deep

learning

what you will learn

01/

INTRO TO DEEP LEARNING CONCEPTS

  • INTRO TO DEEP LEARNING

  • COMPUTATION GRAPH

02/

INTRO TO PYTORCH

  • CODING WITH PYTORCH

03/

BASIC NEURAL NETWORKS

  • NEURAL NETWORK REPRESENTATION

  • ACTIVATION FUNCTIONS 

  • DERIVATIVES

  • GRADIENT DESCENT

  • BACK PROPAGATION

  • RANDOM INITIALIZATION

04/

DEEP NEURAL NETWORKS

  • DEEP NEURAL NETWORKS

  • FORWARD PROPAGATION

  • BUILDING BLOCKS OF NEURAL NETWORK FORWARD AND BACKWARD PROPAGATION

  • PARAMETERS VS HYPER PARAMETERS  REGULARIZATION

  • DROPOUT

05/

TESTING YOUR NEURAL NETWORKS

  • APPLIED DEEP LEARNING  NORMALIZATION

  • WEIGHT INITIALIZATION

  • NUMERICAL APPROXIMATION  GRADIENT CHECKING

  • OPTIMIZATION ALGORITHMS

06/

OPTIMIZATION ALGOS

  • GRADIENT DESCENT

  •  WEIGHTED AND AVERAGE GRADIENT DESCENT

  • MOMENTUM

  •  RMSPROP

  • ADAM

  •  LEARNING RATE DECAY
  • HYPERPARAMETER TUNING
  • BATCH NORMALIZATION

  • SOFTMAX REGRESSION CLASSIFIER

07/

CONVOLUTION NEURAL NETWORKS (CNN)

  • FOUNDATION OF CNN 

  • PADDING 

  • CONVOLUTIONS 

  • POOLING 

  • EXAMPLES

08/

FAMOUS NEURAL NETWORK CNN ARCHITECTURES

  • ALEXNET 

  • RESNET 

  • MOBILENET 

  • U-NET 

  • INCEPTION

09/

APPLICATIONS OF CNN

  • TRANSFER LEARNING 

  • DATA AUGMENTATION 

  • OBJECT LOCALIZATION 

  • LANDMARK DETECTION 

  • OBJECT DETECTION 

  • BOUNDING BOX PREDICTION 

  • ANCHOR BOX 

  • YOLO 

  • FACE RECOGNITION 

  • NEURAL STYLE TRANSFER

10/

SEQUENCE MODELS IN DEEP LEARNING

  • SEQUENCE MODELS 

  • NOTATION 

  • RECURRENT NEURAL NETWORKS 

  • BACK PROPAGATION WITH TIME 

  • PROBLEMS WITH RNN 

  • LSTM NETWORKS 

  • GRU NETWORKS 

  • TRANSFORMER NETWORKS 

  • BEAM SEARCH 

  • TRIGGER WORD DETECTION 

  • SPEECH RECOGNITION 

  • BERT MODEL

11/

DEEP NEURAL NETWORKS

  • DEEP NEURAL NETWORKS

  • FORWARD PROPAGATION 

  • BUILDING BLOCKS OF NEURAL NETWORK 

  • FORWARD AND BACKWARD 

  • PROPAGATION 

  • PARAMETERS VS HYPER PARAMETERS 

  • REGULARIZATION 

  • DROPOUT