Deep Learning Tutorial
Deep Learning is one of the most important technologies in the modern world.
What You Will Learn
- Learn the basics of Deep Learning
- Understand core concepts and syntax
- Build practical projects
- Best practices and modern techniques
Course Content
INTRODUCTION TO DEEP LEARNING
Chapter 1
→
NEURAL NETWORK
Chapter 2
→
FUNDAMENTALS OF TRAINING
Chapter 3
→
IMPORTANT ACTIVATION FUNCTIONS
Chapter 4
→
ESSENTIAL PYTHON & LIBRARIES
Chapter 5
→
BUILDING YOUR FIRST NEURAL NETWORKS
Chapter 6
→
EXERCISES WITH PYTHON CODE
Chapter 7
→
CONVOLUTIONAL NEURAL NETWORKS (CNNs)
Chapter 8
→
RECURRENT NEURAL NETWORKS (RNNs)
Chapter 9
→
OPTIMIZATION TECHNIQUES
Chapter 10
→
DATA PREPROCESSING & AUGMENTATION
Chapter 11
→
AUTOENCODERS
Chapter 12
→
TRANSFER LEARNING
Chapter 13
→
SEQUENCE MODELS FOR NLP
Chapter 14
→
DEPLOYMENT BASICS
Chapter 15
→
TRANSFORMERS
Chapter 16
→
GENERATIVE MODELS
Chapter 17
→
REINFORCEMENT LEARNING
Chapter 18
→
GRAPH NEURAL NETWORKS (GNNs)
Chapter 19
→
ADVANCED CNN
Chapter 20
→
ADVANCED NLP
Chapter 21
→
TRANSFORMERS IN DEEP LEARNING
Chapter 22
→
GENERATIVE MODELS
Chapter 23
→
GRAPH NEURAL NETWORKS (GNNs)
Chapter 24
→
ADVANCED CNN
Chapter 25
→
ADVANCED NLP
Chapter 26
→
SELF-SUPERVISED LEARNING
Chapter 27
→
MODEL OPTIMIZATION & SCALING
Chapter 28
→
EXPLAINABLE AI (XAI)
Chapter 29
→
MULTIMODAL DEEP LEARNING
Chapter 30
→