sc JNTU-K B.TECH R19 4-2 Syllabus For Deep learning PDF 2022 – Cynohub

# JNTU-K B.TECH R19 4-2 Syllabus For Deep learning PDF 2022

### Get Complete Lecture Notes for Deep learning on Cynohub APP

You will be able to find information about 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 Deep learning after reading this blog. Deep learning has 5 units altogether and you will be able to find notes for every unit on the CynoHub app. 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 Deep learning are mentioned below in detail. If you are having a hard time understanding 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.

### Deep learning Unit One

#### Linear Algebra

Linear Algebra: Scalars, Vectors, Matrices and Tensors, Matrix operations, types of matrices, Norms, Eigen decomposition, Singular Value Decomposition, Principal Components Analysis.

Probability and Information Theory: Random Variables, Probability Distributions, Marginal Probability, Conditional Probability, Expectation, Variance and Covariance, Bayes’ Rule, Information Theory. Numerical Computation: Overflow and Underflow, Gradient-Based Optimization, Constrained Optimization, Linear Least Squares.

### Deep learning Unit Two

#### Machine Learning

Machine Learning: Basics and Underfitting, Hyper parameters and Validation Sets, Estimators, Bias and Variance, Maximum Likelihood, Bayesian Statistics, Supervised and Unsupervised

Learning, Stochastic Gradient Descent, Challenges Motivating Deep Learning. Deep Feedforward Networks: Learning XOR, Gradient-Based Learning, Hidden Units, Architecture Design, Back-Propagation and other Differentiation Algorithms.

### Deep learning Unit Three

#### Regularization for Deep Learning

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 Tying and Parameter Sharing, Sparse Representations, Bagging and Other Ensemble Methods, Dropout, Adversarial Training, Tangent Distance, Tangent Prop and Manifold Tangent

Classifier. Optimization for Training Deep Models: Pure Optimization, Challenges in Neural

Network Optimization, Basic Algorithms, Parameter Initialization Strategies, Algorithms with Adaptive Learning Rates, Approximate Second-Order Methods, Optimization Strategies and Meta-Algorithms.

### Deep learning Unit Four

#### Convolutional Networks

Convolutional Networks: The Convolution Operation, Pooling, Convolution, Basic Convolution Functions, Structured Outputs, Data Types, Efficient Convolution Algorithms, Random or Unsupervised Features, Basis for Convolutional Networks.

### Deep learning Unit Five

#### Sequence Modeling

Sequence Modeling: Recurrent and Recursive Nets: Unfolding Computational Graphs, Recurrent

Neural Networks, Bidirectional RNNs, Encoder-Decoder Sequence-to-Sequence Architectures, Deep Recurrent Networks, Recursive Neural Networks, Echo State Networks, LSTM, Gated RNNs, Optimization for Long-Term Dependencies, Auto encoders, Deep Generative Models.

### Deep learning Course Objectives

 Demonstrate the major technology trends driving Deep Learning

 Build, train and apply fully connected deep neural networks

 Implement efficient (vectorized) neural networks

 Analyze the key parameters and hyper parameters in a neural network’s architecture

### Deep learning Course Outcomes

 Demonstrate the mathematical foundation of neural network

 Describe the machine learning basics

 Differentiate architecture of deep neural network

 Build a convolutional neural network

 Build and train RNN and LSTMs

### Deep learning Text Books

1) Ian Goodfellow, Yoshua Bengio, Aaron Courville, “Deep Learning”, MIT Press,2016.

2) Josh Patterson and Adam Gibson, “Deep learning: A practitioner’s approach”, O’Reilly Media, First Edition, 2017.

### Deep learning Reference Books

1) Fundamentals of Deep Learning, Designing next-generation machine intelligence algorithms, Nikhil Buduma, O’Reilly, Shroff Publishers, 2019.

2) Deep learning Cook Book, Practical recipes to get started Quickly, Douwe Osinga, O’Reilly, Shroff Publishers, 2019.