Fundamentals of Neural Networks

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 (CNN), and (3) Recurrent Neural Networks (RNN). You will learn about logistic regression and linear regression and know the purpose of neural networks. You will also understand forward and backward propagation as well as the cross-entropy function. Furthermore, you will explore image data, convolutional operation, and residual networks. In the final section of the course, you will understand the use of RNN, Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM).

You will also have code blocks and notebooks to help you understand the topics covered in the course.

By the end of this course, you will have a hands-on understanding of Neural Networks in detail.

All resources and code files are placed here: https://github.com/PacktPublishing/Fundamentals-in-Neural-Networks

Type
video
Category
Data
publication date
2022-12-27
what you will learn

Learn about linear and logistic regression in ANN
Learn about cross-entropy between two probability distributions
Understand convolution operation which scans inputs with respect to their dimensions
Understand VGG16, a convolutional neural network model
Understand why to use recurrent neural network
Understand Long short-term memory (LSTM)

duration
397
key features
Understand the intuition behind Artificial Neural Networks, Convolution Neural Networks, and Recurrent Neural Networks Understand backward and forward propagation in ANN Understand Bidirectional Recurrent Neural Networks (BRNN)
approach
This course contains a detailed discussion on various topics of Deep Learning, mathematical description, and code walkthroughs of the three common families of neural networks.
audience
This course can be taken by a beginner level audience that intends to obtain an in-depth overview of Artificial Intelligence, Deep Learning, and three major types of neural networks: Artificial Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks. 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.
meta description
Get to know the fundamentals of Neural Networks by taking this hands-on course for beginners
short description
Get started with Neural networks and understand the underlying concepts of Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks. This hands-on course will help you understand deep learning in detail with no prior coding or programming experience required.
subtitle
Build up your intuition of the fundamental building blocks of Neural Networks
keywords
Artificial Neural Networks, Recurrent Neural Networks, Convolutional Neural Networks, ANN, CNN, RNN, logistic regression, linear regression, long short-term memory, TensorFlow, Python, Keras.
Product ISBN
9781837639519