Fundamentals in Neural Networks

Partner: Udemy
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Description: Deep learning (also known as deep structured learning) is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised. Deep-learning architectures such as deep neural networks, deep belief networks, deep reinforcement learning, recurrent neural networks and convolutional neural networks have been applied to fields including computer vision, speech recognition, natural language processing, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance.This course covers the following three sections: (1) Neural Networks, (2) Convolutional Neural Networks, and (3) Recurrent Neural Networks. You will be receiving around 4 hours of materials on detailed discussion, mathematical description, and code walkthroughs of the three common families of neural networks. The descriptions of each section is summarized below.Section 1 - Neural Network1.1 Linear Regression1.2 Logistic Regression1.3 Purpose of Neural Network1.4 Forward Propagation1.5 Backward Propagation1.6 Activation Function (Relu, Sigmoid, Softmax)1.7 Cross-entropy Loss Function1.8 Gradient DescentSection 2 - Convolutional Neural Network2.1 Image Data2.2 Tensor and Matrix2.3 Convolutional Operation2.4 Padding2.5 Stride2.6 Convolution in 2D and 3D2.7 VGG162.8 Residual NetworkSection 3 - Recurrent Neural Network3.1 Welcome3.2 Why use RNN3.3 Language Processing3.4 Forward Propagation in RNN3.5 Backpropagation through Time3.6 Gated Recurrent Unit (GRU)3.7 Long Short Term Memory (LSTM)3.8 Bidirectional
Category: Teaching & Academics > Science > Deep Learning
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Price: 34.99
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Source: Impact
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