Deep Learning Coursera -- deeplearning.ai

clean_computation_graph
computation_graph
computation_graph_logression
cost_surface
derivative
food_matrix
gd_no_vectorization
gd_vectorization
gradient_descent
leaky_relu
lr_overview
lr_overview_graph
more_derivatives
m_examples_nn
neural_network_basics
new_notation_nn
nn_overview
nn_overview_graph
putting_it_all_together_new_notation
relu_deriv
scale
sigmoid
sigmoid_deriv
supervised_learning
super_simple_network
tanh
tanh_deriv
unit_breakdown
unstructured_vs_structured_data
vectorized_activations
vectorized_a_nn
vectorized_z_nn
WX_vector
deep_representations
simple_deep_nn_2
chosing_f1_score
speed_v_angle_orth
using_a_single_number
two_metrics_optimize
forward_backward
ai_progress_over_time
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error_analysis_table
train-dev-set
multi-task
transfer_learning
step_by_step_vs_end_to_end
general_error_formation