بهترین مقالات این هفته
GNoME
https://www.nature.com/articles/s41586-023-06735-9
Open-Source LLMs vs. ChatGPT
https://arxiv.org/abs/2311.16989
Adversarial Diffusion Distillation
https://stability.ai/research/adversarial-diffusion-distillation
Seamless
https://ai.meta.com/research/publications/seamless-multilingual-expressive-and-streaming-speech-translation/
MEDITRON-70B
https://arxiv.org/abs/2311.16079v1
Foundation Models Outcompeting Special-Purpose Tuning
https://arxiv.org/abs/2311.16452
UniIR
https://arxiv.org/abs/2311.17136
Safe Deployment of Generative AI
https://www.nature.com/articles/d41586-023-03803-y
On Bringing Robots Home
https://arxiv.org/abs/2311.16098v1
Translatotron 3
https://arxiv.org/abs/2305.17547
GNoME
https://www.nature.com/articles/s41586-023-06735-9
Open-Source LLMs vs. ChatGPT
https://arxiv.org/abs/2311.16989
Adversarial Diffusion Distillation
https://stability.ai/research/adversarial-diffusion-distillation
Seamless
https://ai.meta.com/research/publications/seamless-multilingual-expressive-and-streaming-speech-translation/
MEDITRON-70B
https://arxiv.org/abs/2311.16079v1
Foundation Models Outcompeting Special-Purpose Tuning
https://arxiv.org/abs/2311.16452
UniIR
https://arxiv.org/abs/2311.17136
Safe Deployment of Generative AI
https://www.nature.com/articles/d41586-023-03803-y
On Bringing Robots Home
https://arxiv.org/abs/2311.16098v1
Translatotron 3
https://arxiv.org/abs/2305.17547
Latent Consistency Models
اين يكي پروژه اي ديدم خيلي جالب بود واسم
مقاله و پروژه هم ميزارم براتون
https://latent-consistency-models.github.io/
https://github.com/luosiallen/latent-consistency-model
https://arxiv.org/abs/2310.04378
https://huggingface.co/spaces/SimianLuo/Latent_Consistency_Model
اين يكي پروژه اي ديدم خيلي جالب بود واسم
مقاله و پروژه هم ميزارم براتون
https://latent-consistency-models.github.io/
https://github.com/luosiallen/latent-consistency-model
https://arxiv.org/abs/2310.04378
https://huggingface.co/spaces/SimianLuo/Latent_Consistency_Model
❤1
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MagicAnimate: Human Animation
MagicAnimate: the new SOTA in human animation. Code available: let's dance!
Review
https://t.ly/Oq7Za
Paper
https://arxiv.org/pdf/2311.16498.pdf
Project
https://showlab.github.io/magicanimate/
Code
https://github.com/magic-research/magic-animate
Demo
https://huggingface.co/spaces/zcxu-eric/magicanimate
MagicAnimate: the new SOTA in human animation. Code available: let's dance!
