Învățarea profundă este un subset de învățare automată, iar învățarea automată este un subset de AI, care este un termen umbrelă pentru orice program de calculator care face ceva inteligent.
– Developers
– Recapitulare din cursul anterior – Machine Learning de bază
– Fundamente de imagine
– Rețele neuronale convoluționale
– Transfer de învățare
– Localizare obiect
– Implementarea Machine Learning
Recap from previous course – Basic ML
– Machine Learning
– Neural Networks
– Deep Learning
Image Fundamentals
– Pixels
– Image Channels and channel ordering
– Scale
– OpenCV library
– Reading, writing images
– Opening/writing a video stream (Webcam, etc)
Convolutional Neural Networks
– Recap
– Batch Normalization
– Drop out
– Saving, loading a model
– Restarting training
– TensorBoard
Transfer Learning
– Popular datasests (ImageNet, etc)
– Existing CNNs for Image Classification (Inception, ResNet, etc)
– Freezing layers
– Transfer Learning on our own classification problem
Object Localization
– Problem definition
– Popular datasets (Coco, etc)
– Existing frameworks (YOLO, etc)
Deploying ML
– Protobuf, flatbuffers, JSON, gRPC
– Remote Procedure Call
– TensorFlow RESTful API
Artificial Neural Networks, Machine Learning, Deep Thinking Advanced
