. presented at RecSys 2019.

[PDF] daiwk.github.io/assets/youtube-multitask.pdf

// MY RECOMMENDATIONS:
Great "Machine Learning" Book:
📚 Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: amzn.to/3pwbjjE

Great "System Design" Book:
📚 Designing Data-Intensive Applications: amzn.to/3n05KYW

#machinelearning #systemdesign #recommendationsystem #recommendersystem #recommendation #rankingsystem #ranking #google #youtube #ai #ml #artificialintelligence #mlsystemdesign

Principles of Good Machine Learning Systems Design



This talk covers what it means to operationalize ML models. It starts by analyzing the difference between ML in research vs. in production, ML systems vs. traditional software, as well as myths about ML production.

It then goes over the principles of good ML systems design and introduces an iterative framework for ML systems design, from scoping the project, data management, model development, deployment, maintenance, to business analysis. It covers the differences between DataOps, ML Engineering, MLOps, and data science, and where each fits into the framework.

The talk ends with a survey of the ML production ecosystem, the economics of open source, and open-core businesses.

About:
Databricks provides a unified data analytics platform, powered by Apache Spark™, that accelerates innovation by unifying data science, engineering and business.
Read more here: databricks.com/product/unified-data-analytics-platform

See all the previous Summit sessions: databricks.com/sparkaisummit/north-america/sessions

Connect with us:
Website: databricks.com
Facebook: facebook.com/databricksinc
Twitter: twitter.com/databricks
LinkedIn: linkedin.com/company/databricks/
Instagram: instagram.com/databricksinc/ Databricks is proud to announce that Gartner has named us a Leader in both the 2021 Magic Quadrant for Cloud Database Management Systems and the 2021 Magic Quadrant for Data Science and Machine Learning Platforms. Download the reports here. databricks.com/databricks-named-leader-by-gartner

Google Machine Learning System Design Mock Interview



Today I am interviewing Dan for a second time on a machine learning system design problem centered around Youtube recommendations.

Want to be featured in the next mock interview video? Apply here: airtable.com/shrdQrwKK7xxGLm6l

👉 Subscribe to my data science channel: bit.ly/2xYkyUM
🔥 Get 10% off machine learning interview prep: interviewquery.com/?ref=datasciencejay

❓ Check out our ML interview course: interviewquery.com/courses/data-science-course/lessons/modeling
🔑 Get professional coaching here: interviewquery.com/coaching
🐦 Follow us on Twitter: twitter.com/interview_query

We go over how to build a recommendation system for Youtube, what kind of machine learning algorithms we would use, what the edge cases would be, how we could account for performance and scaling, and much more! For the full video please check out Interview Query

Quick Links:
0:55 - Youtube Recommendations Interview Question
3:35 - Collaborative Filtering
10:50 - Cold Start Problem?

More from Jay:
Read my personal blog: datastream.substack.com/
Follow me on Linkedin: linkedin.com/in/jay-feng-ab66b049/
Find me on Twitter: twitter.com/datasciencejay

Related Links:
Google ML Engineer Interview Profile: interviewquery.com/interview-experiences/google/machine-learning-engineer
Google data scientist interview: interviewquery.com/blog-the-google-data-scientist-interview

Machine Learning System Design Interview: YouTube Recommendations



Today I’m joined by Sachin, a senior data scientist. We’ll go over a machine learning system design question on how to build YouTube’s homepage recommendations system and optimize it.

