For a convolutional or pooling operation, the stride denotes the number of pixels by which the window moves after each operation. This clip is from the chapter "Convolutional Neural Networks" of the series "Fundamentals in Neural...

Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. This clip is from the chapter "Convolutional Neural...

This video explains the purpose of neural networks. This clip is from the chapter "Artificial Neural Networks" of the series "Fundamentals in Neural Networks".This section explains artificial neural networks where you will learn every...

This video explains padding in convolutional neural networks. This clip is from the chapter "Convolutional Neural Networks" of the series "Fundamentals in Neural Networks".This section explains convolutional neural networks where you...

NLP is a tool for structuring data in a way that AI systems can process that deals with language. NLP uses AI to 'read' through a document and extract key information. This clip is from the chapter "Recurrent Neural Networks" of the...

This video demonstrates how to build a deeper and wider neural network model. This clip is from the chapter "Artificial Neural Networks" of the series "Fundamentals in Neural Networks".This section explains artificial neural networks...

This video demonstrates transfer learning. This clip is from the chapter "Convolutional Neural Networks" of the series "Fundamentals in Neural Networks".This section explains convolutional neural networks where you will start with image...

This video demonstrates functional API versus sequential API. This clip is from the chapter "Artificial Neural Networks" of the series "Fundamentals in Neural Networks".This section explains artificial neural networks where you will...

This video demonstrates how to use Keras TensorFlow as API to essentially design and craft the neural network architecture. This clip is from the chapter "Artificial Neural Networks" of the series "Fundamentals in Neural Networks".This...

This video demonstrates sequence-to-sequence stock candlestick forecast. This clip is from the chapter "Recurrent Neural Networks" of the series "Fundamentals in Neural Networks".This section explains NLP, we will start with recurrent...

This video demonstrates the architecture and how to carry out the code using TensorFlow in collab and building a convolutional neural network. This clip is from the chapter "Convolutional Neural Networks" of the series "Fundamentals in...

This video demonstrates how to design a recurrent neural network or RNN. This clip is from the chapter "Recurrent Neural Networks" of the series "Fundamentals in Neural Networks".This section explains NLP, we will start with recurrent...

This video demonstrates convolutional operations in 1-dimension. This clip is from the chapter "Convolutional Neural Networks" of the series "Fundamentals in Neural Networks".This section explains convolutional neural networks where you...

This video explains image data in CNN (Convolutional Neural Network). This clip is from the chapter "Convolutional Neural Networks" of the series "Fundamentals in Neural Networks".This section explains convolutional neural networks where...

This video explains the optimization problem using the gradient descent algorithm. This clip is from the chapter "Artificial Neural Networks" of the series "Fundamentals in Neural Networks".This section explains artificial neural...

Gated recurrent units (GRUs) are a gating mechanism in recurrent neural networks. GRUs have been shown to exhibit better performance on certain smaller and less frequent datasets. This clip is from the chapter "Recurrent Neural Networks"...

The forward propagation in an RNN makes a few assumptions: 1) We assume the hyperbolic tangent activation function for the hidden layer. 2) We assume that the output is discrete as if the RNN is used to predict words or characters. This...

This video explains the cross-entropy function, which is designed under the assumption that the variable you are trying to predict is binary. This clip is from the chapter "Artificial Neural Networks" of the series "Fundamentals in...

This video explains the course outline and what the course has to offer. This clip is from the chapter "Welcome" of the series "Fundamentals in Neural Networks".This section introduces you to the course and the course outline.

The Convolution layer (CONV) uses filters that perform convolution operations as it is scanning the input with respect to its dimensions. Its hyperparameters include the filter size and stride. The resulting output is called a feature...

This video explains Convolution in 2D and 3D. This clip is from the chapter "Convolutional Neural Networks" of the series "Fundamentals in Neural Networks".This section explains convolutional neural networks where you will start with...

Bidirectional recurrent neural networks (BRNN) connect two hidden layers of opposite directions to the same output. BRNNs are especially useful when the context of the input is needed. For example, in handwriting recognition, the...

Backpropagation through time (BPTT) is a gradient-based technique for training certain types of recurrent neural networks. It can be used to train Elman networks. The algorithm was independently derived by numerous researchers. This clip...

This video explains backward propagation, which is defined by the optimization problem called the gradient descent algorithm. This clip is from the chapter "Artificial Neural Networks" of the series "Fundamentals in Neural Networks".This...