Book: 📚Successful #AlgorithmicTrading
Authors: By Michael L. Halls-Moore
ISBN: Null
year: 2023
pages: 208
Tags: #Machine_learning# Trading
@Machine_learn

امشب اخرين تخفيف از اين پك هاي يادگيري مي باشد....!

@Raminmousa

SQ-Transformer: Inducing Systematicity in Transformers by Attending to Structurally Quantized Embeddings

🖥 Github: https://github.com/jiangyctarheel/sq-transformer

📕 Paper: https://arxiv.org/pdf/2402.06492v1.pdf

🔥Datasets: https://paperswithcode.com/dataset/wmt-2014

@Machine_learn

Book: 📚An introduction to graph theory
Authors: Darij Grinberg
ISBN: Null
year: 2023
pages: 442
Tags: #Graph
@Machine_learn

⚡️ V-JEPA: The next step toward Yann LeCun’s vision of advanced machine intelligence (AMI)

▪Github: https://github.com/facebookresearch/jepa

▪Paper: https://ai.meta.com/research/publications/revisiting-feature-prediction-for-learning-visual-representations-from-video/

▪Blog: https://ai.meta.com/blog/v-jepa-yann-lecun-ai-model-video-joint-embedding-predictive-architecture/

@Machine_learn

MIM-Refiner

🖥 Github: https://github.com/ml-jku/MIM-Refiner

📕 Paper: https://arxiv.org/pdf/2402.10093v1.pdf

🔥Datasets: https://paperswithcode.com/dataset/imagenet

✨ Tasks: https://paperswithcode.com/task/image-clustering

@Machine_learn

با عرض سلام دو پکیچ یادگیری ماشین و یادگیری عمیق را برای دوستانی که می خواهند تا فرداشب با تخفیف ۵۰٪ مجدد قرار دادیم.

1: introduction to machine learning
2: Regression (linear and non-linear)
3: Tensorflow introduction
4: Tensorflow computaion graph
5: Tensorflow optimizer and loss function
6: Tensorflow linear and non linear regression
7: logistic regression
8: Tensorflow regression
___________
9: introduction to traditional machine learning
*10: knn and desicion tree
*11: desicion tree and Naive bayes
*12: desicion tree, knn, Naive bayes implementation
*13: k-means
*14: Guassion Mixture Model(GMM)
*15: implementation K-means and GMM
_
16: introduction to Artificial Neural Network
17: Multi-level Neural Network
18: Introduction to Convolution Neural Network
19: Tensorflow Multi-level Neural Network
20:Tensorflow CNN
21:CNN image clasaification
22: Cnn text clasaification
23: Recurrent Neural Network(RNN)

جهت تهیه می تونین به ایدی بنده مراجعه کنین

@Raminmousa

TIA Toolbox

🖥 Github: https://github.com/tissueimageanalytics/tiatoolbox

📕 Paper: https://arxiv.org/pdf/2402.09990v1.pdf

✨ Tasks: https://paperswithcode.com/task/whole-slide-images

@Machine_learn

PDD: Positional Discourse Divergence

🖥 Github: https://github.com/williamlyh/pos_div_metric

📕 Paper: https://arxiv.org/pdf/2402.10175v1.pdf

🔥Dataset: https://paperswithcode.com/dataset/recipe1m-1

✨ Tasks: https://paperswithcode.com/task/coherence-evaluation

@Machine_learn

Visually Dehallucinative Instruction Generation: Know What You Don't Know

🖥 Github: https://github.com/ncsoft/idk

📕 Paper: https://arxiv.org/pdf/2402.09717v1.pdf

🔥Dataset: https://paperswithcode.com/dataset/visual-question-answering

✨ Tasks: https://paperswithcode.com/task/hallucination

@Machine_learn

با عرض سلام جايگاه دوم از اين مقاله باقي مونده دوستاني كه نياز دارن ني تونن با بنده در ارتباط باشند
@Raminmousa

Energy-Time-series-anomaly-detection

🖥 Github: https://github.com/HardikPrabhu/Energy-Time-series-anomaly-detection

📕 Paper: https://arxiv.org/pdf/2402.14384v1.pdf

✨ Tasks: https://paperswithcode.com/task/anomaly-detection

@Machine_learn

Book: 📚Introduction to Generative AI
Authors: Numa Dhamani and Maggie Engler
ISBN: Null
year: 2023
pages: 318
Tags: #AI
@Machine_learn

Graph Diffusion Policy Optimization

🖥 Github: https://github.com/sail-sg/gdpo

📕 Paper: https://arxiv.org/pdf/2402.16302v1.pdf

🔥Dataset: https://paperswithcode.com/dataset/zinc

✨ Tasks: https://paperswithcode.com/task/graph-generation

@Machine_learn

DCVSMNet: Double Cost Volume Stereo Matching Network

🖥 Github: https://github.com/m2219/dcvsmnet

📕 Paper: https://arxiv.org/pdf/2402.16473v1.pdf

🔥Dataset: https://paperswithcode.com/dataset/kitti

✨ Tasks: https://paperswithcode.com/task/stereo-matching-1

@Machine_learn

Book: 📚Fundamentals of Data Science Theory and Practice
Authors: Jugal K. Kalita Dhruba K. Bhattacharyya Swarup Roy
ISBN: 978-0-323-91778-0
year: 2023
pages: 336
Tags: #Data_science
@Machine_learn