Watch the rest on the question page: interviewquery.com/questions/youtube-recommendations

Previous YouTube Recommendations interview with Dan: youtu.be/9TxN1VoPYq4

Follow Sachin on LinkedIn: linkedin.com/in/venugopal-mani-umn/

His sources:
HRNN Paper: openreview.net/pdf?id=8QFKbygVy4r
Prof Weinberger's channel: youtube.com/channel/UC7p_I0qxYZP94vhesuLAWNA/featured
Prof. Justin Johnson's course: youtube.com/watch?v=dJYGatp4SvA&list=PL5-TkQAfAZFbzxjBHtzdVCWE0Zbhomg7r

Want to be featured in the next mock interview video? Apply here: airtable.com/shrdQrwKK7xxGLm6l

👉 Subscribe to my data science channel: bit.ly/2xYkyUM
🔥 Get 10% off machine learning interview prep: interviewquery.com/?ref=datasciencejay

❓ Check out our ML interview course: interviewquery.com/courses/data-science-course/lessons/modeling
🔑 Get professional coaching for your next interview: interviewquery.com/coaching
🐦 Follow us on Twitter: twitter.com/interview_query

00:00 - Question
00:17 - Clarifying Questions
03:57 - System Architecture of an ideal recommender
13:24 - Various Components of the architecture
26:25 - Online/Offline evaluation of the recommendation
30:03 - AB Test Launch

More from Jay:
Read my personal blog: datastream.substack.com/
Follow me on Linkedin: linkedin.com/in/jay-feng-ab66b049/
Find me on Twitter: twitter.com/datasciencejay

#DataScience #MachineLearning #SystemDesign #DataScientist #DataEngineer #MachineLearningEngineer

Data Scientist Interview - Machine Learning Project System Design | FAANG Mock Interview



This is going to be a different videos from my regular Coding tutorials. Today we are talking about High-Level System design round that most FAANG companies do for Data Scientist or Machine Learning Engineers role. This round would be ideally your second or third round.

Sometimes, High Level System design is before or after the Low-Level / Coding rounds.

In this video we'll design a system "Design an End-to-End ML System
To Detect Faulty Mobile Parts in the Manufacturing"

How to Design a Neural Network | 2020 Edition



In this video, I covered some of the useful neural network design techniques that came out or popularized between 2018 and 2020. At the end of the video, I went through some of our recent papers and explained how my colleagues and I designed neural networks for constrained environments.

Earlier video:
How to Design a Convolutional Neural Network
youtu.be/fTw3K8D5xDs

My work mentioned in the video:
Seeing Through the Clouds With DeepWaterMap
isikdogan.com/files/isikdogan2019_deepwatermap_v2.pdf

A Machine Learning Imaging Core using Separable FIR-IIR Filters
arxiv.org/pdf/2001.00630.pdf

Other papers referenced in the video:
ShuffleNetV2: Practical Guidelines for Efficient CNN Architecture Design
arxiv.org/pdf/1807.11164.pdf

MobileNetV2: Inverted Residuals and Linear Bottlenecks
arxiv.org/pdf/1801.04381.pdf

Learning Transferable Architectures for Scalable Image Recognition (NASNet)
arxiv.org/pdf/1707.07012.pdf

Attention Is All You Need (the Transformer)
arxiv.org/pdf/1706.03762.pdf

Squeeze-and-Excitation Networks
arxiv.org/pdf/1709.01507.pdf

Searching for MobileNetV3
arxiv.org/pdf/1905.02244.pdf

How Recommender Systems Work (Netflix/Amazon)



The key insights behind content and collaborative filtering (Matrix Factorization). How Amazon, Netflix, Facebook and others predict what you will like.

Paper in this video:
Matrix Factorization Techniques for Recommender Systems
inf.unibz.it/~ricci/ISR/papers/ieeecomputer.pdf

Artificial Intelligence Colloquium: Radio Frequency Machine Learning Systems



Speaker: Mr. Enrico Mattei, Senior Research Scientist, Expedition Technology

DARPA is developing the foundations for applying second-wave machine learning to the RF spectrum domain. This talk will focus on technical insights and innovations necessary to apply machine learning to the RF domain, as well as review some initial results in applying these techniques to RF fingerprinting.

darpa.mil/program/radio-frequency-machine-learning-systems

Deep Learning in Bioinformatics | Recent Advancement



Google Slide:
docs.google.com/presentation/d/1j_JOlDDKEOukmfq9uDlcrX5mSpLxxGEQmE61JueOaHA/edit#slide=id.p

