Deep learning with TensorFlow Python-Walsoul Pvt Lt

Deep learning with TensorFlow Python-Walsoul Pvt Lt

City: BENGALURU
Country: India
Email:
Phone No.:
Start Date: 08-06-2019
End Date: 16-06-2019
Website URL: https://www.townscript.com/e/deep-learning-with-tensorflow-python-412341?prS=listing&seS=topicpage
Price: 12000

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
  • Pandas Refresher
 
DataFrame
Dataframe operations
 
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
  • Word embeding
  • Implementing RNN with TensorFlow.
  • Time Series and RNN
  • Sequence prediction with RNN.
 
 
 
 
Projects :
 
  • Three Projects on Image classifications
  • One Project on time series with RNN
  • One Project on sequence prediction

TICKETS

Tickets for Deep learning with TensorFlow Python can be booked here.

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

 

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