Review
https://t.ly/Oq7Za
Paper
https://arxiv.org/pdf/2311.16498.pdf
Project
https://showlab.github.io/magicanimate/
Code
https://github.com/magic-research/magic-animate
Demo
https://huggingface.co/spaces/zcxu-eric/magicanimate
PyPose: A Library for Robot Learning with Physics-based Optimization
داكيومنت
https://pypose.org/docs/main/index.html
اموزش
https://pypose.org/tutorials/
گيتهاب
https://github.com/pypose/pypose/blob/main/CONTRIBUTING.md
داكيومنت
https://pypose.org/docs/main/index.html
اموزش
https://pypose.org/tutorials/
گيتهاب
https://github.com/pypose/pypose/blob/main/CONTRIBUTING.md
بهترین مقالات این هفته
Gemini
https://storage.googleapis.com/deepmind-media/gemini/gemini_1_report.pdf
EfficientSAM
https://arxiv.org/abs/2312.00863
Magicoder
https://arxiv.org/abs/2312.02120
LLMs on Graphs
https://arxiv.org/abs/2312.02783
Llama Guard
https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/
Human-Centered Loss Functions
https://github.com/ContextualAI/HALOs/blob/main/assets/report.pdf
Chain of Code
https://arxiv.org/abs/2312.04474
Data Management For LLMs
https://arxiv.org/abs/2312.01700
RankZephyr
https://arxiv.org/abs/2312.02724
The Efficiency Spectrum of LLMs
https://arxiv.org/abs/2312.00678
Gemini
https://storage.googleapis.com/deepmind-media/gemini/gemini_1_report.pdf
EfficientSAM
https://arxiv.org/abs/2312.00863
Magicoder
https://arxiv.org/abs/2312.02120
LLMs on Graphs
https://arxiv.org/abs/2312.02783
Llama Guard
https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/
Human-Centered Loss Functions
https://github.com/ContextualAI/HALOs/blob/main/assets/report.pdf
Chain of Code
https://arxiv.org/abs/2312.04474
Data Management For LLMs
https://arxiv.org/abs/2312.01700
RankZephyr
https://arxiv.org/abs/2312.02724
The Efficiency Spectrum of LLMs
https://arxiv.org/abs/2312.00678
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https://bbycroft.net/llm
اين يك وبسايت ازمايشي و درك فهم
بسيار جالبه برام حتما يه سر به اين سايت خفن بزنيد
اين يك وبسايت ازمايشي و درك فهم
بسيار جالبه برام حتما يه سر به اين سايت خفن بزنيد
👍1
AI and Jobs: Has the Inflection Point Arrived? Evidence from an Online Labor Platform
https://arxiv.org/abs/2312.04180
https://arxiv.org/abs/2312.04180
Can we combine PyTorch and Scikit-Learn to create the ultimate ML library? Yes, Skorch does just that.
It allows you to leverage PyTorch's deep learning and Sklearn's ease of use. No need for complex code. You can use a simple net.fit(X, y) for training.
https://github.com/skorch-dev/skorch/
It allows you to leverage PyTorch's deep learning and Sklearn's ease of use. No need for complex code. You can use a simple net.fit(X, y) for training.
https://github.com/skorch-dev/skorch/
Neural_Networks_Cheat_Sheet__1704040110.pdf
7.7 MB
Network Neural cheat sheet
Machine Learning
Photo
PyTorch vs. TensorFlow: the Battle of Machine Learning Frameworks
Deep learning frameworks are essential tools that simplify the development of artificial neural networks (ANNs), and their evolution has been rapid. Among these, TensorFlow and PyTorch stand out, each holding its own in various machine learning realms. But how does one decide the ideal tool for specific projects? This comprehensive guide aims to elucidate their strengths and weaknesses.
Origins and Overview:
TensorFlow: Born from the brains at Google's Brain team, TensorFlow transitioned from being a proprietary tool to an open-source marvel. As an end-to-end platform, it offers everything from basic arithmetic operations to neural network deployment. Its adaptability is evident in its compatibility with platforms such as CPUs, GPUs, TPUs, and mobile devices. Notably, industry giants like Google, Uber, and Microsoft have integrated TensorFlow into their operations.
PyTorch: Introduced in 2016, PyTorch struck a chord by marrying user-friendliness with high performance. Its Pythonic design approach and dynamic computation graphs have made it a top choice in the research community. Developed primarily in C++, its efficiency is notable, and its adoption in platforms like Tesla Autopilot and Uber’s Pyro further attests to its capabilities.
Popularity Trends in Context: PyTorch vs TensorFlow
The shifting dynamics in the popularity between PyTorch and TensorFlow over a period can be linked with significant events and milestones in the world of these frameworks:
TensorFlow’s Initial Popularity: In the early phase of our timeline, TensorFlow had a distinct edge in popularity. This can be credited to its strong backing by Google and its wide-ranging tools that catered to both beginners and professionals.
