Certified Training   

✅ Fundamentals of Deep Learning (28h)/(42h)   

• Introduction to Python programming
• Introduction to Deep Learning & Neural Networks
• • Supervised vs. Unsupervised Learning
  • Introduction to Perceptrons, Activation Functions
• Mathematical Foundations
• • Linear Algebra
  • Probability
  • Calculus for Deep Learning
  • Backpropagation
  • Optimization (SGD, Adam, RMSProp)

✅  Deep Neural Networks & Optimization  ​ (28h)/(42h)

• Building Deeper Architectures
• • Batch Normalization
  • Dropout
  • Regularization Techniques
• Hyperparameter Tuning & Model Optimization
• • Learning Rate Scheduling,
  • Weight Initialization
  • Optimization Tools
  • Weights & Biases
• Capstone project: DNN Application (28h)/(42h)

✅Convolutional Neural Networks (CNNs) & Computer Vision (28h)/(42h)

• Convolutional Layers, 
• Pooling
• Feature Extraction
• Advanced Architectures (ResNet, EfficientNet, Vision Transformers)
• Image Processing & Object Detection
• • YOLO
  • Faster R-CNN, 
  • Semantic Segmentation (U-Net)
• Capstone Project: Computer Vision Application (28h)/(42h)
• • Facial Recognition, Medical Image Analysis, Autonomous Driving (28h)/(42h)

✅Natural Language Processing (NLP) & Transformers (28h)/(42h)

• Introduction to NLP & RNNs
• Sequence Models: LSTMs, GRUs, Attention Mechanism
• NLP Models (Transformer Architecture: BERT, GPT, T5, LLaMA)
• Text Generation (Fine-tuning Pretrained Models)
• Capstone project: NLP Application (28h)/(42h)

✅  Generative AI & Advanced Deep Learning ​ (28h)/(42h)

• Generative Adversarial Networks (GANs)
• GANs and Diffusion Models
• Text-to-Image Generation
• Image-to-Image Generation
• Capstone project: Generative AI Application (28h)/(42h)

✅  Deep Reinforcement Learning (DRL) (28h)/(42h)

• Introduction to Deep Q-Networks (DQN)
• Experience Replay
• Target Network
• Deep Q-Learning, Policy Gradient Methods
• Capstone project: DRL in Real-World Applications (28h)/(42h)
• DRL in robotics, DRL in finance and trading, DRL in recommendation systems

✅ Reinforcement Learning & AI Agents  (28h)/(42h)  

• Components of RL: Agent, Environment, States, Actions, Rewards
• Differences between RL, Supervised, and Unsupervised Learning
• Model-Free Learning: Temporal Difference (TD) Learning
• TD(0) Algorithm
• SARSA (State-Action-Reward-State-Action)
• Q-Learning
• Difference between On-Policy and Off-Policy Learning




• Capstone project: RL in Real-World Applications (28h)/(42h)

​RL in robotics; RL in finance and trading; RL in recommendation systems