Building a NN from scratch in Python
Start by building a very simple network: import numpy as np class NeuralNetwork: def __init__(self, x,y): self.input = x self.y = y self.Weights1 = np.random.randn(self.input.shape[1],5) …
Start by building a very simple network: import numpy as np class NeuralNetwork: def __init__(self, x,y): self.input = x self.y = y self.Weights1 = np.random.randn(self.input.shape[1],5) …
During inference, precision in floats is not needed and can be reduced to using 8 bits instead of 32 bits this allows to bin continuous …
After training we can optimize a frozen graph or even a dynamic graph by removes training-specific and debug-specific nodes, fusing common operations, and removes code …
In prediction/inference mode, variable types are unnecessary, so by freezing the graph we convert all variables in a graph and checkpoint into constants. Also there …
To perform experience replay we store the agent’s experiences et=(st,at,rt,st+1) Then we use a random sample of these prior actions instead of the most recent …
Learns a policy which tells an agent what action to take under what circumstances. Q-learning learns a policy that is optimal in the sense that …
* Is a discrete time stochastic control process for decision making in situations where outcomes are partly random and partly under the control of a …
A variational autoencoder provides a probability distribution for describing an observation/attribute in latent/hidden space.
The chain rule/general product rule permits the calculation of any member of the joint distribution of a set of random variables using only conditional probabilities.
* How software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. * The focus is finding …