[100%OFF]Fundamentals in Neural Networks

⏱ Duration12 hours
❤ Rating: out of 5.0
📢 language:English
🎯 Platform: udemy


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🛠 Requirement

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🧾what you will learn:

There is no prior coding or programming experience required. This course assumes you have your own laptop and the code will be done using Colab.


Created by Yiqiao Yin
4.7 Rating for Instructor
22 Reviews for the courses
5,340 Students Enrolled
7 Number of courses
                🎁 Coupon Code: CYBERJULY

📚 Description

DescriptionDeep 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 RNN (bi-RNN)Who this course is for:Beginner level audience that intends to obtain in-depth overview of Artificial Intelligence, Deep Learning, and three major types neural networks: Artificial Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks.Show moreShow less
[100%OFF]Fundamentals in Neural Networks
[100%OFF]Fundamentals in Neural Networks


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