Sure, here's a sample year-long syllabus for teaching high school students about neural networks with a focus on promoting creativity and entrepreneurship:

Week 1: Introduction to Neural Networks

* Overview of artificial intelligence (AI) and machine learning (ML)
* Definition of neurons and their role in human brains
* History of AI and ML
* Explanation of how neural networks work and their components (layers, nodes, activation functions)
* Discussion of different types of neural networks (feedforward, convolutional, recurrent) and their applications
* Assignment: Research a current application of neural networks and present it to the class

Week 2: Data Preparation

* Importance of clean and well-structured data for training neural networks
* Understanding of data formats and how they affect network performance
* Introduction to data preprocessing techniques such as normalization, scaling, feature selection, and handling missing values
* Practice using Python libraries (e.g., Pandas, NumPy) to prepare datasets
* Assignment: Clean and structure a dataset and explain your reasoning

Week 3: Feedforward Neural Networks

* Detailed explanation of feedforward neural networks including input layer, hidden layers, output layer, forward propagation, backpropagation, cost function, optimization algorithms, and hyperparameters tuning
* Implementation of feedforward neural networks using Python libraries (e.g., TensorFlow, Keras)
* Hands-on exercise using real or simulated data to train a simple feedforward neural network and evaluate its performance
* Assignment: Train a feedforward neural network model on a given dataset and optimize its parameters to improve accuracy

Week 4: Convolutional Neural Networks (CNNs)

* Explanation of CNN architecture and its use in image classification tasks
* Importance of pooling and strides in CNNs
* Understanding of batch normalization and ReLU activation function
* Practice implementing CNN models using Python librari
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