Dear Community, We are proud to announce the academia program for student : https://haibal.com/academia/ The HAIBAL project is structured in the same way as Keras. The project consists of more than 3000 VIs including, all is coded in LabVIEW native: 16 activations (ELU, Exponential, GELU, HardSigmoid, LeakyReLU, Linear, PReLU, ReLU, SELU, Sigmoid, SoftMax, SoftPlus, SoftSign, Swish, TanH, ThresholdedReLU), nonlinear mathematical function generally placed after each layer having weights. 84 functional layers/layers (Dense, Conv, MaxPool, RNN, Dropout, etc…). 14 loss functions (BinaryCrossentropy, BinaryCrossentropyWithLogits, Crossentropy, CrossentropyWithLogits, Hinge, Huber, KLDivergence, LogCosH, MeanAbsoluteError, MeanAbsolutePercentage, MeanSquare, MeanSquareLog, Poisson, SquaredHinge), function evaluating the prediction in relation to the target. 15 initialization functions (Constant, GlorotNormal, GlorotUniform, HeNormal, HeUniform, Identity, LeCunNormal, LeCunUniform, Ones, Orthogonal, RandomNormal, Random,Uniform, TruncatedNormal, VarianceScaling, Zeros), function initializing the weights. 7 Optimizers (Adagrad, Adam, Inertia, Nadam, Nesterov, RMSProp, SGD), function to update the weights. Get started full playlist available on our 𝐲𝐨𝐮𝐭𝐮𝐛𝐞 𝐜𝐡𝐚𝐧𝐧𝐞𝐥https://lnkd.in/eCjWmQZA Get started with HAIBAL deep learning toolkit (𝐰𝐞𝐛𝐬𝐢𝐭𝐞 𝐝𝐨𝐜𝐮𝐦𝐞𝐧𝐭𝐢𝐨𝐧)https://lnkd.in/e6cPn4Fq#deeplearning #automation #LabVIEW #Graiphic #NI #NationalInstruments

LabVIEW

Dear Community,

We are proud to announce the academia program for student : https://haibal.com/academia/

The HAIBAL project is structured in the same way as Keras.

The project consists of more than 3000 VIs including, all is coded in LabVIEW native:

16 activations (ELU, Exponential, GELU, HardSigmoid, LeakyReLU, Linear, PReLU, ReLU, SELU, Sigmoid, SoftMax, SoftPlus, SoftSign, Swish, TanH, ThresholdedReLU), nonlinear mathematical function generally placed after each layer having weights.
84 functional layers/layers (Dense, Conv, MaxPool, RNN, Dropout, etc…).
14 loss functions (BinaryCrossentropy, BinaryCrossentropyWithLogits, Crossentropy, CrossentropyWithLogits, Hinge, Huber, KLDivergence, LogCosH, MeanAbsoluteError, MeanAbsolutePercentage, MeanSquare, MeanSquareLog, Poisson, SquaredHinge), function evaluating the prediction in relation to the target.
15 initialization functions (Constant, GlorotNormal, GlorotUniform, HeNormal, HeUniform, Identity, LeCunNormal, LeCunUniform, Ones, Orthogonal, RandomNormal, Random,Uniform, TruncatedNormal, VarianceScaling, Zeros), function initializing the weights.
7 Optimizers (Adagrad, Adam, Inertia, Nadam, Nesterov, RMSProp, SGD), function to update the weights.