Chris☯ (@chrisdrago94) 's Twitter Profile
Chris☯

@chrisdrago94

Data Science | IA | Machine Learning | Dragonball | Cowboy Bebop e cia

ID: 1090047209058586624

calendar_today29-01-2019 00:41:21

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Akshay 🚀 (@akshay_pachaar) 's Twitter Profile Photo

The architecture diagram presented below illustrates some of the key components & how they interact with each other! It will be followed by detailed descriptions & code for each component:

The architecture diagram presented below illustrates some of the key components & how they interact with each other!

It will be followed by detailed descriptions & code for each component:
Shubham Saboo (@saboo_shubham_) 's Twitter Profile Photo

Microsoft just released 18 FREE courses on Generative AI for beginners. The courses include prompt engineering concepts, building LLM apps in Python, RAG, AI agents and vector databases. Each course includes video tutorial and step-by-step Python code walkthrough.

Microsoft just released 18 FREE courses on Generative AI for beginners.

The courses include prompt engineering concepts, building LLM apps in Python, RAG, AI agents and vector databases.

Each course includes video tutorial and step-by-step Python code walkthrough.
Ihtesham Haider (@ihteshamit) 's Twitter Profile Photo

I tested GPT-4o and Claude Sonnet 3.5 with same mega prompts. The results will blow your mind 🤯 Claude Sonnet 3.5 Vs. GPT-4o (Video demos are included 👇)

I tested GPT-4o and Claude Sonnet 3.5 with same mega prompts.

The results will blow your mind 🤯

Claude Sonnet 3.5             Vs.            GPT-4o

(Video demos are included 👇)
Abhishek🌱 (@abhishekcur) 's Twitter Profile Photo

Machine learning full course by Andrew Ng (Stanford CS229) -To learn some of the basics of ML: 1. Linear Regression and Gradient Descent 2. Logistic Regression 3. Naive Bayes 4. SVMs 5. Kernels 6. Decision Trees 7. Introduction to Neural Networks 8. Debugging ML Models

Machine learning full course by Andrew Ng (Stanford CS229)
-To learn some of the basics of ML:
 1. Linear Regression and Gradient Descent
 2. Logistic Regression
 3. Naive Bayes
 4. SVMs
 5. Kernels
 6. Decision Trees
 7. Introduction to Neural Networks
 8. Debugging ML Models
Swapna Kumar Panda (@swapnakpanda) 's Twitter Profile Photo

Stanford’s FREE Machine Learning Courses: CS221 - Artificial Intelligence CS229 - Machine Learning CS230 - Deep Learning CS234 - Reinforcement Learning CS224U - NL Understanding CS224N - NLP with Deep Learning Course links are inside: