1. Introduction

This tutorial aims to introduce the basic concepts of neural network design, including different types of architectures and how to choose the right one for your specific problem.

By the end of this tutorial, you'll:
- Have an understanding of the basic concepts of neural networks.
- Be familiar with different types of neural network architectures.
- Know how to choose the right architecture for your problem.

Before you begin, it's recommended to have a basic understanding of Python programming and Machine Learning concepts. Familiarity with libraries like TensorFlow or PyTorch can be an added advantage.

2. Step-by-Step Guide

2.1 Neural Networks

Neural networks are a set of algorithms that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling or clustering raw input.

2.2 Types of Neural Network Architectures

There are many types of neural network architectures. Here, we'll focus on three main types:
1. Feedforward Neural Networks (FNNs): Information in this network moves in only one direction—forward—from the input layer, through the hidden layers, to the output layer.

1. Recurrent Neural Networks (RNNs): These have connections that can form directed cycles. This means that you can sometimes return to where you started, allowing for feedback connections.

2. Convolutional Neural Networks (CNNs): These are mainly used for image processing, recognition, and processing, and are designed to automatically and adaptively learn spatial hierarchies of features.

2.3 Choosing the Right Architecture

The choice of architecture depends on the type of problem you're trying to solve. For instance, use FNNs for simple pattern recognition, RNNs for time series analysis, and CNNs for image recognition tasks.

3. Code Examples

3.1 Building a Simple Feedforward Neural Network with TensorFlow

import tensorflow as tf

# Define a model
model = tf.keras.models.Sequential()

# Add the input layer and hidden layer
model.add(tf.keras.layers.Dense(units=5, activation='relu', input_shape=(3, )))

# Add the output layer
model.add(tf.keras.layers.Dense(units=1, activation='sigmoid'))

# Compile the model
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

# Print the summary of the model
model.summary()

• Sequential() creates an empty model.
• The add() function adds layers to the model. We're adding two layers: one hidden layer with 5 neurons and an output layer with 1 neuron. The input_shape=(3, ) parameter is needed for the first layer to establish the input shape.
• compile() configures the model for training.

3.2 Expected Output

Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense (Dense)                (None, 5)                 20        
_________________________________________________________________
dense_1 (Dense)              (None, 1)                 6         
=================================================================
Total params: 26
Trainable params: 26
Non-trainable params: 0
_________________________________________________________________

4. Summary

In this tutorial, we've learned about the basics of neural network design, including the different types of architectures like FNNs, RNNs, and CNNs. We also discussed how to choose the right architecture for your problem. Finally, we walked through a code example of building a simple FNN using TensorFlow.

To further your learning, you might want to explore more complex architectures and other libraries like PyTorch.

5. Practice Exercises

1. Exercise: Create a simple RNN using TensorFlow.
2. Exercise: Create a simple CNN using TensorFlow.

Tips: For the RNN, remember that you'll need to use SimpleRNN layers. For the CNN, you'll need to use Conv2D and MaxPooling2D layers. You can find examples and documentation for these on the TensorFlow website.

Remember, the best way to learn is by doing. Happy coding!