Neural Networks Beginnings. Jade Carter

Automatic Emotion Detection:

      The neural network takes data about a person's voice, facial expressions, or body gestures.

      Then the neural network analyzes this data and uses it to determine the person's emotional state. For example, the neural network may determine that a person is happy, sad, angry, or experiencing other emotions.

      For this, the neural network can use convolutional neural networks, recurrent neural networks, or a combination of different types of networks.

      These are just some examples of how neural networks can be applied in real life. Each of these examples can be implemented using different types of neural networks and configurations, and each may require a large amount of data for training. However, understanding the basics of how neural networks work and their structural elements, such as neurons, weights, and activation functions, is key to building effective neural networks and solving various machine learning tasks.

      The examples described in the first chapter can be implemented using various software tools for machine learning and neural network development. Let's look at the most popular ones.

      TensorFlow: an open-source software for machine learning developed by Google. TensorFlow supports various types of neural networks and makes it easy to create, train, and deploy machine learning models.

      Keras: a high-level interface for building neural networks that works on top of TensorFlow. Keras simplifies the process of creating neural networks and allows for quick experimentation with different architectures and hyperparameters.

      PyTorch is an open-source machine learning software developed by Facebook. PyTorch also supports various types of neural networks and has a user-friendly interface for creating and training models.

      Scikit-learn is a Python library for machine learning. Scikit-learn includes many machine learning algorithms, including some types of neural networks, and simplifies the process of creating and evaluating models.

      The specific choice of working environment depends on the specific task and the developer's personal preferences. However, all of these tools have extensive documentation and user communities that can help in the process of working with them.

      Let's take a closer look at the implementation of the practical examples mentioned above in the TensorFlow environment.

      Digit recognition in images. For digit recognition in images, we can use a neural network with several convolutional layers and fully connected layers based on the TensorFlow library. Below is an approximate implementation of such a neural network.

      The first step is to import the necessary TensorFlow modules and load the training and testing data:

      import tensorflow as tf

      from tensorflow import keras

      #Load MNIST dataset

      (train_images, train_labels), (test_images, test_labels) = keras.datasets.mnist.load_data()

      #Convert data to a format suitable for training a neural network and normalize it

      train_images = train_images.reshape((60000, 28, 28, 1))

      train_images = train_images / 255.0

      test_images = test_images.reshape((10000, 28, 28, 1))

      test_images = test_images / 255.0

      Define the neural network model. In this example, we will use a neural network with three convolutional layers, each followed by a max pooling layer, and two fully connected layers. The output layer will consist of 10 neurons corresponding to the digit classes, and will use the softmax activation function.

      model = keras.Sequential([

       keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),

       keras.layers.MaxPooling2D((2, 2)),

       keras.layers.Conv2D(64, (3, 3), activation='relu'),

       keras.layers.MaxPooling2D((2, 2)),

       keras.layers.Conv2D(64, (3, 3), activation='relu'),

       keras.layers.Flatten(),

       keras.layers.Dense(64, activation='relu'),

       keras.layers.Dense(10, activation='softmax')

      ])

      Then we can compile the model, specifying the loss function, optimizer, and metrics for evaluating the model's performance.

      model.compile(optimizer='adam',

       loss='sparse_categorical_crossentropy',

       metrics=['accuracy'])

      After that, we can start the training process by passing the training and testing data to the model and specifying the number of epochs (iterations) and batch size (the number of examples processed in one iteration).

      model.fit(train_images, train_labels, epochs=5, batch_size=64, validation_data=(test_images, test_labels))

      Finally, we can evaluate the performance of the model on the test data.

      test_loss, test_acc = model.evaluate(test_images, test_labels)

      print('Test accuracy)

      The result of training a neural network for recognizing digits in images will be a model that can take an image of a handwritten digit as input and predict which digit is depicted in the image. This code allows us to train a neural network for object recognition in images, specifically for classifying images from the CIFAR-10 dataset. The trained neural network can be used to recognize objects in other images that were not used in the training set. To do this, simply feed the image to the neural network and get the output as the probability of belonging to each class.

      To check the accuracy of the model, a test set of images with known labels (i.e. correct answers) can be used, and the model's predictions can be compared to these labels. The higher the accuracy of the model on the test data, the more successfully it performs the task of recognizing digits.

      After training the model, it can be used to recognize digits in new images, for example, in an application for reading handwritten digits on postal codes, bank checks, or in other areas where automatic digit recognition is required.

      2. Automatic Speech Recognition. To implement the second example in the TensorFlow environment, we will need the CIFAR-10 dataset, which can be loaded using the built-in TensorFlow function. The CIFAR-10 dataset contains 60,000 color images of size 32x32 pixels, divided into 10 classes. For training the neural network, we will use 50,000 images, and for testing – the remaining 10,000. Here's what the implementation of the second example looks like in TensorFlow:

      import tensorflow as tf

      from tensorflow import keras

      from tensorflow.keras import layers

      #Defining the architecture of a neural network

      model = keras.Sequential(

       [

       layers.LSTM(128, input_shape=(None, 13)),

       layers.Dense(64, activation="relu"),

       layers.Dense(32, activation="relu"),

       layers.Dense(10, activation="softmax"),

       ]

      )

      #Compilation of the model

      model.compile(

       optimizer=keras.optimizers.Adam(learning_rate=0.001),

       loss=keras.losses.CategoricalCrossentropy(),

       metrics=["accuracy"],

      )

      #Loading audio file

      audio_file = tf.io.read_file("audio.wav")

      audio, _ = tf.audio.decode_wav(audio_file)

      audio = tf.squeeze(audio, axis=-1)

      audio = tf.cast(audio, tf.float32)

      #