References
1. Tang, B., Pan, Z., Yin, K. and Khateeb, A., 2019. Recent advances of deep learning in bioinformatics and computational biology. Frontiers in genetics, 10, p.214.
2. Alipanahi, B., Delong, A., Weirauch, M.T. and Frey, B.J., 2015. Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning. Nature biotechnology, 33(8), pp.831-838.
3. Park, S., Koh, Y., Jeon, H., Kim, H., Yeo, Y. and Kang, J., 2020. Enhancing the interpretability of transcription factor binding site prediction using attention mechanism. Scientific reports, 10(1), pp.1-4.
4. Lin, E., Mukherjee, S. and Kannan, S., 2020. A deep adversarial variational autoencoder model for dimensionality reduction in single-cell RNA sequencing analysis. BMC bioinformatics, 21(1), pp.1-11.
5. Wan, J.J., Chen, B.L., Kong, Y.X., Ma, X.G. and Yu, Y.T., 2019. An early intestinal cancer prediction Algorithm Based on Deep Belief network. Scientific reports, 9(1), pp.1-13.
6. Chen, G., Tsoi, A., Xu, H. and Zheng, W.J., 2018. Predict effective drug combination by deep belief network and ontology fingerprints. Journal of biomedical informatics, 85, pp.149-154.

Image used in Video
1. Aphex34, CC BY-SA 4.0 creativecommons.org/licenses/by-sa/4.0, via Wikimedia [email protected] upload.wikimedia.org/wikipedia/commons/6/63/Typical_cnn.png
2. researchgate.net/publication/320658590_Deep_Clustering_with_Convolutional_Autoencoders/figures?lo=1&utm_source=google&utm_medium=organic
3. sciencedirect.com/science/article/pii/S0019057819302903
4. Glosser.ca, CC BY-SA 3.0 creativecommons.org/licenses/by-sa/3.0, via Wikimedia Commons
5. frontiersin.org/files/Articles/420104/fgene-10-00214-HTML/image_m/fgene-10-00214-g007.jpg

Futher Reading:
1. towardsdatascience.com/what-is-deep-learning-and-how-does-it-work-2ce44bb692ac
2. medium.com/tensorflow/mit-deep-learning-basics-introduction-and-overview-with-tensorflow-355bcd26baf0
3. jeremyjordan.me/autoencoders/
4. stats.stackexchange.com/questions/51273/what-is-the-difference-between-a-neural-network-and-a-deep-belief-network

Email: [email protected]
Website: liquidbrain.org/videos
Patreon: patreon.com/liquidbrain

More information:
bit.ly/Brandon_Yeo

Machine Learning Design Patterns



Design patterns capture best practices and solutions to recurring problems. Join us for the tech talks and Q&As, by the authors of the newly released O’Reilly book “Machine Learning Design Patterns”, covering solutions to common challenges in Data Preparation, Model Building, and MLOps.

Lak, Sara and Michael will introduce three of these tried-and-proven methods to help engineers tackle problems that frequently crop up during the ML process. They will dive into the pattern: a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation.

Talk #1: Data Representation Design Pattern: Embeddings, by Valliappa Lakshmanan
Talk #2: Reproducibility Design Pattern: Model Versioning, by Sara Robinson
Talk #3: Problem Representation Design Pattern: Multilabel, by Michael Munn

learn.xnextcon.com/event/eventdetails/W2021011911

Machine Learning Full Course - Learn Machine Learning 10 Hours | Machine Learning Tutorial | Edureka



