Machine learning books and papers

⚡️ Stable Diffusion 3.5 Large. # install Diffusers pip install -U diffusers # Inference import torch from diffusers import StableDiffusion3Pipeline pipe = StableDiffusion3Pipeline.from_pretrained("stabilityai/stable-diffusion-3.5-large", torch_dtype=torch.bfloat16) pipe = pipe.to("cuda") image = pipe( "A happy woman laying on a grass", num_inference_steps=28, guidance_scale=3.5, ).images[0] image.save("woman.png") 🟡Arxiv @Machine_learn

با عرض سلام امروز اخرين وقت براي مشاركت در اين مقاله مي باشد...!

Title: Advanced Classification of Drug-Drug Interactions for Assessing Adverse Effect Risks of Fluvoxamine and Curcumin Using Deep Learning in COVID-19 ——————————————————————— Keywords: Drug–Drug Interactions; Deep Neural Network; Fluvoxamine; Curcumin; Machine Learning. ——————————————————————— Journal of Infrastructure, Policy and Development نفر اول پرشده نفر دوم و سوم و چهارم خالی هست. مقاله در اخرین ریوایزد خود می باشد. @Raminmousa @Machine_learn https://t.me/+SP9l58Ta_zZmYmY0

THINKING LLMS: GENERAL INSTRUCTION FOLLOWING WITH THOUGHT GENERATION 📚 Reed @Machine_learn

📕 Applied Causal #Inference Powered by #MachineLearning 📌Book @Machine_learn

با عرض سلام نيازمند co-author براي مقاله زیر هستيم. Target Journal: International Journal of Media and Networks | Opast Publishing Group (opastpublishers.com) if: 1.2 Paper link: A Survey of Generative Adversarial Network on Next Generation Network[v1] | Preprints.org تغييرات كامل نسخه نهايي تا يك هفته اينده اعمال ميشه كسي از دوستان تمايل به همكاري داشت به ايدي بنده پيام بدن. @Raminmousa @Machine_learn https://t.me/+SP9l58Ta_zZmYmY0

با عرض سلام نيازمند co-author براي مقاله زیر هستيم. Target Journal: International Journal of Media and Networks | Opast Publishing Group (opastpublishers.com) if: 1.2 Paper link: A Survey of Generative Adversarial Network on Next Generation Network[v1] | Preprints.org تغييرات كامل نسخه نهايي تا يك هفته اينده اعمال ميشه كسي از دوستان تمايل به همكاري داشت به ايدي بنده پيام بدن. @Raminmousa @Machine_learn https://t.me/+SP9l58Ta_zZmYmY0

🌟 Zamba2-Instruct В семействе 2 модели: 🟢Zamba2-1.2B-instruct; 🟠Zamba2-2.7B-instruct. # Clone repo git clone https://github.com/Zyphra/transformers_zamba2.git cd transformers_zamba2 # Install the repository & accelerate: pip install -e . pip install accelerate # Inference: from transformers import AutoTokenizer, AutoModelForCausalLM import torch tokenizer = AutoTokenizer.from_pretrained("Zyphra/Zamba2-2.7B-instruct") model = AutoModelForCausalLM.from_pretrained("Zyphra/Zamba2-2.7B-instruct", device_map="cuda", torch_dtype=torch.bfloat16) user_turn_1 = "user_prompt1." assistant_turn_1 = "assistant_prompt." user_turn_2 = "user_prompt2." sample = [{'role': 'user', 'content': user_turn_1}, {'role': 'assistant', 'content': assistant_turn_1}, {'role': 'user', 'content': user_turn_2}] chat_sample = tokenizer.apply_chat_template(sample, tokenize=False) input_ids = tokenizer(chat_sample, return_tensors='pt', add_special_tokens=False).to("cuda") outputs = model.generate(**input_ids, max_new_tokens=150, return_dict_in_generate=False, output_scores=False, use_cache=True, num_beams=1, do_sample=False) print((tokenizer.decode(outputs[0]))) 🖥GitHub https://t.me/deep_learning_proj

An Infinite Descent into Pure Mathematics 📚 Book @Machine_learn

Tutorial on Diffusion Models for Imaging and Vision 📚 Book @Machine_learn

NotebookLlama: An Open Source version of NotebookLM 📚 Book @Machine_learn

The State of AI Report 📚 Report @Machine_learn

فقط نفر ٤ كم داريم

با عرض سلام در حال نوشتن مقاله اي تحت عنوان title:A Comparative Survey on Large Language Models for Biological Data and Knowledge Graph systems هستيم كه ژورنال هدف Nature ميباشد. ٢ نفر از دوستان به دليل مشغله كاري نتونستن همكاري كنن. نفر ٤ و نفر ٦ از اين ليست رو تصمیم به جايگذيني كرديم. دوستاني كه توانايي كار دارن لطفا به بنده پيام بدن. تسك ها كامل مشخص شده و هزينه هر شخص هم تعيين شده. @Raminmousa @Machine_learn https://t.me/+SP9l58Ta_zZmYmY0

📑 A guide to RNA sequencing and functional analysis 📎 Study the paper @Machine_learn

💡 SAM2Long, a training-free enhancement to SAM 2 for long-term video segmentation 🟡Technical Report: https://huggingface.co/papers/2410.16268 🟡Github: https://github.com/Mark12Ding/SAM2Long 🟡Homepage: https://mark12ding.github.io/project/SAM2Long/ @Machine_learn

فقط نفر ۲ و ۴ از این باقی مونده ....!

private link: https://t.me/+SP9l58Ta_zZmYmY0

Title: BERTCaps: BERT Capsule for persian Multi-domain Sentiment Analysis. Abstract: Sentiment classification is widely known as a domain-dependent problem. In order to learn an accurate domain-specific sentiment classifier, a large number of labeled samples are needed, which are expensive and time-consuming to annotate. Multi-domain sentiment analysis based on multi-task learning can leverage labeled samples in each single domain, which can alleviate the need for large amount of labeled data in all domains. In this article, the purpose is BERTCaps to provide a multi-domain classifier. In this model, BERT was used for Instance Representation and Capsule was used for instance learning. In the evaluation dataset, the model was able to achieve an accuracy of 0.9712 in polarity classification and an accuracy of 0.8509 in domain classification. journal: https://www.sciencedirect.com/journal/array If: 2.3 نفرات ٢ تا ٤ اين مقاله رو نياز داريم. دوستاني كه مايل به شركت هستن مي تونن به ايدي بنده پيام بدن. @Raminmousa @Paper4money @Machine_learn

LLM Engineer's Handbook: Master the art of engineering Large Language Models from concept to production. 🖥 Github @Machine_learn