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Deep Learning without
PhD, masters, graduation 

Mayur Bhangale
StoreKey
A Python based scientific computing package targeted at two sets of
audiences:
• A replacement for numpy to use the power of GPUs
• A deep learning research platform that provides maximum flexibility

and speed
Basics
Variables, Tensors,

Autograd
Predictive models
Linear Regression
• Fit a line to a data set of observations
• Use this line to predict unobserved values
Predictive models
Linear Regression
Input
x
Output
y
mx + c = y
Linear Function
10
20
100
200
1.3
1.2
4.5
4.8
Predictive models
Logistic Regression
• Predicts the probability of occurrence of an event by
fitting data to a logit function.
• Used to predict a binary outcome (1 / 0, Yes / No,
True / False) given a set of independent variables.
Predictive models
Logistic Regression
Input
x
Logits
y
mx + c = y
Linear
Function
10
20
100
200
1.3
1.2
4.5
4.8
S = g(y)
Softmax
Function
g(y)
Softmax
0.1
0.1
0.4
0.4
D(S, L)
Cross
Entropy
Function
0
0
1
1
L
True Labels
Predictive models
Input
x
Logits
y
Linear
Function
Softmax
Function
g(y)
Softmax
D(S, L)
Cross
Entropy
Function
Labels
L
True Labels
Readout LayerInput Layer Output Layer
Logistic Regression
Predictive models
Input
x
Logits
y
Linear
Function
Softmax
Function
g(y)
Softmax
Cross
Entropy
Function
Labels
L
True Labels
Readout LayerInput Layer Output Layer
Example
Size:
784
Size:
10
Convolutional Neural Network
Thank you
Mayur Bhangale
mayur@storekey.in
mayurbhangalemayurcb mayurbhangale

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Deep Learning with PyTorch