Less computationally expensive operation compared to Sigmoid/Tanh exponentials. Accelerates convergence \(\rightarrow\) train faster. If output always positive \(\rightarrow\) gradients always positive or negative \(\rightarrow\) bad for gradient updates. Solution: Have to carefully initialize weights to prevent this. Pytorch nn sequential call update#
No signal to update weights \(\rightarrow\) cannot learn.Activation saturates at 0 or 1 with gradients \(\approx\) 0.Large positive number \(\rightarrow\) 1.
Large negative number \(\rightarrow\) 0. Function: takes a number & perform mathematical operation. LogisticRegressionModel ( ( linear ): Linear ( in_features = 784, out_features = 10, bias = True ) ) Logistic Regression Problems ¶ NVIDIA Inception Partner Status, Singapore, May 2017 NVIDIA Self Driving Cars & Healthcare Talk, Singapore, June 2017 NUS-MIT-NUHS NVIDIA Image Recognition Workshop, Singapore, July 2018 Recap of Facebook PyTorch Developer Conference, San Francisco, September 2018įacebook PyTorch Developer Conference, San Francisco, September 2018 NExT++ AI in Healthcare and Finance, Nanjing, November 2018 IT Youth Leader of The Year 2019, March 2019ĪMMI (AIMS) supported by Facebook and Google, November 2018 Oral Presentation for AI for Social Good Workshop ICML, June 2019 Markov Decision Processes (MDP) and Bellman Equationsįractional Differencing with GPU (GFD), DBS and NVIDIA, September 2019ĭeep Learning Introduction, Defence and Science Technology Agency (DSTA) and NVIDIA, June 2019 Supervised Learning to Reinforcement Learning (RL) Weight Initialization and Activation Functions 
Long Short Term Memory Neural Networks (LSTM)įully-connected Overcomplete Autoencoder (AE)įorward- and Backward-propagation and Gradient Descent (From Scratch FNN Regression)įrom Scratch Logistic Regression Classification Building a Feedforward Neural Network with PyTorch (GPU)
Model E: 3 Hidden Layer Feedforward Neural Network (ReLU Activation)ģ. Model D: 2 Hidden Layer Feedforward Neural Network (ReLU Activation) Model C: 1 Hidden Layer Feedforward Neural Network (ReLU Activation) Model B: 1 Hidden Layer Feedforward Neural Network (Tanh Activation) Model A: 1 Hidden Layer Feedforward Neural Network (Sigmoid Activation) Logistic Regression Transition to Neural Networksīuilding a Feedforward Neural Network with PyTorch
0 kommentar(er)
