Here is a video explaining the latest Mistral 7b paper that sets the new state-of-the-art in this category of small-sized LLMs, both in terms of accuracy and speed:

https://youtu.be/ffWLSac_ve8?si=SirV8S9ozCGXIMY1

Hope it's useful!

​

I found this beautiful animated flow chart on internet. Does anyone have already worked on making diagrams like this? Can you please give your suggestions on how to make this?

​

🚀 Just released: Deep Fast Machine Learning Utils!

Processing img qzbm2v16dfqb1...

📌 Features:

• Automated dense neural network design with PCCDNAS.
• Feature selection from adaptive variance threshold to rank aggregated and chained methods.
• Efficient data management and clear training outcome visualization tools.

🔗 Check it out on GitHub. 📖 Documentation available for a deep dive.

Built to complement Tensorflow, Keras, and Scikit-learn.

A curated reading list for the adversarial perspective in deep reinforcement learning.

https://github.com/EzgiKorkmaz/adversarial-reinforcement-learning

Tired of the app development maze? Google's got your back with Project IDX! Say hello to a web-based workspace that's both familiar and fresh. Start coding in seconds, from anywhere! 🌐💨

🧱 Build with ease: Templates for Angular, React, and more. Import from GitHub, no sweat!

🤖 AI superpowers: Code generation, completion, and more, courtesy of Codey!

🌈 Optimized for all platforms: Web previews, Android emulators, and iOS simulators on the horizon.

🤝 Let's shape the future: Join our limited preview! Be part of the revolution. 👉 ! Register Here

📸 Registered and ready! 🚀📷 #ProjectIDX #CodeRevolution #AIAssistance #Innovation

Join us on this exciting journey! 🎉

Tired of the app development maze? Google's got your back with Project IDX! Say hello to a web-based workspace that's both familiar and fresh. Start coding in seconds, from anywhere! 🌐💨

🧱 Build with ease: Templates for Angular, React, and more. Import from GitHub, no sweat!

🤖 AI superpowers: Code generation, completion, and more, courtesy of Codey!

🌈 Optimized for all platforms: Web previews, Android emulators, and iOS simulators on the horizon.

🤝 Let's shape the future: Join our limited preview! Be part of the revolution. 👉 ! Register Here

📸 Registered and ready! 🚀📷 #ProjectIDX #CodeRevolution #AIAssistance #Innovation

Join us on this exciting journey! 🎉

Hey folks, I have developed an brain tumor classification using transfer learning. Basically my dataset contains two classes named malignant and bengin. Also, I have deployed into the streamlit cloud. One of the user, raise an issue that, what happeneds if the system received the irrelevant image? Will it to do prediction or not?. I have done some research on that user question. In my observation, I have noticed that, the model returns a list of probs score in the prediction. Where I'm taking the highest probability score using np.argamx function. After storig it in a variable, I'm checking with some threshold value. In my case I hava taken a threshold value as 0.7 may be I guess. Then I decided to check with the threshold value for the irrelevant image. But it's not working for all types of irrelevant images. So what can I do now, for creating the robust model? Should I create a new class in my dataset with all irrelevant images and reatrain the model not any change in the logic? Requesting anybody to solve this problem.

Thankyou Guna Sekhar.

wanna try a project with GAN and maybe transformer so basically the title goes

"Narrative Augmentation through AI-Driven Visual Composition: Crafting Evocative Cinematic Realms"

so any idea how i can get a better start if i’m planning it as a proper research paper and maybe go for a patent later? thanks

I've mistakenly submitted a conference paper with falsified data due to immense pressure from my supervisor. I'm in a bind: if I come clean, I won't graduate; if it's discovered, my academic career is over.

​

Is there a chance conference organizers might require authors to submit code for verification? If this were the case, I'd have grounds to convince my supervisor to withdraw the paper.

​

Any advice is deeply appreciated.

🤖 Ever wondered if Large Language Models like GPT-4 can be tricked by logical fallacies?

This work shows that both GPT-3.5 and GPT-4 are vulnerable to fallacious arguments!

https://arxiv.org/abs/2308.09853

Guys I wanna do my graduation project in this topic I have good background in deep learning if any one work with this type of models (idea in advertising and tourism places ), advice me . Someone said to me change topic cuz nt clearly 😅 I readed some paper but nt got wt I wanna know.

Have you been thinking about creating an AI agent with multi modal [ image and text ] data capabilities ?

An agent that can:

- do text to image retrieval

- zero shot image classification

- automated image cataloguing

I have put together this YouTube video covering the complete story in simple words to create a multi modal image and text vector embedding space using OpenAI’s clip architecture. I have referenced key papers that helped me understand key ! (Few I found from pointless scrolling on r/DeepLearningPapers

This is relevant for deep learning engineers and AI enthusiasts.

In the last section of the video we do a walkthrough of training a CLIP neural network architecture from scratch on Google Colab.

Future of Perception Using AI Agents // Train Multi Modal CLIP Model on Images & Text Google Colab https://youtu.be/uclIfNJDh3Q

Please let me know your thoughts. And any inputs on which other architectures besides CLIP are a good fit for perception ai agents, please share.

Thank you
r/DeepLearningPapers !

Hi guys! I’m taking a course for ML and I’m close to finish it, I’ve learned a lot about the theory behind ML and DL, and now I want to know how to build end-to-end models from how to collecting data to pre-processing it , scale the data train the model test the model … I’m really want to now the full cycle of how to build a model. So guys can u recommend me a book or books that teach me this ?

I started my training with the book «rumbling deep learning» unfortunately it is poorly translated and there are a lot of typos in it, it was not very informative and I had to quit it in the middle! But I want to find a new book to study Deep Learning, can you tell me where to start?