| Course Name | Duration | Classes | Total Fee | Mode of Training | Class Timing |
|---|---|---|---|---|---|
|
Deep Learning
Course |
3 Months |
60 |
60,000 PKR |
Online & Face-to-Face |
To be decided mutually with students |
Deep Learning Training Certification Course
- Deep learning teaches how computers learn from data. It uses brain-like networks called neural networks. It helps machines make smart decisions.
- You will learn how data is given to computers. Computers find patterns by themselves. It makes learning automatic and fast.
- Deep learning is part of Artificial Intelligence. It helps in image, speech, and text understanding. It works like the human brain learns.
- You will study layers of neural networks. Each layer learns something new. Together, they solve big problems.
- The course shows how to train models. You will use data to make models smarter. Practice improves the model’s accuracy.
- You will learn tools like TensorFlow and PyTorch. These tools help in building AI models easily. They make learning fun and practical.
- Deep learning is used in real life. It helps in self-driving cars and robots. It makes technology more powerful and smart.
Advantages of Deep Learning
- Deep learning helps computers learn from data automatically. It improves with more practice and examples. This makes machines smarter with time.
- It builds strong skills for AI and automation. You can create systems that think like humans. It’s a future-ready skill.
- It improves image and voice recognition. Useful in apps like Google Photos or Alexa. You learn how they actually work.
- It helps in solving complex problems easily. Like predicting diseases or weather. Smart models make work faster.
- It increases job opportunities in tech fields. Big companies need deep learning experts. It can grow your career quickly.
- It enhances data analysis and decision making. You can find hidden patterns in data. It makes smarter business choices.
- It boosts creativity and innovation. You can make AI art, games, or chatbots. Learning it makes you a tech creator.
Deep Learning Training Certification Course Outline
Deep Learning is a part of Artificial Intelligence that helps computers learn from data. It uses special computer models called neural networks to think like the human brain. This course teaches how machines can recognize images, speech, and patterns. Students will learn simple ways to build and train deep learning models step by step.
Module 1: Introduction to Deep Learning
Understand what deep learning means.
Learn how it mimics the human brain.
Know the difference between AI, ML, and DL.
Explore real-life uses like speech and image recognition.
Module 2: Neural Networks Basics
Learn about neurons and layers.
Understand weights, bias, and activation.
Know how information flows through a network.
Build your first simple neural model.
Module 3: Data Preparation and Processing
Learn how to collect and clean data.
Understand normalization and scaling.
Split data into training and testing parts.
Prepare data for deep learning models.
Module 4: Building Deep Learning Models
Learn to build models using TensorFlow or PyTorch.
Understand model layers, epochs, and parameters.
Train your first model step by step.
Test the model and check accuracy.
Module 5: Convolutional Neural Networks (CNNs)
Understand how CNNs process images.
Learn about filters, pooling, and feature maps.
Build a simple image classifier.
Recognize objects like cats or cars from pictures.
Module 6: Recurrent Neural Networks (RNNs)
Learn how RNNs handle time-based data.
Understand sequences and memory in models.
Use RNNs for text or speech prediction.
Create a basic text generation project.
Module 7: Optimization & Evaluation
Learn about loss functions and optimizers.
Understand overfitting and underfitting.
Improve accuracy using tuning and dropout.
Evaluate models using test data.
Module 8: Advanced Topics in Deep Learning
Explore autoencoders, GANs, and transfer learning.
Understand how deep learning powers AI tools.
Learn real-world applications in health, art, and robotics.
Practice with mini advanced projects.
Final Module : Certification & Final Project
Complete a hands-on final project.
Review all modules and submit your work.
Get assessed on performance and creativity.
Receive your official Deep Learning Certificate.
Experience and Inspiring Trainers:
Real-time Practice and Projects:
Prerequisites for Deep Learning Course:
- You should know how to use a computer. Simple skills like saving files and using software are helpful. It makes learning easier and faster.
- Know basic math like algebra and probability. Math helps to understand how models work. Don’t worry, only simple math is needed.
- Python is the main language for deep learning. You should know how to write small programs. Practice using simple Python examples.
- Learn what is data, model, and training. Understand basic ML terms like accuracy and prediction. It helps to move easily into deep learning.
- Be able to solve problems step by step. Think logically when you face errors. This helps in debugging and improving models.
- Be curious about how computers learn. Enjoy exploring data and making smart systems. Interest keeps you motivated to learn more.
- Know about mean, median, and variance. These are useful for understanding datasets. Simple statistical ideas go a long way.
- No prior experience in Deep Learning is required, but having these basics will help in grasping concepts faster.
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