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Course overview
The Deep Learning course at Embrizon Technologies provides a comprehensive understanding of neural networks and their advanced applications. It covers essential topics, including computer vision, natural language processing, and real-world projects. This course is designed for those looking to build a strong foundation in deep learning and neural networks to excel in AI-driven careers.
COURSE STRUCTURE
Introduction to Deep Learning
- What is Deep Learning?
- Overview of Neural Networks
- Key Deep Learning Applications
- How Deep Learning Differs from Traditional Machine Learning
- Setting up a Development Environment
Neural Networks Fundamentals
- Structure of Artificial Neurons
- Activation Functions
- Feedforward Neural Networks
- Backpropagation
- Optimization Algorithms
Convolutional Neural Networks (CNNs)
- Introduction to CNNs
- Convolutional Layers
- Pooling Layers
- CNN Architectures (LeNet, AlexNet, etc.)
- CNN Applications in Computer Vision
Recurrent Neural Networks (RNNs)
- Introduction to RNNs
- Sequence Data and Time Series
- Long Short-Term Memory (LSTM) Networks
- Applications of RNNs in Natural Language Processing (NLP)
- Hands-on Project with RNNs
Transfer Learning and Fine-tuning
- Introduction to Transfer Learning
- Pre-trained Models
- Transfer Learning with CNNs
- Fine-tuning Deep Learning Models
- Practical Use Cases
Data Science and Preprocessing
- Introduction to Data Science
- Data Collection Methods
- Data Cleaning Techniques
- Feature Engineering
- Data Visualization
Supervised Learning
- Overview of Supervised Learning
- Linear Regression
- Logistic Regression
- Decision Trees
- Model Evaluation Techniques
Unsupervised Learning
- Introduction to Unsupervised Learning
- Clustering Algorithms
- Dimensionality Reduction
- Autoencoders
- Applications of Unsupervised Learning
Real-World Capstone Projects
- Computer Vision Project using CNNs
- NLP Project using RNNs
- Transfer Learning Project
- Model Evaluation and Optimization
- Presenting the Final Project
Learning Objectives
- Understand the basic concepts of deep learning and neural networks.
- Learn to implement CNNs and RNNs for computer vision and NLP tasks.
- Explore the application of transfer learning and fine-tuning in real-world models.
- Develop skills in data preprocessing, feature engineering, and data visualization.
- Build and evaluate deep learning models with practical use cases.
Learning Methods
- Presentations & Lectures
- Hands-On, Project-Based Learning
- Live Mentorship Sessions
- Self-Paced Learning & Recorded Lectures
Learning Outcome
By the end of the course, learners will have a strong grasp of deep learning fundamentals, including CNNs, RNNs, and transfer learning. They will gain hands-on experience in building neural networks, preprocessing data, and solving real-world challenges using AI models.
How to Enroll Program
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Elevate your skills with hands-on experience, 12+ live projects, and mentorship from industry-leading experts in the top 2% of their field. Our tailored plans cater to your unique needs, allowing you to transform your practical knowledge into real-world expertise.
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Frequently Asked Questions
What is the difference between the Live and Self-Paced courses?
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Live Class:
- Offers real-time interaction with mentors.
- Includes scheduled live sessions where you can ask questions and get instant feedback.
- Provides personalized mentorship through live engagement.
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Self-Paced Class:
- Access pre-recorded video content at your convenience.
- No fixed schedule – you can learn whenever it suits you.
- Get mentor support via pre-scheduled mentoring sessions without the need for live attendance.
Will I receive a certificate after completing the course?
Yes, upon successful completion of the course, you will receive a Certificate for Course Completion.
What type of projects will I be working on?
ou’ll be working on 5+ live projects that are designed to give you hands-on experience and practical knowledge.
How much mentor support is provided in the Self-Paced course?
The Self-Paced course offers 15+ hours of mentor training to help guide you through the course and projects.
How do I enroll in the Self-Paced course?
You can easily enroll in the Self-Paced course by clicking on the Enroll Now button and completing the registration process.
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