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JNTUH B.TECH R18 4-1 Syllabus For Neural networks & deep learning PDF 2022

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JNTUH B.TECH R18 4-1 Syllabus For Neural networks & deep learning PDF 2022

Get Complete Lecture Notes for Neural networks & deep learning on Cynohub APP

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You will be able to find information about Neural networks & deep learning along with its Course Objectives and Course outcomes and also a list of textbook and reference books in this blog.You will get to learn a lot of new stuff and resolve a lot of questions you may have regarding Neural networks & deep learning after reading this blog. Neural networks & deep learning has 5 units altogether and you will be able to find notes for every unit on the CynoHub app. Neural networks & deep learning can be learnt easily as long as you have a well planned study schedule and practice all the previous question papers, which are also available on the CynoHub app.

All of the Topic and subtopics related to Neural networks & deep learning are mentioned below in detail. If you are having a hard time understanding Neural networks & deep learning or any other Engineering Subject of any semester or year then please watch the video lectures on the official CynoHub app as it has detailed explanations of each and every topic making your engineering experience easy and fun.

Neural networks & deep learning Unit One

UNIT-I

Artificial Neural Networks Introduction, Basic models of ANN, important terminologies, Supervised Learning Networks, Perceptron Networks, Adaptive Linear Neuron, Back-propagation Network. Associative Memory Networks. Training Algorithms for pattern association, BAM and Hopfield Networks

Neural networks & deep learning Unit Two

UNIT-II

Unsupervised Learning Network- Introduction, Fixed Weight Competitive Nets, Maxnet, Hamming Network, Kohonen Self-Organizing Feature Maps, Learning Vector Quantization, Counter Propagation Networks, Adaptive Resonance Theory Networks. Special Networks-Introduction to various networks.

Get Complete Lecture Notes for Neural networks & deep learning on Cynohub APP

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Neural networks & deep learning Unit Three

UNIT – III

Introduction to Deep Learning, Historical Trends in Deep learning, Deep Feed – forward networks, Gradient-Based learning, Hidden Units, Architecture Design, Back-Propagation and Other Differentiation Algorithms

Neural networks & deep learning Unit Four

UNIT – IV

Regularization for Deep Learning: Parameter norm Penalties, Norm Penalties as Constrained Optimization, Regularization and Under-Constrained Problems, Dataset Augmentation, Noise Robustness, Semi-Supervised learning, Multi-task learning, Early Stopping, Parameter Typing and Parameter Sharing, Sparse Representations, Bagging and other Ensemble Methods, Dropout, Adversarial Training, Tangent Distance, tangent Prop and Manifold, Tangent Classifier

Neural networks & deep learning Unit Five

UNIT – V

Optimization for Train Deep Models: Challenges in Neural Network Optimization, Basic Algorithms, Parameter Initialization Strategies, Algorithms with Adaptive Learning Rates, Approximate Second- Order Methods, Optimization Strategies and Meta-Algorithms
Applications: Large-Scale Deep Learning, Computer Vision, Speech Recognition, Natural Language Processing

Neural networks & deep learning Course Objectives

To introduce the foundations of Artificial Neural Networks
To acquire the knowledge on Deep Learning Concepts
To learn various types of Artificial Neural Networks
To gain knowledge to apply optimization strategies

Neural networks & deep learning Course Outcomes

Ability to understand the concepts of Neural Networks
Ability to select the Learning Networks in modeling real world systems
Ability to use an efficient algorithm for Deep Models
Ability to apply optimization strategies for large scale applications

Neural networks & deep learning Text Books

Deep Learning: An MIT Press Book By Ian Goodfellow and Yoshua Bengio and Aaron Courville
Neural Networks and Learning Machines, Simon Haykin, 3rd Edition, Pearson Prentice Hall.

Neural networks & deep learning Reference Books

coming soon

Scoring Marks in Neural networks & deep learning

Scoring a really good grade in Neural networks & deep learning is a difficult task indeed and CynoHub is here to help!. Please watch the video below and find out how to get 1st rank in your B.tech examinations . This video will also inform students on how to score high grades in Neural networks & deep learning. There are a lot of reasons for getting a bad score in your Neural networks & deep learning exam and this video will help you rectify your mistakes and help you improve your grades.

Information about JNTUH B.Tech R18 Neural networks & deep learning was provided in detail in this article. To know more about the syllabus of other Engineering Subjects of JNTUH check out the official CynoHub application. Click below to download the CynoHub application.

Get Complete Lecture Notes for Neural networks & deep learning on Cynohub APP

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