🔥 Machine Learning Engineer Masters Program (Use Code "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎"): edureka.co/masters-program/machine-learning-engineer-training
This Edureka Machine Learning Full Course video will help you understand and learn Machine Learning Algorithms in detail. This Machine Learning Tutorial is ideal for both beginners as well as professionals who want to master Machine Learning Algorithms. Below are the topics covered in this Machine Learning Tutorial for Beginners video:
00:00 Introduction
2:47 What is Machine Learning?
4:08 AI vs ML vs Deep Learning
5:43 How does Machine Learning works?
6:18 Types of Machine Learning
6:43 Supervised Learning
8:38 Supervised Learning Examples
11:49 Unsupervised Learning
13:54 Unsupervised Learning Examples
16:09 Reinforcement Learning
18:39 Reinforcement Learning Examples
19:34 AI vs Machine Learning vs Deep Learning
22:09 Examples of AI
23:39 Examples of Machine Learning
25:04 What is Deep Learning?
25:54 Example of Deep Learning
27:29 Machine Learning vs Deep Learning
33:49 Jupyter Notebook Tutorial
34:49 Installation
50:24 Machine Learning Tutorial
51:04 Classification Algorithm
51:39 Anomaly Detection Algorithm
52:14 Clustering Algorithm
53:34 Regression Algorithm
54:14 Demo: Iris Dataset
1:12:11 Stats & Probability for Machine Learning
1:16:16 Categories of Data
1:16:36 Qualitative Data
1:17:51 Quantitative Data
1:20:55 What is Statistics?
1:23:25 Statistics Terminologies
1:24:30 Sampling Techniques
1:27:15 Random Sampling
1:28:05 Systematic Sampling
1:28:35 Stratified Sampling
1:29:35 Types of Statistics
1:32:21 Descriptive Statistics
1:37:36 Measures of Spread
1:44:01 Information Gain & Entropy
1:56:08 Confusion Matrix
2:00:53 Probability
2:03:19 Probability Terminologies
2:04:55 Types of Events
2:05:35 Probability of Distribution
2:10:45 Types of Probability
2:11:10 Marginal Probability
2:11:40 Joint Probability
2:12:35 Conditional Probability
2:13:30 Use-Case
2:17:25 Bayes Theorem
2:23:40 Inferential Statistics
2:24:00 Point Estimation
2:26:50 Interval Estimate
2:30:10 Margin of Error
2:34:20 Hypothesis Testing
2:41:25 Supervised Learning Algorithms
2:42:40 Regression
2:44:05 Linear vs Logistic Regression
2:49:55 Understanding Linear Regression Algorithm
3:11:10 Logistic Regression Curve
3:18:34 Titanic Data Analysis
3:58:39 Decision Tree
3:58:59 what is Classification?
4:01:24 Types of Classification
4:08:35 Decision Tree
4:14:20 Decision Tree Terminologies
4:18:05 Entropy
4:44:05 Credit Risk Detection Use-case
4:51:45 Random Forest
5:00:40 Random Forest Use-Cases
5:04:29 Random Forest Algorithm
5:16:44 KNN Algorithm
5:20:09 KNN Algorithm Working
5:27:24 KNN Demo
5:35:05 Naive Bayes
5:40:55 Naive Bayes Working
5:44:25Industrial Use of Naive Bayes
5:50:25 Types of Naive Bayes
5:51:25 Steps involved in Naive Bayes
5:52:05 PIMA Diabetic Test Use Case
6:04:55 Support Vector Machine
6:10:20 Non-Linear SVM
6:12:05 SVM Use-case
6:13:30 k Means Clustering & Association Rule Mining
6:16:33 Types of Clustering
6:17:34 K-Means Clustering
6:17:59 K-Means Working
6:21:54 Pros & Cons of K-Means Clustering
6:23:44 K-Means Demo
6:28:44 Hierarchical Clustering
6:31:14 Association Rule Mining
6:34:04 Apriori Algorithm
6:39:19 Apriori Algorithm Demo
6:43:29 Reinforcement Learning
6:46:39 Reinforcement Learning: Counter-Strike Example
6:53:59 Markov's Decision Process
6:58:04 Q-Learning
7:02:39 The Bellman Equation
7:12:14 Transitioning to Q-Learning
7:17:29 Implementing Q-Learning
7:23:33 Machine Learning Projects
7:38:53 Who is a ML Engineer?
7:39:28 ML Engineer Job Trends
7:40:43 ML Engineer Salary Trends
7:42:33 ML Engineer Skills
7:44:08 ML Engineer Job Description
7:45:53 ML Engineer Resume
7:54:48 Machine Learning Interview Questions

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For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free).

