Introduction to Neural Networks in Python - Tensorflow-Keras

Overview

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Dive into a comprehensive tutorial on neural networks in Python using TensorFlow and Keras. Learn the fundamentals of neural network architecture, including input layers, hidden layers, and output layers. Explore key concepts such as hyperparameters, batch size, learning rate, optimizers, activation functions, and dropout. Gain practical experience through coding examples that progress from simple linear classifications to complex multi-label tasks. Discover how to load and process data, build and fit neural networks, and evaluate model performance. Follow along with hands-on exercises, including classifying quadratic data, clustering, and predicting single data points. By the end of this tutorial, acquire the essential knowledge and skills to start implementing neural networks for various classification tasks in Python.

Syllabus

Video overview
Why use neural networks
How neural nets work architecture basics
Hyperparameter overview batch size, optimizer, dropout, learning rate, epochs
How do we choose layers, neurons, & other parameters?
Why do we need an activation function?
What activation function should I use?
Keras vs Tensorflow vs PyTorch
Coding starts github & setup
Writing our first neural network linear example
Selecting optimizer & loss function model.compile
Fitting training data to our model model.fit
Shuffle order of training data
Evaluate model on test data model.evaluate
Example #2: Classifying quadratic data
Example #3: Classifying 6 clusters of data try on your own
Using network to predict a single data point model.predict
Example #4: Classifying multiple labels at a time BinaryCrossentropy loss
Example #5: Classifying our complex data from start of video
Conclusion & Next steps of learning neural nets