Abhishek-Thakur
In this video, the creator goes over the basics of building a simple neural network using Keras for image classification. They use the popular MNIST dataset to classify images of handwritten digits into 10 categories. They start by loading the dataset into Keras and build a sequential model with two dense layers and a 10-way softmax output layer. They then prepare the dataset for fitting by flattening and normalizing it. The creator also explains how to evaluate the model using the training and test loss, and they demonstrate how to make predictions on the test set. The final accuracy obtained is 98.24%, with the creator noting that upcoming videos will cover more technical terms related to deep learning.
In this section, the speaker introduces the first example of a neural network using the MNIST dataset to classify grayscale images of handwritten digits into 10 different categories (0 through 9). They load the dataset in Keras and go over the arrays of images and labels, verifying their shapes. They note that this dataset has been studied intensively and is considered the "hello world" of deep learning. While there may be many new concepts to understand, the speaker assures viewers that they will eventually become familiar with all the material.
In this section, the speaker explains the process of building a simple neural network using Keras. They start by loading a data set with training and test images and then proceed to build a sequential model using two dense layers and a 10-way softmax output layer. The speaker explains that the core building block of a neural network is the layer, which acts as a filter for data and extracts representations from it. They also discuss the three things needed to compile the model: an optimizer, a loss function, and metrics to monitor. Lastly, they choose RMS prop as the optimizer and explain that it's the mechanism through which the model updates itself based on the training data.
In this section, the speaker discusses how to prepare the dataset for fitting the neural network model using Keras. They mention that the dataset needs to be flattened and normalized, and they demonstrate how to do this using basic numpy operations. They also explain how to call the model.fit function to fit the model, passing the train images, train labels, number of epochs, and batch size as parameters. The speaker then shows how to make predictions on the test set, displaying the predicted probability values for each sample in the test set and explaining how to determine which value was predicted for a specific sample.
In this section, the video creator discusses the evaluation of the neural network. The accuracy of the model is determined using the training and test loss. It is explained that training accuracy is calculated on the training dataset and not on the test dataset or validation dataset. The model is evaluated using model.evaluate, with training accuracy being the test loss and test accuracy being model.evaluate with the test images and test labels. The accuracy obtained is 98.24%, which is a little bit lower than the accuracy during the training of images. The creator concludes that this is because the neural network starts to memorize the training images, and in the upcoming videos, users will learn the technical terms related to sequential, model to compile, layered or tenses, and activation functions.
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