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Fundamentals of Neural Networks - Long Short-Term Memory (LSTM)
Long short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture used in the field of deep learning. LSTM networks are well-suited to classifying, processing, and making predictions based on time series data...
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Fundamentals of Neural Networks - Gated Recurrent Unit (GRU)
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"...
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Fundamentals of Neural Networks - Backward Propagation Through Time
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...
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Fundamentals of Neural Networks - Forward Propagation in RNN
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...
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Fundamentals of Neural Networks - Language Processing
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...
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Fundamentals of Neural Networks - Why Use RNN
A Recurrent neural network is a type of artificial neural network commonly used in speech recognition and natural language processing. Recurrent neural networks recognize data's sequential characteristics and use patterns to predict the...
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Fundamentals of Neural Networks - Welcome to RNN
This video explains recurrent neural networks and why we want to use 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...
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Fundamentals of Neural Networks - Lab 1 - Introduction to Convolutional 1-Dimensional
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...
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Fundamentals of Neural Networks - Residual Network
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...
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Fundamentals of Neural Networks - VGG16
This video explains VGG16 which is a convolutional neural network model proposed by K. Simonyan and A. Zisserman from the University of Oxford in the paper "Very Deep Convolutional Networks for Large-Scale Image Recognition". This clip...
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Fundamentals of Neural Networks - Convolution in 2D and 3D
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...
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Fundamentals of Neural Networks - Stride
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...
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Fundamentals of Neural Networks - Padding
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...
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Fundamentals of Neural Networks - Convolutional Operation
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...
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Fundamentals of Neural Networks - Tensor and Matrix
This video explains what we mean by Tensor and Matrix. 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...
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Fundamentals of Neural Networks - Image Data
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...
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Fundamentals of Neural Networks - Lab 5 - Building Deeper and Wider Model
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...
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Fundamentals of Neural Networks - Gradient Descent
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...
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Fundamentals of Neural Networks - Cross-Entropy Loss Function
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...
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Fundamentals of Neural Networks - Activation Function
This video explains the role of the activation function, which is an interesting phenomenon in the design of neural networks. This clip is from the chapter "Artificial Neural Networks" of the series "Fundamentals in Neural Networks".This...
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Fundamentals of Neural Networks - Backward Propagation
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...
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Fundamentals of Neural Networks - Forward Propagation
This video explains forward propagation and will dive deeper into the architecture of neural networks. This clip is from the chapter "Artificial Neural Networks" of the series "Fundamentals in Neural Networks".This section explains...
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Fundamentals of Neural Networks - Purpose of Neural Networks
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...
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Fundamentals of Neural Networks - Logistic Regression
This video explains logistic regression and specifically if the target here is discrete or binary. This clip is from the chapter "Artificial Neural Networks" of the series "Fundamentals in Neural Networks".This section explains...