LIVE: Design and build a Chatbot from Scratch



Notes: drive.google.com/file/d/1ZNxivs8zyiMHLPjKECbcPFwOsLDUceSS/view?usp=sharing
Announcement: youtu.be/XIrMHf7opwQ

Design Patterns for Machine Learning in Production - Sergei Izrailev, Beeswax



This presentation was recorded at #H2OWorld 2017 in Mountain View, CA.

Enjoy the slides: slideshare.net/0xdata/design-patterns-for-machine-learning-in-production.

Learn more about H2O.ai: h2o.ai/.

Follow @h2oai: twitter.com/h2oai.

- - -

Dr. Sergei Izrailev is Chief Data Scientist at Beeswax, where he is responsible for data strategy and building AI applications powering the next generation of real-time bidding technology. Before Beeswax, Sergei led data science teams at Integral Ad Science and Collective, where he focused on architecture, development and scaling of data science based advertising technology products. Prior to advertising, Sergei was a quant/trader and developed trading strategies and portfolio optimization methodologies. Previously, he worked as a senior scientist at Johnson & Johnson, where he developed intelligent tools for structure-based drug discovery. Sergei holds a Ph.D. in Physics and Master of Computer Science degrees from the University of Illinois at Urbana-Champaign.

Designing Recommender Systems with Reachability in Mind @ ICML2020-Participatory Approaches to ML



Recommendations systems are a ubiquitous part of everyday life helping us navigate the immense amounts of information available online. But following recommendations can lead to unintended consequences such as polarization and radicalization.

We propose a way of evaluating recommendations system by looking at the degree of agency they grant to individuals using the system. People have agency within a system if they can modify how the algorithm perceives their preferences. In our research we propose a way of quantifying the degree of agency in recommenders and argue that it is a promising new directions for evaluating algorithms that interact with humans.

For more detail about this research check out our short paper: people.eecs.berkeley.edu/~sarahdean/stochastic_reachability.pdf

To see more recent work in Participatory Approaches to Machine Learning check out the workshop at ICML 2020: participatoryml.github.io/

To see more of our other work see our research websites:
Sarah Dean: people.eecs.berkeley.edu/~sarahdean/
Mihaela Curmei: mcurmei627.github.io/research/

How Gmail Uses Iterative Design, Machine Learning and AI to Create More Assistive Features



Paul Lambert and Annika Crowley- Google

Machine learning is arguably the most important trend in technology today. But designing great user experiences for ML-powered features presents unique challenges to a product designer: How do you create a mental model for a system with thousands of parameters? How do you explain recommendations from a system that has no rules? How do you provide user control? How do you respect user privacy expectations and tackle issues like ML fairness?

Learn about the challenges the team encountered and techniques they developed in building machine learning features in Gmail and Inbox, as well as creating the Smart Compose, Smart Reply, Nudging, Priority Inbox, Highlights and other ML-powered productivity features shipped to over a billion users.

How to Learn System Design as Beginner for Interviews | Complete RoadMap | SDE1 to SDE2



I have talked about how to learn System Design or High-Level Design(HLD) as a beginner. I have discussed what are the best resources for learning System Design like how to use educative.io courses. This will help you to transition from an SDE-1 to an SDE-2 role at any company.

I have covered all the concepts that are mandatory to learn system design and the standard problems you need to know before going to the interview.

