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Introduction to Deep Learning with TensorFlow 2.0


Introduction to Deep Learning with TensorFlow 2.0

Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: aac, 44100 Hz
Language: English | VTT | Size: 2.35 GB | Duration: 5.5 hours


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
Multiple Linear Regression in TensorFlow
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.
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.
Necessary Data Analysis
Multi-colinearity
Factor Analysis
Feature Engineering:
Lasso Regression
Identify optimal penalty factor
Feature Selection
Pipeline Model
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.
Who this course is for:
Anyone who want to build and train their own network
Curious of data science
Who want to learning Deep Learning

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