Deep learning

Basics of Machine Learning

Reinforcement Learning

• Evaluation Metrics

• Overfitting and Underfitting
• Supervised Learning
• Unsupervised Learning
• SLearningemi-Supervised

Introduction to Neural Networks

• Basics of Neural Networks
• Activation Functions
• Feedforward Neural Networks
• CNN (Convolutional Neural Network)
• RNN (Recurrent Neural Network)
• Optimizers

Deep Learning Frameworks

• TensorFlow
• PyTorch
• Keras
• Caffe
• Theano
• MXNet

Convolutional Neural Networks (CNNs)

• Training RNNs
• Practical Applications of RNNs
• Issues in Training RNNs
• Basics of RNNs
• Long Short Term Memory (LSTM)
• Gated Recurrent Units (GRU)

Generative Adversarial Networks (GANs)

• Applications of GANs
• Challenges in Training GANs
• Recent Advancements in GANs
• Basics of GANs
• Architecture of GANs
• Architecture of GANs

Reinforcement Learning

• Deep Q-Learning
• Applications of Reinforcement Learning
• Issues and Challenges
• Basics of Reinforcement Learning
• Policy Gradient Methods
• Q-Learning

Transfer Learning

• Basics of Transfer Learning
• Fine-tuning Pretrained Models
• Applications of Transfer Learning
• Advantages and Limitations

Natural Language Processing (NLP)

• Basics of NLP
• Sequence Models
• Attention Mechanism
• Transformer Models
• BERT and GPT

Computer Vision

• Basics of Computer Vision
• Object Detection
• Image Segmentation
• Face Recognition
• Autonomous Vehicles

Speech Recognition

• Basics of Speech Recognition
• Language Modeling
• Acoustic Modeling
• DeepSpeech

Advanced Topics

• Autoencoders
• Self-Supervised Learning
• Multi-task Learning
• Meta-learning
• Explainable AI
• Neural Architecture Search

 

مهارت در دوره پایتون