Aim

This course is designed to introduce participants to the fundamental concepts of deep learning, including its core principles, algorithms, and applications. The program covers the building blocks of deep learning, such as neural networks, activation functions, and backpropagation. By the end of the course, participants will be equipped with the knowledge and skills to implement deep learning models for real-world problems in areas such as computer vision, natural language processing, and robotics.

Program Objectives

Understand the core principles of deep learning and its relationship with other machine learning techniques.
Learn about neural networks, including their structure, components, and how they learn through backpropagation.
Explore key deep learning models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs).
Gain hands-on experience in building and training deep learning models using popular frameworks like TensorFlow and Keras.
Apply deep learning techniques to solve practical problems in areas like image classification, speech recognition, and text analysis.

Program Structure

Module 1: Introduction to Deep Learning

Overview of classical computing vs quantum computing.
Key concepts in quantum mechanics: superposition, entanglement, and quantum interference.
Applications of quantum computing in solving intractable problems for classical computers.

Module 2: Neural Networks and Backpropagation

Understanding the structure of neural networks: Layers, weights, biases, and activation functions.
The backpropagation algorithm: How neural networks learn and adjust weights to minimize error.
Gradient descent and optimization: The role of stochastic gradient descent (SGD) and other optimization techniques in training models.

Module 3: Deep Learning Models

Convolutional Neural Networks (CNNs): Key components, applications in image processing, and how they differ from traditional neural networks.
Recurrent Neural Networks (RNNs): Understanding how RNNs are designed for sequence data, such as time series and text.
Generative Adversarial Networks (GANs): Introduction to GANs, their architecture, and applications in generating synthetic data and images.

Module 4: Building Neural Networks with TensorFlow and Keras

Introduction to TensorFlow and Keras: Popular frameworks for building and training deep learning models.
How to build, compile, and train a neural network using Keras for simple tasks like image classification.
Hands-on examples and projects to implement neural networks using TensorFlow and Keras.

Module 5: Advanced Deep Learning Architectures

Transfer learning: Leveraging pre-trained models for faster training on new tasks and datasets.
Autoencoders: Using autoencoders for unsupervised learning and dimensionality reduction.
Reinforcement learning: Introduction to reinforcement learning and its applications in deep learning.

Module 6: Applications of Deep Learning

Practical applications of deep learning: image classification, object detection, speech recognition, and natural language processing.
Hands-on projects: Implement deep learning models to classify images, recognize speech, and generate text.
Case studies of deep learning applications in various industries, including healthcare, autonomous vehicles, and finance.

Module 7: Evaluation and Tuning of Deep Learning Models

How to evaluate the performance of deep learning models using metrics like accuracy, precision, recall, and F1-score.
Model tuning techniques: hyperparameter tuning, regularization, and cross-validation to improve model performance.
Handling overfitting and underfitting: Techniques like dropout, data augmentation, and early stopping.

Final Project

Design a deep learning model to solve a real-world problem, such as image classification, sentiment analysis, or speech-to-text conversion.
Train the model using a dataset, tune the model’s hyperparameters, and evaluate its performance.
Prepare a final report that includes the model’s design, performance, and future improvements.

Participant Eligibility

Students and professionals in Computer Science, Engineering, Data Science, and Mathematics.
Researchers and practitioners interested in implementing deep learning techniques for real-world applications.
Anyone interested in gaining a comprehensive understanding of deep learning and its applications in AI.

Program Outcomes

Comprehensive understanding of the core principles of deep learning and neural networks.
Hands-on experience in implementing deep learning models using popular frameworks like TensorFlow and Keras.
Proficiency in applying deep learning techniques to solve problems in image processing, speech recognition, and natural language processing.
Skills in model evaluation, tuning, and optimization for real-world deep learning applications.

Program Deliverables

Access to e-LMS: Full access to course materials, resources, and tools.
Hands-on Project Work: Practical assignments on building, training, and optimizing deep learning models.
Research Paper Publication: Opportunities to publish research findings in relevant journals.
Final Examination: Certification awarded after completing the exam and final project.
e-Certification and e-Marksheet: Digital credentials provided upon successful completion.

Future Career Prospects

Deep Learning Researcher
Machine Learning Engineer
AI Specialist
Data Scientist
Computer Vision Engineer

Job Opportunities

Tech Companies: Developing deep learning models for applications in AI, computer vision, and natural language processing.
Healthcare Firms: Using deep learning for medical image analysis, disease prediction, and personalized treatment plans.
Autonomous Vehicle Companies: Developing deep learning models for object detection and navigation in self-driving cars.
Finance Companies: Applying deep learning for fraud detection, algorithmic trading, and customer segmentation.

Category	E-LMS, E-LMS+Video, E-LMS+Video+Live Lectures

Reviews

There are no reviews yet.

Be the first to review “Deep Learning Fundamentals Course”

Achieve Excellence & Enter the Hall of Fame!

Elevate your research to the next level! Get your groundbreaking work considered for publication in prestigious Open Access Journal (worth USD 1,000) and Opportunity to join esteemed Centre of Excellence. Network with industry leaders, access ongoing learning opportunities, and potentially earn a place in our coveted

Hall of Fame.

Achieve excellence and solidify your reputation among the elite!

AI

Deep Learning Fundamentals Course

Aim

Program Objectives