Important Concepts
1. Scalability
2. Performance
3. Latency and Throughput
4. Consistency
5. Availability
6. Partition Tolerance
7. CAP Theorem
8. Domain Name System
9. Content Delivery Network
10. Load Balancers and Reverse Proxy
11. Microservices
12. Databases
13. Caching
14. Message Queues

System Design Primer - github.com/donnemartin/system-design-primer

Standard Questions
1. Pastebin
2. TinyURL
3. Uber
4. BookMyShow
5. Twitter Feed
6. Facebook Messenger
7. Search in Instagram
8. Video Streaming Service

Educative.io course - educative.io/courses/grokking-the-system-design-interview

System Design template - leetcode.com/discuss/career/229177/My-System-Design-Template

Netflix Blog - netflixtechblog.com/

Uber Engg Blog - eng.uber.com/

Google Developers - developers.googleblog.com/

High Scalability - highscalability.com/

Subscribe to my channel for more such videos :D

Join my Telegram link for interview preparation material and updates:
t.me/thecodeskool

You can also reach me at:

Instagram:
instagram.com/thecodeskool/

Twitter:
twitter.com/theCodeSkool

LinkedIn:
linkedin.com/in/deevankshu-garg-602501111/

Guidance, Navigation and Control System Design - Matlab / Simulink / FlightGear Tutorial



Model: github.com/Vinayak-D/GNCAirstrike

In this video you will learn how to build a complete guidance, navigation and control (GNC) system for a rocket / missile which is commanded to reach a specified target starting from a random initial position, by using LQR /LQG and Kalman filtering methods for control and estimation. You will learn

1) How to calculate azimuth, latitude, and longitudes
2) Calculate guidance commands, range, miss distance, elevation
3) Design Linear Quadratic Regulator / Gaussian (LQR) for a 2d state space model
4) Build a 3-DOF Simulation with the Aerospace Blockset provided within Simulink
5) Perform simulation with FlightGear

TIMESTAMPS:

Theory: 00:50
Matlab Code: 06:00
Simulink Model (Control): 10:05
Simulink Model (Guidance, Navigation): 17:13
Guidance Command Calculation: 19:30
Simulation: 22:02
Conclusion: 25:10

RESOURCES:

Run Simulink in real time (Pacer): mathworks.com/matlabcentral/fileexchange/29107-real-time-pacer-for-simulink
Raytheon Paper: researchgate.net/profile/Curtis_Mracek2/publication/303256153_Missile_Longitudinal_Autopilots_Connections_Between_Optimal_Control_and_Classical_Topologies/links/573a1c2308ae9ace840dc5cc.pdf
MATLAB & Simulink Tutorials Playlist: youtube.com/playlist?list=PLlIRr36VdiM_Ja6tr91mk_ZQF6ATvVRXP]

Thanks for watching!

My Instagram: instagram.com/vinayak_desh/
My Website: vinayakd.com/

Recommendation Systems using Machine Learning



The most common types of recommendation systems are content based and collaborative filtering recommender systems.
In collaborative filtering the behavior of a group of users is used to make recommendations to other users. Recommendation is based on the preference of other users.

Recommendation implementation video url: youtube.com/watch?v=R64Lh1Qwl_0

Below are the various playlist created on ML,Data Science and Deep Learning. Please subscribe and support the channel. Happy Learning!

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Designing Machine Learning Systems Pdf, Designing Machine Learning Systems | book summary | Read a book with me, 34.65 MB, 25:14, 5,241, Sophia Yang, 2022-06-12T03:44:18.000000Z, 19, Download Operating System Books free PDF - AllAbout-Engineering.com, www.allabout-engineering.com, 1360 x 1870, jpeg, Designing machine learning systems. O’reilly members get unlimited access to live online training experiences, plus books, videos, and digital content from o’reilly and nearly. Equation 18. 2 introduced yet another hyperparameter, γ, which represents how much of the previous correction should influence the current correction. its value can be of the order of 0. 9. Effectively, the concept of momentum aids in reaching the solution faster because:, 20, designing-machine-learning-systems-pdf, Design Ideas

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