种子简介

种子名称： [FreeCoursesOnline.Me] MANNING - Machine Learning for Mere Mortals
文件类型： 视频
文件数目： 66个文件
文件大小： 2.69 GB
收录时间： 2021-12-4 22:02
已经下载： 3次
资源热度： 203
最近下载： 2024-7-23 02:36

下载BT种子文件

下载Torrent文件(.torrent) 立即下载

磁力链接下载

magnet:?xt=urn:btih:801c8a91963034d9453043ad07eb54ca5d40987a&dn=[FreeCoursesOnline.Me] MANNING - Machine Learning for Mere Mortals 复制链接到迅雷、QQ旋风进行下载，或者使用百度云离线下载。

喜欢这个种子的人也喜欢

种子包含的文件

[FreeCoursesOnline.Me] MANNING - Machine Learning for Mere Mortals.torrent

U01M01 The basics.mp434.68MB

U01M02 Machine Learning versus Artificial Intelligence.mp433.01MB

U01M03 Supervised learning.mp443.06MB

U01M04 Unsupervised learning.mp416.22MB

U01M05 Reinforcement learning.mp428.4MB

U01M06 A quick math refresher.mp411.02MB

U01M07 Slope of a line.mp445.1MB

U01M08 Scalars, vectors, and tensors.mp426.47MB

U01M09 Matrices and matrix arithmetic.mp413.44MB

U01M10 Set up your computing environment.mp41.83MB

U01M11 Install Python tools.mp411.18MB

U01M12 Create virtualenv environment.mp45.88MB

U01M13 Install Tensorflow.mp418.31MB

U01M14 The projects.mp410.06MB

U02M01 Supervised learning.mp422.32MB

U02M02 Trend lines.mp44.62MB

U02M03 Cost functions.mp43.8MB

U02M04 Minimizing cost functions.mp48.78MB

U02M05 Visualizing data.mp434.97MB

U02M06 Using linear regression to predict values.mp422.84MB

U02M07 More complicated functions.mp41.71MB

U02M08 Working with matrices.mp44.61MB

U02M09 Letting Tensorflow do the hard work.mp414.17MB

U03M01 More supervised learning.mp44.72MB

U03M02 What are features_.mp45.63MB

U03M03 What makes a good feature_.mp415.53MB

U03M04 Decision trees.mp410.58MB

U03M05 K-nearest neighbor.mp49.92MB

U03M06 Linear classification.mp47.89MB

U03M07 Making it work in Tensorflow.mp421.22MB

U03M08 Creating a spam filter.mp413.66MB

U03M09 Tools and data for email classification.mp436MB

U03M10 Classifying emails.mp49.15MB

U04M01 How clustering works.mp482.12MB

U04M02 Clustering algorithms.mp456.82MB

U04M03 Introducing k-means.mp456.87MB

U04M06 Assigning Points to a Centroid in K-means).mp434.33MB

U05M01 What are neural networks, and how do they work.mp468.7MB

U05M02 The Tensorflow Playground interface.mp414.04MB

U05M03 Adding nodes to use multiple models in the TensorFlow Playground.mp412.81MB

U05M04 What hidden layers are, and how to use them with TensorFlow Playground.mp443.92MB

U05M05 What is the activation function in a neural network_.mp435.82MB

U06M01 Using Neural Networks.mp456.56MB

U06M02 How encoding non-numeric data works.mp464.84MB

U06M03 One hot encoding.mp484.5MB

U06M04 How image recognition relates to a neural network.mp495.14MB

U07M01 Encoding and Representation.mp421.27MB

U07M02 Numeric representation of data.mp440.61MB

U07M03 Text representation of data.mp431.82MB

U07M04 Representation of image data.mp434.06MB

U07M05 Representation of audio data.mp428.39MB

U07M06 Analytics, stock prices, and other time series data.mp443.63MB

U07M07 Preparing data_ finding the data set.mp431.31MB

U07M08 Preparing data_ Features engineering.mp461.38MB

U07M09 Principal Component Analysis_ The mathematical way to determine features.mp423.11MB

U07M10 Feature selection.mp434.54MB

U07M11 Geometry of the data space and the curse of dimensionality.mp458.61MB

U08M01 The difference between an algorithm and a model.mp450.27MB

U08M02 Chaining together models.mp4146.62MB

U09M01 Improving performance in machine learning routines.mp489.81MB

U09M02 Using parallelization.mp457.25MB

U09M03 Outliers.mp4204.64MB

U09M05 What should we do with outliers_.mp4119.1MB

U09M06 Robustness and noise.mp440.25MB

U09M07 Overfitting.mp4104.22MB

U09M08 Regularization.mp4272.46MB