Deep Learning - Artificial Neural Networks with TensorFlow
Master TensorFlow 2 for deep learning and neural networks. Learn to build, train, and optimize ANNs for real-world applications like image classification.
Master TensorFlow 2 for deep learning and neural networks. Learn to build, train, and optimize ANNs for real-world applications like image classification.
This comprehensive course on deep learning with TensorFlow 2 provides a thorough understanding of artificial neural networks (ANNs) and their implementation. Starting with foundational machine learning concepts, students progress through linear classification, regression, and neural network fundamentals. The course covers essential topics including forward propagation, activation functions, and multiclass classification, with practical applications using the MNIST dataset. Students learn to optimize models using various gradient descent techniques and loss functions, including Adam optimization. The curriculum emphasizes hands-on experience with real-world problems, making it ideal for those looking to master deep learning implementation with TensorFlow.
Instructors:
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What you'll learn
Implement artificial neural networks using TensorFlow 2
Apply deep learning techniques to real-world problems like image classification
Master various optimization algorithms and loss functions
Understand forward propagation and activation functions
Create and optimize multiclass classification models
Develop proficiency in model evaluation and hyperparameter tuning
Skills you'll gain
This course includes:
285 Minutes PreRecorded video
5 assignments
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
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There are 5 modules in this course
This course provides a thorough introduction to deep learning and artificial neural networks using TensorFlow 2. Students learn foundational machine learning concepts before diving into neural network implementation. The curriculum covers forward propagation, activation functions, and practical applications in image classification and regression. Advanced topics include loss function optimization and gradient descent techniques. The course balances theoretical understanding with hands-on implementation, preparing students for real-world deep learning applications.
Welcome
Module 1 · 7 Minutes to complete
Machine Learning and Neurons
Module 2 · 1 Hours to complete
Feedforward Artificial Neural Networks
Module 3 · 1 Hours to complete
In-Depth: Loss Functions
Module 4 · 38 Minutes to complete
In-Depth: Gradient Descent
Module 5 · 2 Hours to complete
Fee Structure
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Enhancing IT Education Through Expert-Led Learning
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