- What is your input data?
- What are you trying to predict?
- What type of problem is it - Supervised? Unsupervised? Self-Supervised? Reinforcement Learning?
- Be aware of the hypotheses that you are making at this stage:
Challenge : Write less than 100 word paragraph on any 5 important topics related to Deep Learning Models
Activation functions are introduced in the neural network to capture non-linearities in the input data. It converts the weighted sum of a node's input with an addition of bias, to the node's output, eventually providing an advantage to the network on controlling output of the nodes, compared to a network without activation function which essentially works as linear regression model.
Some most used activation functions are:
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import matplotlib.pyplot as plt | |
import keras.backend as K | |
from keras.callbacks import Callback | |
class LRFinder(Callback): | |
''' | |
A simple callback for finding the optimal learning rate range for your model + dataset. | |
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# Based on https://stackoverflow.com/questions/49221565/unable-to-use-cv-bridge-with-ros-kinetic-and-python3 | |
sudo apt-get install python-catkin-tools python3-dev python3-catkin-pkg-modules python3-numpy python3-yaml ros-melodic-cv-bridge | |
# Create catkin workspace | |
mkdir catkin_ws | |
cd catkin_ws | |
catkin init | |
# Instruct catkin to set cmake variables | |
catkin config -DPYTHON_EXECUTABLE=/usr/bin/python3 -DPYTHON_INCLUDE_DIR=/usr/include/python3.6m -DPYTHON_LIBRARY=/usr/lib/x86_64-linux-gnu/libpython3.6m.so |