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Deep Learning's Greatest Hits

Research "Records" That Shaped and Transformed the Field

Foundational Concepts

Gradient Descent

• Minimization of Functions Having Lipschitz Continuous First Partial Derivatives, L. Armijo, 1966

Stochastic Approximation

• A Stochastic Approximation Method, Robbins and Monro, 1951

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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

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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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Challenge : Write less than 100 word paragraph on any 5 important topics related to Deep Learning Models

Activation Functions

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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Universal Workflow for Approaching a Machine Learning Problem

Define the Problem and Assemble a Dataset :

• 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: