Udemy – Keras Deep Learning and Generative Adversarial Networks (GANs) 2023-6 – Downloadly

After that, we will look at the loss function, different types of popular loss functions and the different scenarios we have for using each of them. Just like activation and loss functions, we have optimizers that optimize the neural network based on the training feedback. We will also look at the details of the most popular optimizers and which one to use. Then finally we will discuss the most popular types of deep learning neural networks and their basic structure and usage. Also, this period is divided into exactly two halves. The first half is about building deep learning multilayer neural network models for text-based datasets and the second half is about building convolutional neural networks for image-based datasets. In the text-based simple feedforward multilayer neural network model, we start with a regression model to predict house prices in King County, USA. The first step is to download and load the dataset from the Kaggle website into our application. Then as a second step, we perform EDA or exploratory data analysis of the uploaded data and then prepare the data to feed it into our deep learning model. Then we define the Keras deep learning model. Once we define the model, we compile the model and later put our dataset into the compiled model and wait for the training to complete. After training, the training history and metrics such as accuracy, loss, etc. can be evaluated and visualized using matplotlib. Finally we have our pre-trained model. We will try to predict King County real estate prices using our deep learning model and evaluate the results. This was a text-based regression model. Now we move forward with the text-based binary classification model. We will use a derived version of the heart disease dataset from the UCI Machine Learning Repository. Our goal is to predict whether a person will have heart disease or not. The same steps are repeated here. The first step is to fetch and load the dataset into our program. Then as a second step, we perform EDA or exploratory data analysis of the uploaded data and then prepare the data to feed it into our deep learning model. Then we define the Keras deep learning model. Once we define the model, we compile the model and later put our dataset into the compiled model and wait for the training to complete. After training, the training history and metrics such as accuracy, loss, etc. can be evaluated and visualized using matplotlib.

Finally we have our pre-trained model. We will try to predict heart disease using our deep learning model and evaluate the results. After the text-based binary classification model. Now we proceed with the text-based multi-class classification model. We will use the red wine quality dataset from the Kaggle website. Our goal is to predict the multiple categories in which the redwine example can be placed from the learnings obtained from this dataset. The same steps are repeated here. The first step is to fetch and load the dataset into our program. Then as a second step, we perform EDA or exploratory data analysis of the uploaded data and then prepare the data to feed it into our deep learning model. Then we define the Keras deep learning model. Once we have defined the model, we compile the model and later put our dataset into the compiled model and wait for the training to complete. After training, the training history and metrics such as accuracy, loss, etc. can be evaluated and visualized using matplotlib. Finally we have our pre-trained model. We will try to make a prediction for the wine quality with a new set of data and then evaluate the classification results. We can spend a lot of time, resources, and effort to train a deep learning model. We will learn about the techniques to save a pre-trained model. This process is called serialization. We will first serialize a model. Then load it into another program and make a prediction without repeating the tutorial. This was about text-based data. Now let’s move on to image-based data. In the introductory session, we will get acquainted with the basics of digital image, where we will learn about the composition and structure of a digital image. Then we will learn about basic image processing using Keras functions. There are many classes and functions that help in image preprocessing in the Keras library API. We will learn about the most popular and useful functions one by one. Another important and useful image processing function in Keras is Image Augmentation, where slightly different versions of images are automatically created during training. We will learn about boosting a single image, boosting images in a directory structure as well as boosting a data frame image. Then another theory session on the basics of Convolutional Neural Networks or CNN. We will learn how the main layers of CNN work like convolution layer, pooling layer and fully connected layer. Convolution for image processing has concepts like stride padding and flattening. We will also learn them one by one. Now we are ready to get started with our CNN model. We will design a model that can classify 5 different types of flowers when presented with an image of a flower in each of these categories. First, we will download the dataset from the Kaggle website. Then the first step will be to download and load this data set from our computer into our program. Then as a second step, we have to manually split this dataset for training and then testing the model. We label them in separate folders with each class in the training and testing folders. Then we define the Keras deep learning model. Once we define the model, we compile the model and later put our dataset into the compiled model and wait for the training to complete. After training, the training history and metrics such as accuracy, loss, etc. can be evaluated and visualized using matplotlib.

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