Deep Learning and Artificial Neural Networks (ANN) Mastery
in Deep Learning & Machine LearningWhat you will learn?
Build and optimize deep learning models using TensorFlow/Keras
Implement CNN architectures for image classification and object detection
Apply autoencoding and regularization techniques for better performance
Use pre-trained models for transfer learning in AI applications
About this course
🔹 About This Course
• A comprehensive self-paced course covering Artificial Neural Networks (ANN) and Deep Learning.
• Focuses on model building, optimization, CNNs, and transfer learning.
• Hands-on projects and real-world applications to enhance practical knowledge.
🔹 Course Overview
• Learn the fundamentals of deep learning, model optimization, and advanced architectures.
• Master CNNs, hyperparameter tuning, and transfer learning techniques.
• Industry-relevant applications for AI, computer vision, and automation.
🔹 Course Curriculum & Modules
✅ Week 1: Introduction to ANN & Deep Learning
✅ Week 2: Neural Networks, Hyperparameters & Model Building
✅ Week 3: Optimizers, Regularization & Autoencoding
✅ Week 4: Deep Learning & CNNs
✅ Week 5: CNN Architecture & Transfer Learning
🔹 What You Will Learn
✔️ Build and optimize deep learning models using TensorFlow/Keras
✔️ Implement CNN architectures for image classification and object detection
✔️ Apply autoencoding and regularization techniques for better performance
✔️ Use pre-trained models for transfer learning in AI applications
🔹 Learning Objectives
🎯 Understand the fundamentals of ANN and deep learning
🎯 Develop expertise in CNN architecture and its applications
🎯 Gain hands-on experience with model tuning and optimization
🎯 Learn real-world AI solutions through practical projects
🔹 Course Features & Benefits
✅ Self-paced learning with lifetime access to content
✅ Hands-on projects and real-world case studies
✅ Expert-led guidance and practical assignments
✅ Certification upon successful completion
🔹 Who This Course is For
🎯 AI & ML Enthusiasts
🎯 Data Scientists & Analysts
🎯 Software Engineers & Developers
🎯 Anyone interested in Deep Learning & Neural Networks
🔹 Skills Covered
✔️ Artificial Neural Networks (ANN)
✔️ Deep Learning Model Building & Optimization
✔️ Convolutional Neural Networks (CNN)
✔️ Hyperparameter Tuning & Regularization
✔️ Transfer Learning & Autoencoders
🔹 Special Benefits to Students for Enrolling Now
🚀 Exclusive hands-on projects to boost your portfolio
🚀 Lifetime access to updated course content
🚀 Internship & job assistance support
🔹 Exclusive Complimentary Benefit: One-on-One Expert Interaction
📢 Live Q&A and doubt-clearing sessions with deep learning experts
🔹 Course Certificate Advantage
🏅 Industry-recognized certification to showcase your expertise
🏅 Enhances job opportunities in AI and ML domains
🔹 Books & References
📚 "Deep Learning" by Ian Goodfellow
📚 "Neural Networks and Deep Learning" by Michael Nielsen
📚 Official TensorFlow & PyTorch Documentation
🔹 Top Indian Companies Hiring with These Certifications and Skillsets
🏢 TCS, Infosys, Wipro, Accenture, HCL, Capgemini, Amazon, Flipkart, Reliance, Cognizant
This course is designed to equip learners with the essential skills and knowledge required to build and optimize deep learning models for real-world applications. 🚀
Suggested by top companies
Top companies suggest this course to their employees and staff.
Requirements
Basic Technology and Internet Access - A stable internet connection and the ability to use online learning platforms are essential. Students should be comfortable with navigating the course platform, accessing videos, downloading materials, and completing assessments
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Comments (0)
This course offers a comprehensive understanding of Artificial Neural Networks, Convolutional Neural Networks, and Deep Learning. It starts with a deep dive into the fundamental principles of Artificial Neural Networks, including neuron operations and problem-solving applications. Following this, the course explores Convolutional Neural Networks, emphasizing their use in computer vision, explaining convolutional and pooling layers, and their impact on image classification.
Objectives:
Understand the fundamental principles of Artificial Neural Networks (ANNs) and their applications in solving complex problems.
Apply ANNs to solve complex problems, utilizing appropriate frameworks and programming languages, such as TensorFlow or PyTorch, to build and train neural networks.
Demonstrate the acquired knowledge and skills to develop practical solutions using ANNs and CNNsfor complex problems in diverse domains.
Implement and evaluate the performance of CNNs in image recognition, object detection, and other computer vision applications.
Analyze the architectural components of Convolutional Neural Networks (CNNs) and their role in computer vision tasks.
Construct cutting-edge solutions by applying Artificial Neural Networks, Convolutional Neural Networks to solve complex real-world problems across diverse domains."