PyTorch’s Rise: Moving forward, PyTorch started gaining momentum. Its approach, which many found to be more flexible for research and experimentation, played a role in attracting attention. Additionally, as more resources and support became available for PyTorch, its user base grew.
Recent Landscape: Towards the end, both PyTorch and TensorFlow have settled into their roles in the machine learning world. TensorFlow remains a solid pick for those looking at large-scale deployments and industry solutions. PyTorch, with its emphasis on flexibility, remains higher popularity for many specially in the research domain.
Deep Dive: Static vs. Dynamic Computational Graphs
A foundational distinction between TensorFlow and PyTorch is their approach to computational graphs. TensorFlow employs static computational graphs, while PyTorch advocates for dynamic ones.
Making an Informed Choice
Choosing between TensorFlow and PyTorch isn’t about selecting the “best” framework but about finding the one that aligns best with your needs. Both frameworks offer unique advantages and have made significant strides in addressing their initial limitations. By evaluating your project’s requirements, your familiarity with Python, the need for scalability, deployment preferences, and the kind of community support you’re seeking, you can make a choice that ensures efficiency and productivity. As the world of deep learning continues to evolve, so will these frameworks, and staying updated will empower you to make informed decisions time and again.
Deep learning frameworks are essential tools that simplify the development of artificial neural networks (ANNs), and their evolution has been rapid. Among these, TensorFlow and PyTorch stand out, each holding its own in various machine learning realms. But how does one decide the ideal tool for specific projects? This comprehensive guide aims to elucidate their strengths and weaknesses.
Origins and Overview:
TensorFlow: Born from the brains at Google's Brain team, TensorFlow transitioned from being a proprietary tool to an open-source marvel. As an end-to-end platform, it offers everything from basic arithmetic operations to neural network deployment. Its adaptability is evident in its compatibility with platforms such as CPUs, GPUs, TPUs, and mobile devices. Notably, industry giants like Google, Uber, and Microsoft have integrated TensorFlow into their operations.
PyTorch: Introduced in 2016, PyTorch struck a chord by marrying user-friendliness with high performance. Its Pythonic design approach and dynamic computation graphs have made it a top choice in the research community. Developed primarily in C++, its efficiency is notable, and its adoption in platforms like Tesla Autopilot and Uber’s Pyro further attests to its capabilities.
Popularity Trends in Context: PyTorch vs TensorFlow
The shifting dynamics in the popularity between PyTorch and TensorFlow over a period can be linked with significant events and milestones in the world of these frameworks:
TensorFlow’s Initial Popularity: In the early phase of our timeline, TensorFlow had a distinct edge in popularity. This can be credited to its strong backing by Google and its wide-ranging tools that catered to both beginners and professionals.
PyTorch’s Rise: Moving forward, PyTorch started gaining momentum. Its approach, which many found to be more flexible for research and experimentation, played a role in attracting attention. Additionally, as more resources and support became available for PyTorch, its user base grew.
Recent Landscape: Towards the end, both PyTorch and TensorFlow have settled into their roles in the machine learning world. TensorFlow remains a solid pick for those looking at large-scale deployments and industry solutions. PyTorch, with its emphasis on flexibility, remains higher popularity for many specially in the research domain.
Deep Dive: Static vs. Dynamic Computational Graphs
A foundational distinction between TensorFlow and PyTorch is their approach to computational graphs. TensorFlow employs static computational graphs, while PyTorch advocates for dynamic ones.
Making an Informed Choice
Choosing between TensorFlow and PyTorch isn’t about selecting the “best” framework but about finding the one that aligns best with your needs. Both frameworks offer unique advantages and have made significant strides in addressing their initial limitations. By evaluating your project’s requirements, your familiarity with Python, the need for scalability, deployment preferences, and the kind of community support you’re seeking, you can make a choice that ensures efficiency and productivity. As the world of deep learning continues to evolve, so will these frameworks, and staying updated will empower you to make informed decisions time and again.