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Introduction to Deep Learning with TensorFlow 2.0

 

Introduction to Deep Learning with TensorFlow 2.0

GETTING STARTED

Introduction to Deep Learning with TensorFlow 2.0 Advanced implementation of regression model and essential tasks to be performed like feature selection in TensorFlow 2.x
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What you'll learn

  • TensorFlow 2.0
  • Gradient Descent Algorithm
  • Create Pipeline regression model in TensorFlow
  • Lasso Regression
  • Feature Selection with lasso
  • Programming in TensorFlow 2.0
  • Selection of Penalty factor lambda
  • Visualizing graph in TensorBoard
  • Neuron or Perceptron Model Architecture
  • Loss or Cost Function
  • TensorFlow Keras API
  • Linear Regression
  • Create customized model in TensorFlow
  • Exploratory Data Analysis
  • Data Preprocessing

Requirements

  • Beginner to Python

 Description

In this course, you will learn advanced linear regression technique process and with this you can able to build any regression problem. Starting from

  • TensorFlow 2.x

  • Linear Regression

  • Gradient Descent Algorithm

With this intuition we will work on project: Customer Revenue Prediction.

Problem Statement: A large child education toy company which sells educational tablets and gaming systems both online and in retail stores wanted to analyse the customer data. The goal of the problem is determine the following objective as shown below.

  1. Data Analysis & Preprocessing: Analyze customer data and draw the insights w.r.t revenue and based on the insights we will do data preprocessing.  In this module you will learn the following.

    1. Necessary Data Analysis

    2. Multi-colinearity

    3. Factor Analysis

  2. Feature Engineering:

    1. Lasso Regression

    2. Identify optimal penalty factor

    3. Feature Selection

  3. Pipeline Model

  4. Evaluation

We will start with basic of tensorflow 2.x to advanced techniques in it. Then we drive into intuition behind linear regression and optimization function like gradient descent.





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