Course Description

“Deep Learning A-Z: Hands-on Artificial Neural Networks”. This course a thorough introduction to the fundamentals of deep learning and practical experience in implementing neural networks using Python and popular deep learning libraries.

Module 1: Introduction to Deep Learning

• A Glimpse into the History of Deep Learning
• Unveiling the Real-world Applications of Deep Learning
• Exploring the Lucrative Career Opportunities in Deep Learning

Module 2: Deep Learning Fundamentals

• Demystifying Deep Learning: A Comprehensive Definition
• Unraveling the Distinction between Deep Learning and Machine Learning
• Navigating the Diverse Landscape of Deep Learning
• Delving into the Structure of Neural Networks
• Exploring the Varieties of Neurons
• Activation Functions: Understanding Their Role
• Demystifying Learning Algorithms

Module 3: Building Artificial Neural Networks

• Introduction to TensorFlow, PyTorch, and Keras
• Data Preprocessing for Neural Networks
• Constructing Simple Neural Networks
• Training Neural Networks: Unleashing Their Power
• Evaluating Neural Network Performance

Module 4: Deep Learning Applications

• Image Processing: Recognizing, Classifying, and Generating Images
• Speech Recognition: Converting Text to Speech, Identifying Speakers, and Achieving Seamless Translation
• Natural Language Processing: Analyzing Sentiment, Extracting Text, and Summarizing Documents
• Text Generation: Crafting Articles, Composing Stories, and Unleashing Creativity

Module 5: Real-world Projects Powered by Deep Learning

• Project 1: Facial Recognition
• Project 2: Image Classification
• Project 3: Building a Chatbot
• Project 4: Sentiment Analysis

Certification

Upon successful completion of this course and passing the final exam, you will receive a certificate of completion from GLOBEL GROUP to showcase your newfound skills.

Who This Course is for

This course is meticulously designed to cater to a diverse range of learners, including:

• Beginners in Artificial Intelligence and Deep Learning:
  • University students pursuing AI specializations
  • Professionals seeking career transitions
  • Curious individuals eager to acquire new skills
• Software Developers and Engineers:
  • Aspiring to integrate deep learning expertise into their skillsets
  • Intrigued by building intelligent applications using deep learning
• Researchers:
  • Seeking to apply deep learning techniques to their research endeavors