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Women don't need special treatment and are immensely talented: Vijayashree Natarajan …

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Her life, presently, she says, is all about AI, automation, and ML (machine learning), and finds it an absolute delight to be able to work on …


GitHub - labmlai/annotated_deep_learning_paper_implementations: 🧑 🏫 59 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

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This is a collection of simple PyTorch implementations of neural networks and related algorithms. We believe these would help you understand these algorithms better. We are actively maintaining this repo and adding new implementations almost weekly. If you use this for academic research, please cite it using the following BibTeX entry. This shows the most popular research papers on social media.


Speeding-Up Thermal Simulations Of Chips With ML

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A new technical paper titled "A Thermal Machine Learning Solver For Chip Simulation" was published by researchers at Ansys. Abstract "Thermal analysis provides deeper insights into electronic chips' behavior under different temperature scenarios and enables faster design exploration. However, obtaining detailed and accurate thermal profile on chip is very time-consuming using FEM or CFD. Therefore, there is an urgent need for speeding up the on-chip thermal solution to address various system scenarios. In this paper, we propose a thermal machine-learning (ML) solver to speed-up thermal simulations of chips. The thermal ML-Solver is an extension of the recent novel approach, CoAEMLSim (Composable Autoencoder Machine Learning Simulator) with modifications to the solution algorithm to handle constant and distributed HTC. The proposed method is validated against commercial solvers, such as Ansys MAPDL, as well as a latest ML baseline, UNet, under different scenarios to demonstrate its enhanced accuracy, scalability, and generalizability."


gist: AI Web Page Summary

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Continuing my exploration of AI tools, I created the gist, a Safari Web Extension based on OpenAI GPT3. It allows users to quickly get a summary of the active web page. My goal for it was not to…


Enrich Your Web Application With a Free A.I. Voice Recognition

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But it is not user-friendly. At this point, we need to trigger the recording from the UI, and not from the console. Similarly, we need to process the speech predictions and display them on the page or use them somehow. To finalize our concise demo, we'll simply add a button to allow the user to start the recording and we'll display the predictions on the page, as a list.


Using AI for Early Detection of Illnesses such as Malaria

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Detecting malaria can be stressful for most doctors. It requires expertise and relevant experiences to both reduce and eventually eliminate false positive and false negative diagnoses. Add to that antimalarial drugs are becoming less effective as the parasite has become increasingly drug-resistant and the traditional disease discovery methods are both time-consuming and resource intensive. Artificial Intelligence (AI) can help relieve this problem. Leveraging existing data, AI could be used to learn patterns within the data to detect malaria in patients with a higher degree of accuracy. This paper attempts to find a suitable AI-based model to accurately detect malaria in patients.


Ex Machina: Ava The Final Girl

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After I watched Men, I went to see what others had to say about it, and the first place I went to was a recorded conversion about the film on Diregentleman's channel. Toward the end of the conversation, Henry Galley says Men further diminished Garland's previous two films. Personally, I didn't get that in regards to Annihilation, but Ex Machina, on the other hand, I hadn't seen before. I did not watch Garland's directorial debut in 2014. And my reason is that I have been obsessed with pop culture about robotic A.I. ever since I was a kid from Astro Boy (circa.


Machine Learning for Everybody

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Machine learning technology is now so common that you probably use it dozens of times a day without even realizing it. And since it has so many applications, the job prospects are great for anyone with a lot of machine learning experience. We just released a machine learning course on the freeCodeCamp.org YouTube channel that is the perfect place to start your learning journey. Kylie Ying developed this course.


I tested Australian and British Google Cloud Voices - Cyberwarzone

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I have tested out the Neural2 and Wavenet Google Cloud Voices. The Google API allows me to directly access the Text-To-Speech API from Google. The Google API which I used needs an active Google Cloud account. These voices do cost money, but with some luck, you can get a free trial from Google. This API supports dozens of voices and I have selected a few from the list and used them to generate an audio which I supported with text and footage.


Will Artificial Intelligence And Machine Learning Have An Impact On App Development?

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Artificial Intelligence (AI) has gained widespread popularity in recent years, with its application flooding every business sector. AI has gained astonishingly great acceptance in the world of portable technology, by making various functions available at your fingertips. The fast speed of AI development, and accomplishments in automation, automated vehicles, the capacity to beat people at mind games, and computerized user support mean that AI is a progressive technology that will receive extraordinary rewards over the long haul. The ongoing AI environment comprises robotics, machine learning (ML), and artificial neural networks (ANNs) that are improving the semantic, numerical, and legitimate thinking skills of any framework using AI. The two main variables driving the quick reception of AI are top caliber, versatile learning models, and the need to process a lot of information at reasonable costs.