Machine learning books and papers

📽 Introduction to Network Analysis using NetworkX 🎞 Watch @Machine_learn

📃A Survey of Graph Neural Networks for Social Recommender Systems 📎 Study paper @Machine_learn

هزینه نهایی برای این کار رو به ۲۵ میلیون کاهش دادیم برای نفر ۵ ...!🔥

با عرض سلام پروژه Biopars رو شروع كرديم نفر ٥ ام از اين مقاله رو نياز داريم. این کار تحت نظر استاد Rex (Zhitao) Ying انجام میشه. link: https://scholar.google.com.au/citations?user=6fqNXooAAAAJ&hl=en BioPars: a pre-trained biomedical large language model for persian biomedical text mining. ١- مراحل اوليه: جمع اوري متن هاي فارسي بيولوژيكي از منابع (...) ٢- پيش پردازش متن ها و تميز كردن متن ها ٣- اموزش ترنسفورمرها ي مورد نظر ٤- استفاده از بردارها ي اموزش داده شده در سه تسك (...) هزينه سرور به ازاي هر ساعت ١.٢ دلار مي باشد. و حدود ٢ هزار ساعت براي اموزش مدل زباني نياز ميباشد. دوستاني كه نياز دارن مي تونن به تيم ما اضافه بشن 🔸🔸🔸🔸🔸 @Raminmousa

⚡️ NeuZip ▶️ # Install from PyPI pip install neuzip # Use Neuzip for Pytorch model model: torch.nn.Module = # your model + manager = neuzip.Manager() + model = manager.convert(model) 🟡Arxiv 🖥GitHub @Machine_learn

امشب اخرین فرصت برای مشارکت در این مقاله هستش...!🔸🔸

🌟 🌟 OuteTTS-0.2-500M # Install from PyPI pip install outetts # Interface Usage import outetts # Configure the model model_config = outetts.HFModelConfig_v1( model_path="OuteAI/OuteTTS-0.2-500M", language="en", # Supported languages in v0.2: en, zh, ja, ko ) # Initialize the interface interface = outetts.InterfaceHF(model_version="0.2", cfg=model_config) # Optional: Create a speaker profile (use a 10-15 second audio clip) speaker = interface.create_speaker( audio_path="path/to/audio/file", transcript="Transcription of the audio file." ) # Optional: Load speaker from default presets interface.print_default_speakers() speaker = interface.load_default_speaker(name="male_1") output = interface.generate( text="%Prompt Text%%.", temperature=0.1, repetition_penalty=1.1, max_length=4096, # Optional: Use a speaker profile speaker=speaker, ) # Save the synthesized speech to a file output.save("output.wav") 🟡Demo 🖥GitHub @Machine_learn

🔥 nn-zero-to-hero 🖥 Github @Machine_learn

امشب اخرین فرصت برای مشارکت در این مقاله هستش...!🔸🔸

با عرض سلام پروژه Biopars رو شروع كرديم نفر ٥ ام از اين مقاله رو نياز داريم. این کار تحت نظر استاد Rex (Zhitao) Ying انجام میشه. link: https://scholar.google.com.au/citations?user=6fqNXooAAAAJ&hl=en BioPars: a pre-trained biomedical large language model for persian biomedical text mining. ١- مراحل اوليه: جمع اوري متن هاي فارسي بيولوژيكي از منابع (...) ٢- پيش پردازش متن ها و تميز كردن متن ها ٣- اموزش ترنسفورمرها ي مورد نظر ٤- استفاده از بردارها ي اموزش داده شده در سه تسك (...) هزينه سرور به ازاي هر ساعت ١.٢ دلار مي باشد. و حدود ٢ هزار ساعت براي اموزش مدل زباني نياز ميباشد. دوستاني كه نياز دارن مي تونن به تيم ما اضافه بشن 🔸🔸🔸🔸🔸 @Raminmousa

Lecture notes: mathematics for artificial intelligence 📕 Link @Machine_learn

📄 RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis 📎 Study the paper @Machine_learn

با عرض سلام خيلي از دوستان در رابطه با طراحي صفر تا صد پروژه هاي ديپ از بنده سوال پرسيدن داخل پك زير ٣٦ پروژه رو با جزئيات شرح دادم: 1-Deep Learning Basic -01_Introduction --01_How_TensorFlow_Works 2-Classification apparel -Classification apparel double capsule -Classification apparel double cnn 3-ALZHEIMERS USING CNN(ResNet) 4-Fake News (Covid-19 dataset) -Multi-channel -3DCNN model -Base line+ Char CNN -Fake News Covid CapsuleNet 5-3DCNN Fake News 6-recommender systems -GRU+LSTM MovieLens 7-Multi-Domain Sentiment Analysis -Dranziera CapsuleNet -Dranziera CNN Multi-channel -Dranziera LSTM 8-Persian Multi-Domain SA -Bi-GRU Capsule Net -Multi-CNN 9-Recommendation system -Factorization Recommender, Ranking Factorization Recommender, Item Similarity Recommender (turicreate) -SVD, SVD++, NMF, Slope One, k-NN, Centered k-NN, k-NN Baseline, Co-Clustering(surprise) 10-NihX-Ray -optimized CNN on FullDataset Nih-Xray -MobileNet -Transfer learning -Capsule Network on FullDataset Nih-Xray دوستاني كه نياز به اين پروژه ها دارن ميتونن با بنده در ارتباط باشن. @Raminmousa @Machine_learn

