Foundations of Deep Learning and Neural Networks
Master neural networks from perceptrons to CNNs. Explore backpropagation, regularization, and TensorFlow/Keras for image analysis and real-world tasks.
Master neural networks from perceptrons to CNNs. Explore backpropagation, regularization, and TensorFlow/Keras for image analysis and real-world tasks.
This comprehensive course provides a solid foundation in deep learning and neural networks, starting with fundamental concepts and progressing to advanced applications. Beginning with the historical context and basic structures of neural networks, including perceptrons and multi-layer architectures, you'll learn how these systems are trained using activation functions and the backpropagation algorithm. The course examines artificial neural networks in detail, drawing parallels to the human brain while exploring essential components like input/output layers and the Sigmoid function. You'll gain hands-on experience with feed-forward networks, sophisticated training methods, and regularization techniques such as dropout and batch normalization. The curriculum culminates with an in-depth study of convolutional neural networks (CNNs) for image and video analysis, covering key operations like convolution, stride, padding, and pooling. Throughout the course, you'll work with industry-standard frameworks including TensorFlow and Keras to implement your learning in practical scenarios.
Instructors:
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What you'll learn
Understand the history and evolution of neural networks and deep learning
Implement perceptrons and multi-layer neural networks from scratch
Master the backpropagation algorithm for training neural networks
Apply various activation functions effectively in different network architectures
Implement regularization techniques like dropout and batch normalization
Design and train convolutional neural networks for image and video analysis
Skills you'll gain
This course includes:
12 Hours PreRecorded video
3 assignments
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
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There are 6 modules in this course
This course provides a comprehensive introduction to deep learning and neural networks, structured in six progressive modules. Beginning with foundational concepts, the course explores the history of neural networks, perceptrons, and multi-layer structures that form the building blocks of deep learning systems. Students learn about various activation functions and the process of training neural networks through diverse representations. The second module delves into artificial neural networks, examining their inspiration from the human brain and exploring key components such as input/output layers and the Sigmoid function. The course continues with feed-forward networks, covering online/offline modes and vectorized methods for optimization. A significant portion is dedicated to backpropagation, breaking down this crucial training algorithm into detailed steps alongside concepts like loss functions and stochastic gradient descent. The fifth module introduces regularization techniques including dropout strategies and batch normalization. The course concludes with an extensive exploration of convolutional neural networks (CNNs), covering their applications in image and video analysis, and implementation details such as convolution operations, stride, padding, and pooling layers.
Course Introduction
Module 1 · 2 Hours to complete
Artificial Neural Networks-Introduction
Module 2 · 2 Hours to complete
ANN - Feed Forward Network
Module 3 · 1 Hours to complete
Backpropagation
Module 4 · 2 Hours to complete
Regularization
Module 5 · 1 Hours to complete
Convolution Neural Networks
Module 6 · 3 Hours to complete
Instructor
Enhancing IT Education Through Expert-Led Learning
Packt Course Instructors are dedicated to delivering high-quality educational content across a wide range of IT topics, offering over 5,000 eBooks and courses designed to improve student outcomes in technology-related fields. With a focus on practical knowledge, instructors leverage their industry expertise to create engaging learning experiences that help students grasp complex concepts and apply them effectively. The courses cover diverse subjects, from programming languages to advanced data analysis, ensuring that learners at all levels can find relevant resources to enhance their skills. Additionally, Packt emphasizes personalized learning paths and provides analytics tools for educators to monitor student engagement and success, making it a valuable partner in academic settings.
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