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Deep neural network python

Track :

Computer Science

Course Presenter :

Learn With Jay

Lessons no : 2

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What will you learn in this course?
  • Design and implement deep neural networks using Python with TensorFlow and PyTorch for real-world applications
  • Apply data preprocessing, normalization, and augmentation techniques to improve deep learning model performance
  • Optimize neural network models through hyperparameter tuning, regularization, and loss function selection
  • Evaluate and troubleshoot deep neural network models for accuracy, efficiency, and deployment readiness

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Lessons | 2


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31 Reviews

Moksha sai

df 2025-10-09

Jeneba Gs

It's very good and interesting 2025-10-05

Olayemi Akeem Yemi

Good 2025-10-03

Muhammad Naseem

good 2025-09-25

Dhanush Kumar. S

Good 2025-09-19

Atharv Yarandole

Good 2025-09-18

J JEBISH

nice 2025-09-09

Aswin M

Overall I really like this class because all lectures,I really enjoyed this class and the format it was presented in, The experience of this class has being nothing but positive. 2025-09-04

shiv a

GOOD 2025-08-21

Lohit kumar Raparthi

Good 2025-08-07

SANDEEP P

GOOD 2025-08-05

khalid usman

good 2025-08-02

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Related Courses

Deep neural network python, in this course we will learn how to build, train, and evaluate deep neural networks using Python and popular libraries like TensorFlow and PyTorch. We’ll begin by understanding the core concepts of neural networks, including layers, activation functions, loss functions, and backpropagation. Then, we’ll dive into building real-world models, from simple feedforward networks to more advanced deep architectures. The course covers data preprocessing, model optimization, regularization techniques, and performance evaluation. You’ll also gain hands-on experience training models on image and text datasets. Through step-by-step coding tutorials and practical examples, you’ll develop the skills needed to create efficient and accurate deep learning models in Python. By the end of this course, you’ll be confident in designing and deploying deep neural networks to solve real-world problems in classification, regression, and beyond. Learn With Jay