Machine Learning pinned «🤖 A Practical Tip for ML Data Collection When building a machine learning project, getting enough useful data is often just as important as the model itself. If you're collecting public web data for a dataset, you may need to access the same source from different…»
OpenAI researcher Alice Liu went through 57 interviews before being hired, and then openly shared her entire preparation and job search journey.
If you're preparing for Research Scientist or MTS positions, this is one of the most comprehensive resources available.
You can use her notes directly, or simply use the list of topics to study them yourself. This kind of information is rarely published.
Notes on LLMs:
https://alisawuffles.notion.site/alisa-s-book-of-llms
Mathematics:
https://alisawuffles.notion.site/math-notes
Analysis of the job search and interview process:
https://alisawuffles.github.io/blog/job-search/
https://xn--r1a.website/MachineLearning9🫀
If you're preparing for Research Scientist or MTS positions, this is one of the most comprehensive resources available.
You can use her notes directly, or simply use the list of topics to study them yourself. This kind of information is rarely published.
Notes on LLMs:
https://alisawuffles.notion.site/alisa-s-book-of-llms
Mathematics:
https://alisawuffles.notion.site/math-notes
Analysis of the job search and interview process:
https://alisawuffles.github.io/blog/job-search/
https://xn--r1a.website/MachineLearning9
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Uniface
Automate face detection, recognition, and analysis of key facial landmarks with the Uniface Python library.
https://github.com/yakhyo/uniface
https://xn--r1a.website/MachineLearning9
Automate face detection, recognition, and analysis of key facial landmarks with the Uniface Python library.
https://github.com/yakhyo/uniface
https://xn--r1a.website/MachineLearning9
❤4
Cheat sheet for Scapy: creating and configuring packets, working with IP addresses, sending and receiving packets, sniffing traffic, fuzzing, and viewing packet structure. 📦🔍
#Scapy #Networking #Python #CyberSecurity #PacketCrafting #Hacking
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#Scapy #Networking #Python #CyberSecurity #PacketCrafting #Hacking
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Forwarded from Machine Learning with Python
This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages
✅ https://xn--r1a.website/addlist/8_rRW2scgfRhOTc0
✅ https://xn--r1a.website/Codeprogrammer
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Forwarded from Machine Learning with Python
If you're just starting to learn machine learning and want to delve deeper into the mathematics required for machine learning and deep learning, I recommend trying this platform. It's something like LeetCode for machine learning.
This is not an advertisement: I personally used it and decided to share it with you.
https://deep-ml.com
https://xn--r1a.website/CodeProgrammer
This is not an advertisement: I personally used it and decided to share it with you.
https://deep-ml.com
https://xn--r1a.website/CodeProgrammer
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Directions for the development of hardware for deep learning – a lecture by Bill Dally at Georgia Tech, 2024.
youtu.be/gofI47kfD28
https://xn--r1a.website/MachineLearning9
youtu.be/gofI47kfD28
https://xn--r1a.website/MachineLearning9
"Trigonometry" is a free, open-source textbook on trigonometry, with over 1000 pages, covering the subject from basic concepts to advanced topics.
The book covers angles and triangles, trigonometric relationships, the unit circle, sine, cosine, and tangent functions, graphs and their transformations, radians, solving triangles, the sine and cosine theorems, trigonometric identities and equations, inverse trigonometric functions, and formulas for the sum, difference, and double angle.
Later chapters also cover vectors, the dot product, polar coordinates, and complex numbers in polar form.
Each section contains numerous exercises, making the textbook particularly useful for reinforcing theoretical knowledge through practical application as you progress through the material.
https://louis.pressbooks.pub/trigonometry/
The book covers angles and triangles, trigonometric relationships, the unit circle, sine, cosine, and tangent functions, graphs and their transformations, radians, solving triangles, the sine and cosine theorems, trigonometric identities and equations, inverse trigonometric functions, and formulas for the sum, difference, and double angle.
Later chapters also cover vectors, the dot product, polar coordinates, and complex numbers in polar form.
Each section contains numerous exercises, making the textbook particularly useful for reinforcing theoretical knowledge through practical application as you progress through the material.
https://louis.pressbooks.pub/trigonometry/
👍6
Forwarded from Machine Learning with Python
"How to Train a Neural Network" is a concise summary of the MIT course lectures on deep learning from 2024. It focuses on one of the fundamental questions in neural networks: how a model learns its weights.
The summary examines the training process from a mathematical perspective. It covers topics such as forward propagation, loss functions, gradients, backpropagation, and gradient-based optimization methods.
I believe this is an interesting resource for those who want to go beyond a general, intuitive understanding of neural networks and begin to delve into the mathematics that underlies their training.
https://ocw.mit.edu/courses/6-7960-deep-learning-fall-2024/mit6_7960_f24_lec2.pdf
https://xn--r1a.website/CodeProgrammer🤩
The summary examines the training process from a mathematical perspective. It covers topics such as forward propagation, loss functions, gradients, backpropagation, and gradient-based optimization methods.
I believe this is an interesting resource for those who want to go beyond a general, intuitive understanding of neural networks and begin to delve into the mathematics that underlies their training.
https://ocw.mit.edu/courses/6-7960-deep-learning-fall-2024/mit6_7960_f24_lec2.pdf
https://xn--r1a.website/CodeProgrammer
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Forwarded from Machine Learning with Python
This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages
✅ https://xn--r1a.website/addlist/8_rRW2scgfRhOTc0
✅ https://xn--r1a.website/Codeprogrammer
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"Learning Mathematics: Why Memory, Practice, and Technique are More Important than Talent"
To master mathematics, it is necessary to memorize a large amount of information, rules, methods of simplification, and problem-solving techniques. There is no other way. Theory is useful, but in moderation.
It's like learning a language. If you focus too much on grammar, you will never learn to speak it fluently.
https://algebrica.org/learning-mathematics/
To master mathematics, it is necessary to memorize a large amount of information, rules, methods of simplification, and problem-solving techniques. There is no other way. Theory is useful, but in moderation.
It's like learning a language. If you focus too much on grammar, you will never learn to speak it fluently.
https://algebrica.org/learning-mathematics/
Algebrica
Learning Mathematics | Algebrica
Why Memory, Practice, and Technique Matter More Than Talent
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I found a great resource for interactive learning about machine learning and AI – VizLearn.
You can experiment with gradient descent, SVM, PCA, the Bayesian method, BPE tokenization, Q/K/V, KV-cache, quantization, and much more. You can change the input data and see how the calculations themselves change.
It's free and doesn't require registration.
https://vizlearn.in
https://xn--r1a.website/MachineLearning9
You can experiment with gradient descent, SVM, PCA, the Bayesian method, BPE tokenization, Q/K/V, KV-cache, quantization, and much more. You can change the input data and see how the calculations themselves change.
It's free and doesn't require registration.
https://vizlearn.in
https://xn--r1a.website/MachineLearning9
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