Book Online Tickets for Deep learning with TensorFlow Python, Bengaluru. 





COURSE DESCRIPTION

TensorFlow is a famous deep learning framework, this library is based on Python and will help you to run various algorithms of Artificial Neural network. Prerequisite: Participants must have knowledge of Python, knowledge of

Deep learning with TensorFlow Python

 

  • Online & Offline

    Sale Date Ended

    INR 12000
    Sold Out

Invite friends

Contact Us

Page Views : 19

About The Event

COURSE DESCRIPTION

TensorFlow is a famous deep learning framework, this library is based on Python and will help you to run various algorithms of Artificial Neural network. Prerequisite: Participants must have knowledge of Python, knowledge of Machine learning will be helpful.

OBJECTIVE

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