keras-tuner
d2l-en
keras-tuner | d2l-en | |
---|---|---|
4 | 6 | |
2,827 | 21,759 | |
0.6% | 1.6% | |
7.8 | 8.5 | |
about 1 month ago | 15 days ago | |
Python | Python | |
Apache License 2.0 | GNU General Public License v3.0 or later |
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keras-tuner
- Is there any premade evolutionary algorithm selecting optimal NN architectures in TensorFlow ?
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What has priority in the performance?
If you are using tensorflow, you can do all of that with the very elegant (in my opinion) https://keras.io/keras_tuner/
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Space Science with Python - Asteroids meet Deep Learning #10
today I'd like to show you how to optimize a Conv1D network using Keras-Tuner. It enables one to automatically test some pre-defined networks; or it applies Bayesian or Hyperband optimization to find the best model!
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How to know how many layers (LSTM and dense) should I create and how to know the right parameters? (beginner)
Tbh, just try and error. There is no right or wrong. You can use the Hyperparameter Tuner from keras to define some architectures with varying number of layers and units as well as some other hyperparameters.
d2l-en
- which book to chose for deep learning :lan Goodfellow or francois chollet
- d2l-en: Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 400 universities from 60 countries including Stanford, MIT, Harvard, and Cambridge.
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How to pre-train BERT on different objective tasks using HuggingFace
There might is bert library for pre-train bert model in huggingface, But I suggestion that you train bert model in native pytorch to understand detail, Limu's course is recommended for you
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The Transformer in Machine Translation
GitHub's article on Dive into Deep Learning
- D2l-En
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I created a way to learn machine learning through Jupyter
There are actually some online books and courses built on Jupyter Notebook ([Dive to Deep Learning Book](https://github.com/d2l-ai/d2l-en) for example). However yours is more detail and could really helps beginners.
What are some alternatives?
Ray - Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Pytorch-UNet - PyTorch implementation of the U-Net for image semantic segmentation with high quality images
deephyper - DeepHyper: Scalable Asynchronous Neural Architecture and Hyperparameter Search for Deep Neural Networks
DeepADoTS - Repository of the paper "A Systematic Evaluation of Deep Anomaly Detection Methods for Time Series".
vizier - Python-based research interface for blackbox and hyperparameter optimization, based on the internal Google Vizier Service.
TF-Watcher - Monitor your ML jobs on mobile devices📱, especially for Google Colab / Kaggle
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
99-ML-Learning-Projects - A list of 99 machine learning projects for anyone interested to learn from coding and building projects
imbalanced-regression - [ICML 2021, Long Talk] Delving into Deep Imbalanced Regression
petastorm - Petastorm library enables single machine or distributed training and evaluation of deep learning models from datasets in Apache Parquet format. It supports ML frameworks such as Tensorflow, Pytorch, and PySpark and can be used from pure Python code.
einops - Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)
learning-topology-synthetic-data - Tensorflow implementation of Learning Topology from Synthetic Data for Unsupervised Depth Completion (RAL 2021 & ICRA 2021)