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Applied AI from Scratch in Python培訓(xùn)
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 班級(jí)規(guī)模及環(huán)境--熱線:4008699035 手機(jī):15921673576( 微信同號(hào)) |
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每期人數(shù)限3到5人。 |
| 上課時(shí)間和地點(diǎn) |
開(kāi)課地址：【上海】同濟(jì)大學(xué)(滬西)/新城金郡商務(wù)樓(11號(hào)線白銀路站)【深圳分部】：電影大廈(地鐵一號(hào)線大劇院站) 【武漢分部】：佳源大廈【成都分部】：領(lǐng)館區(qū)1號(hào)【沈陽(yáng)分部】：沈陽(yáng)理工大學(xué)【鄭州分部】：錦華大廈【石家莊分部】：瑞景大廈【北京分部】：北京中山學(xué)院 【南京分部】：金港大廈
最新開(kāi)班 (連續(xù)班 、周末班、晚班）：2020年3月16日 |
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1、培訓(xùn)過(guò)程中，如有部分內(nèi)容理解不透或消化不好，可免費(fèi)在以后培訓(xùn)班中重聽(tīng)；
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課程大綱 |
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- Supervised learning: classification and regression
Machine Learning in Python: intro to the scikit-learn API
linear and logistic regression
support vector machine
neural networks
random forest
Setting up an end-to-end supervised learning pipeline using scikit-learn
working with data files
imputation of missing values
handling categorical variables
visualizing data
Python frameworks for for AI applications:
TensorFlow, Theano, Caffe and Keras
AI at scale with Apache Spark: Mlib
Advanced neural network architectures
convolutional neural networks for image analysis
recurrent neural networks for time-structured data
the long short-term memory cell
Unsupervised learning: clustering, anomaly detection
implementing principal component analysis with scikit-learn
implementing autoencoders in Keras
Practical examples of problems that AI can solve (hands-on exercises using Jupyter notebooks), e.g.
image analysis
forecasting complex financial series, such as stock prices,
complex pattern recognition
natural language processing
recommender systems
Understand limitations of AI methods: modes of failure, costs and common difficulties
overfitting
bias/variance trade-off
biases in observational data
neural network poisoning
Applied Project work (optional)
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