This is by far the best resource I've seen for deep learning. I'm working on a project where I need to classify the scenes of outdoor photographs into four distinct categories: cities, beaches, mountains, and forests. I've found a small dataset ( 100 images per class), but my models are quick to overfit and far from accurate. I'm confident I can solve this project, but I need more data. Jose has a point -- without enough training data, your deep learning and machine learning models can't learn the underlying, discriminative patterns required to make robust classifications.

In this tutorial, you will learn how to tune the hyperparameters of a deep neural network using scikit-learn, Keras, and TensorFlow. Optimizing your hyperparameters is critical when training a deep neural network. There are many knobs, dials, and parameters to a network -- and worse, the networks themselves are not only challenging to train but also slow to train as well (even with GPU acceleration). Failure to properly optimize the hyperparameters of your deep neural network may lead to subpar performance. Luckily, there is a way for us to search the hyperparameter search space and find optimal values automatically -- we will cover such methods today.

In this tutorial, you will learn how to train an Optical Character Recognition (OCR) model using Keras, TensorFlow, and Deep Learning. For now, we'll primarily be focusing on how to train a custom Keras/TensorFlow model to recognize alphanumeric characters (i.e., the digits 0-9 and the letters A-Z). Building on today's post, next week we'll learn how we can use this model to correctly classify handwritten characters in custom input images. We'll be starting with the fundamentals of using well-known handwriting datasets and training a ResNet deep learning model on these data. To learn how to train an OCR model with Keras, TensorFlow, and deep learning, just keep reading.

In this tutorial, you will learn how to perform regression using Keras and Deep Learning. You will learn how to train a Keras neural network for regression and continuous value prediction, specifically in the context of house price prediction. Today's post kicks off a 3-part series on deep learning, regression, and continuous value prediction. We'll be studying Keras regression prediction in the context of house price prediction: Unlike classification (which predicts labels), regression enables us to predict continuous values. For example, classification may be able to predict one of the following values: {cheap, affordable, expensive}.

In this tutorial, you will learn how to build an R-CNN object detector using Keras, TensorFlow, and Deep Learning. Today's tutorial is the final part in our 4-part series on deep learning and object detection: What if we wanted to train an object detection network on our own custom datasets? How can we train that network using Selective Search search? And how will using Selective Search change our object detection inference script? In fact, these are the same questions that Girshick et al. had to consider in their seminal deep learning object detection paper Rich feature hierarchies for accurate object detection and semantic segmentation. Each of these questions will be answered in today's tutorial -- and by the time you're done reading it, you'll have a fully functioning R-CNN, similar (yet simplified) to the one Girshick et al. implemented!