Deep Learning with Python

Deep Learning with Python

Location: BTM 2ND STAGE, 773,3RD FLOOR, 7TH CROSS 16TH MAIN
City: BENGALURU, INDIA
Hours: 2:00 PM (IST) ONWARDS
Contact Email:
Mobile No.: Walsoul Pvt Lt
From Date: 02-12-2019
To Date: 31-12-2019

Description:

OVERVIEW

Introductory Terms

 

 
Data and Data Science.
Big Data.
Why Big Data.
Math and Data Science.
Introduction to Statistics.
What is learning?
Different type of learning.
Introduction to Data mining, machine learning.
Introduction to artificial intelligence.
What is a model?
Mathematical models.
 
 
NumPy Refresher :
 
 
 
Introduction to NumPy.
Ndarray.
Array creation
Matrix
addition, subtraction, multiplication on Array
Matrix multiplication.
 
MatPlotlib Refresher :
 
 
 
Pyplot as submodule.
Scatterplot
lineplot
histogram
PiChart
Bar Chart
 
 
TensorFlow Introduction :
 
 
 
TensorFlow History.
Installing TensorFlow.
Introduction to Jupyter.
TensorFlow with Jupyter.
Introduction to tensor in context of tensor flow.
TensorFlow Data types
Computation and Dataflow graph
Concept of session.
Constant
Placeholder
Variables.
 
 
Mathematical operations in TensorFlow :
 
 
 
Multiplication
Summation
Maximum
Minimum
Complex number operations.
Some more mathematical functions.
 
 
Matrix operation and Linear algebra in TensorFlow :
 
 
 
Matrix summation and Substraction.
Matrix Transpose.
Determinant of Matrix.
Matrix multiplication.
Inverse matrix.
 
 
Linear regression :
 
 
 
Introduction to linear regression.
Simple linear regression.
Parameter estimations.
Simple linear regression with TensorFlow.
Evaluating our model.
 
 
Logistic Regression :
 
 
 
Logistic Regression Introduction.
Parameter estimation.
With TensorFlow.
Model Evaluation.
 
 
Clustering :
 
 
 
Introduction to Clustering
Kmeans
Kmeans with TensorFlow
Optimizing Kmeans
Market Segmentation
.
Deep Learning :
 
 
 
Introduction
Use cases
Why I use deep learning ?
 
 
Introduction to Neural Network :
 
 
 
Biological Neuron an Introduction.
Component of biological Neuron.
Artificial Neuron.
Working of artificial neuron.
Activation function
Sigmoid function.
Linear
ReLU
Tanh
Concept of feed forward.
AND, OR and NOT
Perceptron.
Perceptron learning algorithm.
Implementing Perceptron in TensorFlow.
 
 
Multilayer perceptron :
 
 
 
Concept of gradient descent.
Backpropgation algorithm.
Problem of vanishing gradient.
MLP with TensorFlow.
Classifying our data.
 
 
Convolutional Neural networks (CNN) :
 
 
 
Convolutional Neural networks Introduction.
Convolutional Layer.
Pooling Layer .
Connecting fully.
Image classification and Convolutional Networks.
TensorFlow and CNN
Image Classification with TensorFlow.
Model evaluation.
 
 
Recurrent Neural network (RNN) :
 
  • Introduction
  • Back Propagation through time (BPTT)
  • Need of Memory.
  • Long Short Term memory (LSTM).
  • Bi-Directional RNN
  • Implementing RNN with TensorFlow.
  • Time Series and RNN
  • Sequence prediction with RNN.

 

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BTM 2ND STAGE, 773,3RD FLOOR, 7TH CROSS 16TH MAIN, BENGALURU, INDIA

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