How do Neural Networks learn? Take a whirlwind tour of Neural Network architectures Train Neural Networks Optimize Neural Network to achieve SOTA performance Weights & Biases How You Can Train Your Own Neural Nets 3. The Codebit.ly/keras-neural-nets 4. The Goal For Today Code 5. Basic Neural Network Architecture 6. This is the number of features your neural network uses to make its predictions. The input vector needs one input neuron per feature. You want to carefully select these features and remove any that may contain patterns that won't generalize beyond the training set (and cause overfitting).
This is the fifth post (post1, post2, post 3, post 4) in the series that I am writing based on the book First contact with DEEP LEARNING, Practical introduction with Keras. In it I will present an intuitive vision of the main components of the learning process of a neural network and put into practice some of the concepts presented here with an interactive tool called TensorFlow Playground. Remember that a neural network is made up of neurons connected to each other; at the same time, each connection of our neural network is associated with a weight that dictates the importance of this relationship in the neuron when multiplied by the input value. Each neuron has an activation function that defines the output of the neuron. The activation function is used to introduce non-linearity in the modeling capabilities of the network. We have several options for activation functions that we will present in this post. Training our neural network, that is, learning the values of our parameters (weights wij and bj biases) is the most genuine part of Deep Learning and we can see this learning process in a neural network as an iterative process of "going and return" by the layers of neurons. The "going" is a forwardpropagation of the information and the "return" is a backpropagation of the information. The first phase forwardpropagation occurs when the network is exposed to the training data and these cross the entire neural network for their predictions (labels) to be calculated.
This section provides more resources on the topic if you are looking to go deeper. In this post, you discovered tips and tricks for getting the most out of the backpropagation algorithm when training neural network models. Have you tried any of these tricks on your projects? Let me know about your results in the comments below. Do you have any questions? Ask your questions in the comments below and I will do my best to answer.
The vanishing gradients problem is one example of unstable behavior that you may encounter when training a deep neural network. It describes the situation where a deep multilayer feed-forward network or a recurrent neural network is unable to propagate useful gradient information from the output end of the model back to the layers near the input end of the model. The result is the general inability of models with many layers to learn on a given dataset or to prematurely converge to a poor solution. Many fixes and workarounds have been proposed and investigated, such as alternate weight initialization schemes, unsupervised pre-training, layer-wise training, and variations on gradient descent. Perhaps the most common change is the use of the rectified linear activation function that has become the new default, instead of the hyperbolic tangent activation function that was the default through the late 1990s and 2000s. In this tutorial, you will discover how to diagnose a vanishing gradient problem when training a neural network model and how to fix it using an alternate activation function and weight initialization scheme.
Deep Learning (DL) models are revolutionizing the business and technology world with jaw-dropping performances in one application area after another -- image classification, object detection, object tracking, pose recognition, video analytics, synthetic picture generation -- just to name a few. However, they are like anything but classical Machine Learning (ML) algorithms/techniques. DL models use millions of parameters and create extremely complex and highly nonlinear internal representations of the images or datasets that are fed to these models. Whereas for the classical ML, domain experts and data scientists often have to write hand-crafted algorithms to extract and represent high-dimensional features from the raw data, deep learning models, on the other hand, automatically extracts and work on these complex features. A lot of theory and mathematical machines behind the classical ML (regression, support vector machines, etc.) were developed with linear models in mind.