Machine learning books and papers

Constrained Diffusion Implicit Models! We use diffusion models to solve noisy inverse problems like inpainting, sparse-recovery, and colorization. 10-50x faster than previous methods! Paper: arxiv.org/pdf/2411.00359 Demo: https://t.co/m6o9GLnnZF @Machine_learn

Smol TTS models are here! OuteTTS-0.1-350M - Zero shot voice cloning, built on LLaMa architecture, CC-BY license! 🔥 > Pure language modeling approach to TTS > Zero-shot voice cloning > LLaMa architecture w/ Audio tokens (WavTokenizer) > BONUS: Works on-device w/ llama.cpp ⚡ Three-step approach to TTS: > Audio tokenization using WavTokenizer (75 tok per second). > CTC forced alignment for word-to-audio token mapping. > Structured prompt creation w/ transcription, duration, audio tokens. https://huggingface.co/OuteAI/OuteTTS-0.1-350M @Machine_learn

📕 Machine Learning for Absolute Beginners ▪️Link @Machine_learn

Machine Learning with PyTorch and Scikit-Learn Book 📚 book @Machine_learn

AutoWebGLM: Bootstrap And Reinforce A Large Language Model-based Web Navigating Agent 🖥 Github: https://github.com/thudm/autowebglm 📕 Paper: https://arxiv.org/abs/2404.03648v1 🔥Dataset: https://paperswithcode.com/dataset/mind2web @Machine_learn

❤️ اکستنشن ChatGPT Search برای مرورگرهای کرومیوم منتشر شد از طریق این لینک میتونید این افزونه رو دانلود کنید @Machine_learn

Conformal prediction under ambiguous ground truth Paper: https://arxiv.org/pdf/2307.09302v2.pdf Codes: https://github.com/google-deepmind/uncertain_ground_truth https://github.com/alaalab/webcp Dataset: Dermatology ddx dataset @Machine_learn

فقط جایگاه دوم از این مقاله باقی مونده

الحمدالله تو اين بازه ٣ ماه تونستيم مقالات مشاركتي رو تحت وظايف زير انجام بديم: 🔹ثبت ٤ مقاله در حوزه Multi-modal wond classification 🔹ارائه ی دو مقاله در حوزه ی breast cancer segmentation 🔹 ارائه ی سه مقاله در حوزه ی cancer detection که ۸۰٪ مراحل این مقالات هم تموم شده. به زودی پس از اتمام این مقالات لیستی از مقالات مشارکتی رو خواهیم داشت . https://t.me/+SP9l58Ta_zZmYmY0

👩‍💻 Python Notes for Professionals book 🔗 Book @Machine_learn

📖 LLM-Agent-Paper-List is a repository of papers on the topic of agents based on large language models (LLM)! The papers are divided into categories such as LLM agent architectures, autonomous LLM agents, reinforcement learning (RL), natural language processing methods, multimodal approaches and tools for developing LLM agents, and more. 🖥 Github https://t.me/deep_learning_proj

💠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 جايگاه ٢ و ٤ اين مقاله رو نياز داريم. دوستاني كه مايل به شركت هستن مي تونن به ايدي بنده پيام بدن. @Raminmousa @Paper4money @Machine_learn

Data Pipelines with Apache Airflow 📘 book @Machine_learn

📑A Survey of Deep Learning Methods for Estimating the Accuracy of Protein Quaternary Structure Models 📎 Study the paper @Machine_learn

Ms - SmolLM2 1.7B - beats Qwen 2.5 1.5B & Llama 3.21B, Apache 2.0 licensed, trained on 11 Trillion tokens 🔥 > 135M, 360M, 1.7B parameter model > Trained on FineWeb-Edu, DCLM, The Stack, along w/ new mathematics and coding datasets > Specialises in Text rewriting, Summarization & Function Calling > Integrated with transformers & model on the hub! You can run the 1.7B in less than 2GB VRAM on a Q4 👑 Fine-tune, run inference, test, train, repeat - intelligence is just 5 lines of code away! https://huggingface.co/collections/HuggingFaceTB/smollm2-6723884218bcda64b34d7db9 @Machine_learn

💠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 جايگاه ٢ و ٤ اين مقاله رو نياز داريم. دوستاني كه مايل به شركت هستن مي تونن به ايدي بنده پيام بدن. @Raminmousa @Paper4money @Machine_learn

تخفيف ٥٠٪؜🔹 دو پكيچ كدنويسي پايه يادگيري ماشين و يادگيري عميق به همراه ٣٦ بروژه عملي با پشتيباني ٢ ماهه . جهت سفارش به ايدي بنده پيام بدين. 🔺 هزینه هر دو پک با تخفيف ۱۵۰۰ هزار ميباشد. @Raminmousa

SAM2Long: Enhancing SAM 2 for Long Video Segmentation with a Training-Free Memory Tree 🖥 Github: https://github.com/mark12ding/sam2long 📕 Paper: https://arxiv.org/abs/2410.16268v1 🤗 HF: https://huggingface.co/papers/2410.16268 @Machine_learn

Intermediate Python 📖 Book @Machine_learn

🌟 Aya Expanse 🟢Aya Expanse 32B 🟢Aya Expanse 8B 🟠Aya Expanse 32B-GGUF 🟠Aya Expanse 8B-GGUF Expanse 8B Transformers : from transformers import AutoTokenizer, AutoModelForCausalLM model_id = "CohereForAI/aya-expanse-8b" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id) # Format the message with the chat template messages = [{"role": "user", "content": " %prompt% "}] input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt") ## <BOS_TOKEN><|START_OF_TURN_TOKEN|><|USER_TOKEN|>%prompt%<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|> gen_tokens = model.generate( input_ids, max_new_tokens=100, do_sample=True, temperature=0.3, ) gen_text = tokenizer.decode(gen_tokens[0]) print(gen_text) 🟡GGUF 32B 🟡GGUF 8B 🟡Demo @Machine_learn