JNTUK R20 B.Tech CSE 4-1 Deep Learning Techniques Material/ Notes PDF Download: Looking for study JNTUK R20 B.Tech CSE 4-1 Deep Learning Techniques Material. You’ve found the perfect resource! Our material covers all aspects of deep learning methods simply and comprehensively. Learn how to work with sequential data, including deep recurrent and memory networks. Explore advanced concepts like deep Turing machines and understand how to apply these techniques to solve various learning problems effectively.
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JNTUK R20 B.Tech CSE 4-1 Deep Learning Techniques Material – Units
| No. Of Units | Name of the Unit |
| Unit – 1 | Fundamentals of Deep Learning, Fundamentals of Machine Learning |
| Unit – 2 | Introducing Deep Learning |
| Unit – 3 | Neural Network |
| Unit – 4 | Convolutional Neural Networks, Recurrent Neural Networks |
| Unit – 5 | Interactive Applications of Deep Learning, Deep Learning Research |
Unit 1 Syllabus PDF Download | JNTUK R20 B.Tech CSE Deep Learning Material
Fundamentals of Deep Learning: Artificial Intelligence, History of Machine learning: Probabilistic Modeling, Early Neural Networks, Kernel Methods, Decision Trees, Random forests and Gradient Boosting Machines,
Fundamentals of Machine Learning: Four Branches of Machine Learning, Evaluating Machine Learning Models, Overfitting and Underfitting.
| JNTUK R20 B.Tech CSE 4-1 Deep Learning Techniques Material – PDF Download | |
| To Download The JNTUK R20 B.Tech CSE 4-1 Deep Learning Techniques Unit 1 Notes PDF | Download PDF |
Unit 2 Syllabus PDF Download | JNTUK R20 B.Tech CSE Deep Learning Material
Introducing Deep Learning: Biological and Machine Vision, Human and Machine Language, Artificial Neural Networks, Training Deep Networks, and Improving Deep Networks.
| JNTUK R20 B.Tech CSE 4-1 Deep Learning Techniques Material – PDF Download | |
| To Download The JNTUK R20 B.Tech CSE 4-1 Deep Learning Techniques Unit 2 Notes PDF | Download PDF |
Unit 3 Syllabus PDF Download | JNTUK R20 B.Tech CSE Deep Learning Material
Neural Networks: Anatomy of Neural Network, Introduction to Keras: Keras, TensorFlow, Theano and CNTK, Setting up Deep Learning Workstation, Classifying Movie Reviews: Binary Classification,
Classifying newswires: Multiclass Classification.
| JNTUK R20 B.Tech CSE 4-1 Deep Learning Techniques Material – PDF Download | |
| To Download The JNTUK R20 B.Tech CSE 4-1 Deep Learning Techniques Unit 3 Notes PDF | Download PDF |
Unit 4 Syllabus PDF Download | JNTUK R20 B.Tech CSE Deep Learning Material
Convolutional Neural Networks: Neural Network and Representation Learning, Convolutional Layers, Multichannel Convolution Operation,
Recurrent Neural Networks: Introduction to RNN, RNN Code,
PyTorch Tensors: Deep Learning with PyTorch, CNN in PyTorch.
| JNTUK R20 B.Tech CSE 4-1 Deep Learning Techniques Material – PDF Download | |
| To Download The JNTUK R20 B.Tech CSE 4-1 Deep Learning Techniques Unit 4 Notes PDF | Download PDF |
Unit 5 Syllabus PDF Download | JNTUK R20 B.Tech CSE Deep Learning Material
Interactive Applications of Deep Learning: Machine Vision, Natural Language Processing, Generative Adversarial Networks, Deep Reinforcement Learning.
Deep Learning Research: Autoencoders, Deep Generative Models: Boltzmann Machines Restricted Boltzmann Machines, Deep Belief Networks.
| JNTUK R20 B.Tech CSE 4-1 Deep Learning Techniques Material – PDF Download | |
| To Download The JNTUK R20 B.Tech CSE 4-1 Deep Learning Techniques Unit 5 Notes PDF | Download PDF |
JNTUK R20 B.Tech Deep Learning Techniques Material – Outcomes
- Learn the basics of Artificial Intelligence, Machine Learning, and Deep Learning, focusing on fundamental concepts and learning techniques.
- Explore Neural Network training methods and understand various random models used in the process.
- Gain insights into the techniques of popular Deep Learning frameworks such as Keras, TensorFlow, Theano, and CNTK.
- Classify the concepts of Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) to understand their respective applications.
- Implement interactive applications using Deep Learning techniques to gain practical experience and understanding of their real-world usage.
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