Fundamentals in Neural Networks

Build up your intuition of the fundamental building blocks of Neural Networks.

4.25 (16 reviews)
Udemy
platform
English
language
Science
category
instructor
Fundamentals in Neural Networks
2,088
students
6.5 hours
content
Jul 2022
last update
$54.99
regular price

What you will learn

Understand the intuition behind Artificial Neural Networks

Understand the intuition behind Convolutional Neural Networks

Understand the intuition behind Recurrent Neural Networks

Apply Artificial Neural Networks in practice

Apply Convolutional Neural Networks in practice

Apply Recurrent Neural Networks in practice

Why take this course?

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 Network

1.1 Linear Regression

1.2 Logistic Regression

1.3 Purpose of Neural Network

1.4 Forward Propagation

1.5 Backward Propagation

1.6 Activation Function (Relu, Sigmoid, Softmax)

1.7 Cross-entropy Loss Function

1.8 Gradient Descent


Section 2 - Convolutional Neural Network

2.1 Image Data

2.2 Tensor and Matrix

2.3 Convolutional Operation

2.4 Padding

2.5 Stride

2.6 Convolution in 2D and 3D

2.7 VGG16

2.8 Residual Network


Section 3 - Recurrent Neural Network

3.1 Welcome

3.2 Why use RNN

3.3 Language Processing

3.4 Forward Propagation in RNN

3.5 Backpropagation through Time

3.6 Gated Recurrent Unit (GRU)

3.7 Long Short Term Memory (LSTM)

3.8 Bidirectional RNN (bi-RNN)


Section 4 - Technical Walkthrough: Artificial Neural Network

This section walks through each and every building block of deploying an Artificial Neural Network using tensorflow.


Section 5 - Technical Walkthrough: Convolutional Neural Network

This section walks through each and every building block of deploying a Convolutional Neural Network using tensorflow.


Section 6 - Technical Walkthrough: Recurrent Neural Network

This section walks through each and every building block of deploying an Recurrent Neural Network using tensorflow.


Section 7 - Advanced Topics: Autoencoders

This section walks through each and every building block of deploying an Autoencoder using tensorflow. Further, we explore the inference problems using the latent layers of the autoencoder.


Section 8 - Advanced Topics: Image Segmentation

This section walks through each and every building block of deploying an Image-to-image model using tensorflow.



Screenshots

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Reviews

Osman
June 27, 2023
While the material is very good, it is negatively affected by a) Poor and fast handwriting b) poor sound recording. Since we are dealing with AI. shy not converting handwritten text into print letters. The same to the poor sound..thanks
Da
December 5, 2021
A practical guide to Neural Networks even for beginners. Instructor Yin organizes the course structure well and this course is designed with a good balance of instruction videos and examples of applications.

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4424888
udemy ID
12/1/2021
course created date
1/2/2022
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