Fashion MNIST is a direct drop-in replacement for the original MNIST dataset. The dataset is made up of 60,000 training examples and 10,000 testing examples, where each example is a 28 28 grayscaled picture of various articles of clothing. The Fashion MNIST dataset is more difficult than the original MNIST, and thus serves as a more complete benchmarking tool. The model being trained is a CNN with three convolutional layers followed by two dense layers. The job will run for 30 epochs, with a batch size of 128.
Kaggle is a great resource for datasets, as you'll see new ones pop up as competitions begin. Common Crawl is a popular source for text data, and was used to train the higher-dimensional GloVe word vectors. Speaking of which, the GloVe pre-trained word vectors are available to download on that site, which may be useful to you! ImageNet is a common source of images, and the classification task setup by that dataset is used as a benchmark for model quality. Finally, you could always create your own dataset! Really, being a machine learning researcher or practitioner involves a ton more data collection, cleaning, manipulating, and general handling than it may appear at first glance.
Running short on images to train your model? Here's how to increase your dataset size multi-fold with synthetic images using Image… Availability of an extensive, versatile dataset could seal the deal, and you could jump to the next step of your Machine Learning / Deep Learning pipeline. But often you might end up in a situation where the dataset you need is just not readily available.
One goal of machine learning algorithms is to use past information to predict the future. The advantage of machine learning over traditional analytics is the ability for the machine-learning algorithm to automatically build a good model, saving time, preventing overfitting, and generally being more robust. To do this the algorithm builds a model, calculates the error rate of the model, adjusts parameters to lower the error rate, and iterates again, 'learning' from its mistakes. There's a step in between: calculating the error rate requires us to split our dataset into a training and test dataset, in which we train the model on the training dataset, and calculate the error rate on the test dataset. We need to do this because if we calculate the error rate while training on the entire dataset we would get a low error rate since the model is trained on that specific data, and this would be misleading for predicting future, unknown data.
Recognition of jokes in news headlines, driving vehicles, tracking human health -- Machine Learning performs many amazing things if it has the right data. It plays a crucial role in the model training process and output quality. Whatever your algorithm is used for -- image recognition, object tracking, matchmaking or deep analysis, it needs data to learn and evaluate performance based on it. Dataset helps you to organize unstructured data collected from multiple sources to get the target outcome. Initial data that you give to an algorithm for learning is usually called a training dataset. Training data is a foundation for further development that determines how effective and useful your Machine Learning system will be.