from tensorflow.keras.optimizers import Adam # 编译模型 model.compile(optimizer=Adam(learning_rate=0.001), loss='sparse_categorical_crossentropy', metrics=['accuracy']) # 准备数据 X_train = np.array([preprocess_audio(sample['file'].numpy().decode()) for sample in tfds.as_numpy(ds)]) y_...
莫烦Python 教程:https://mofanpy.com/(包括基础、数据处理、机器学习等,部分内容有视频) Django 官方教程:https://docs.djangoproject.com/zh-hans/3.2/intro/tutorial01/ Manim 动画引擎教程:docs.manim.org.cn/shade A Byte of Python:https://python.swaroopch.com/(Python 英文入门教程) ⭐ 谷歌 Python...
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This session is about getting started with the IPython notebook and a quick introduction to numpy for matrix operations and matplotlib for visualization. We will then give a general introduction to the scikit-learn project and API and Machine Learning concepts such as supervised vs unsupervised ...
This book, fully updated for Python version 3.6+, covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. All the figures and numerical results are reproducible using the Python codes provided. The author develops key intuitions ...
That is what Machine Learning is for! Analyzing data and predicting the outcome! In Machine Learning it is common to work with very large data sets. In this tutorial we will try to make it as easy as possible to understand the different concepts of machine learning, and we will work with...
In this course, you'll learn how to use tree-based models and ensembles for regression and classification using scikit-learn. See Details Start Course See More Related Tutorial Introduction to Machine Learning in Python In this tutorial, you will be introduced to the world of Machine Learning (...
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本文主要参考英文教材Python Machine Learning第二章。pdf文档下载链接: https://pan.baidu.com/s/1nuS07Qp 密码: gcb9。 本文主要内容包括利用Python实现一个感知机模型并利用这个感知机模型完成一个分类任务。 Warren和McCullock于1943年首次提出MCP neuron神经元模型[1],之后,Frank Rosenblatt在MCP neuron model...
快速上手教程 https://elitedatascience.com/python-machine-learning-tutorial-scikit-learn 我可以说这是迄今为止我见过的设计得最完善的 ML 包。它实现了广泛的机器学习算法,并使其易于嵌入到实际应用程序中。你可以使用库中大量的函数,比如回归、聚类、模型选择、预处理、分类等等。所以它值得全面学习和使用。它最...