با عرض سلام تيم تحقيقي ما در رابطه با Survey on whole slide image (WSI) نياز به نفرات ٤ و ٥ داره. استارت اين پروژه از ٢٠ دي ماه شروع ميشه . target journal: https://www.nature.com/srep/ دوستاني كه مايل به همكاري هستن ميتونن به ايدي بنده پيام بدن. @Raminmousa @Machine_learn https://t.me/+SP9l58Ta_zZmYmY0

Arcade Academy - Learn Python 📖 Book @Machine_learn

CogAgent: A Visual Language Model for GUI Agents Paper: https://arxiv.org/pdf/2312.08914v3.pdf CVPR 2024: http://openaccess.thecvf.com//content/CVPR2024/papers/Hong_CogAgent_A_Visual_Language_Model_for_GUI_Agents_CVPR_2024_paper.pdf Code1: https://github.com/thudm/cogvlm Code2: https://github.com/digirl-agent/digirl Code3: https://github.com/THUDM/CogAgent Dataset: TextVQA 💠@Machine_learn

KAG: Boosting LLMs in Professional Domains via Knowledge Augmented Generation Paper: https://arxiv.org/pdf/2409.13731v3.pdf Code: https://github.com/openspg/kag Dataset: 2WikiMultiHopQA 🔸@Machine_learn

Python for Everybody Exploring Data Using Python 3 📓 book @Machine_learn

Large Language Models Course: Learn by Doing LLM Projects 🖥 Github: https://github.com/peremartra/Large-Language-Model-Notebooks-Course 📕 Paper: https://doi.org/10.31219/osf.io/qgxea @Machine_learn

🔗 Machine LearningNLP-RoBERTa for Sentiment Analysis with Python Code + Data RoBERTa is a powerful transformer-based language model that has shown exceptional performance in various natural language processing (NLP) tasks, including sentiment analysis. It builds upon the foundation of BERT (Bidirectional Encoder Representations from Transformers) and incorporates several key improvements: 📗 Key Improvements in RoBERTa: 1. Larger Training Dataset: RoBERTa is trained on a significantly larger dataset than BERT, exposing it to a wider range of linguistic patterns and nuances. This extensive training helps it capture more subtle and complex sentiment expressions. 2. Dynamic Masking: RoBERTa employs dynamic masking during training, where the masked tokens are randomly selected for each training epoch. This introduces more variability into the training process, forcing the model to learn more robust representations. 3. Longer Training Sequences: RoBERTa is trained on longer sequences, allowing it to capture longer-range dependencies within the text, which can be crucial for understanding sentiment in longer sentences or paragraphs. 4. Removal of Next Sentence Prediction: RoBERTa removes the next sentence prediction task, focusing solely on language modeling. This simplifies the training objective and allows the model to allocate more resources to improving language understanding. 🐍 How RoBERTa is Used for Sentiment Analysis: 1. Fine-tuning: A pre-trained RoBERTa model is fine-tuned on a specific sentiment analysis dataset. This involves adjusting the model's parameters to better suit the nuances of the target sentiment classification task. 2. Text Encoding: The input text is encoded into a sequence of numerical representations, which are then fed into the fine-tuned RoBERTa model. 3. Sentiment Prediction: The RoBERTa model processes the input sequence and generates a probability distribution over different sentiment classes (e.g., positive, negative, neutral). The class with the highest probability is typically selected as the predicted sentiment. 🔅 Benefits of Using RoBERTa for Sentiment Analysis: • High Accuracy: RoBERTa's strong language understanding capabilities and extensive training enable it to achieve high accuracy in sentiment analysis tasks, even in challenging scenarios. • Robustness: The dynamic masking and larger training dataset make RoBERTa more robust to variations in language style and sentiment expression. • Efficiency: RoBERTa can be efficiently fine-tuned on specific sentiment analysis datasets, allowing for rapid adaptation to new tasks. • Versatility: RoBERTa can be applied to a wide range of sentiment analysis tasks, including binary classification, multi-class classification, and fine-grained sentiment analysis. 🤖 In Summary RoBERTa is a state-of-the-art language model that has significantly advanced the field of sentiment analysis. Its ability to capture complex linguistic patterns and nuances, combined with its robustness and efficiency, makes it a valuable tool for a wide range of NLP applications.