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video start
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Content intro
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Google Colab and notebook Intro
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05:26 1st Notebook model with only training data
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Data Import
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EDA Exploratory Data Analysis
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Data Split Train and Target
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Training data Transformation
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Train and Test Data Split
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Setup model network and layers
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Network Visualization
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Callback function to plot loss per epoch
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Compile Model
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Start Model Training
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Callback function to plot loss & accuracy per epoch
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Check model loss and accuracy in real time
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Validate Model History
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Check Model Performance
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27:17 2nd Notebook model with training & validation data
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Training data split into Train & Validation data
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Compile Model & start Model Training
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Check model loss and accuracy with validation in real time
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Check Model Performance
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Saving colab notebook to Github
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Credits
Description:
Dive into deep learning with Keras in this comprehensive 35-minute tutorial. Learn to build and train machine learning models for heart disease prediction using Google Colab and Jupyter notebooks. Start with data import and exploratory data analysis, then progress through data splitting, transformation, and model setup. Visualize network architecture, implement callback functions for real-time performance tracking, and compare models trained with and without validation data. Master techniques for compiling, training, and evaluating model performance. Gain hands-on experience with ready-to-use code available on GitHub, suitable for local execution or Google Colab. Perfect for AI engineers looking to enhance their skills in deep learning with Keras.

An AI Engineer's Guide to Machine Learning with Keras

Prodramp
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#Computer Science #Deep Learning #Keras #Data Science #Data Visualization #Artificial Intelligence #Neural Networks #Data Analysis #Exploratory Data Analysis #Machine Learning #Model Training #AI Engineering
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