Intro to Deep Learning project in TensorFlow 2.x and Python

Intro to Deep Learning project in TensorFlow 2.x and Python, available at $39.99, has an average rating of 4.2, with 49 lectures, based on 77 reviews, and has 20024 subscribers.

You will learn about 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 This course is ideal for individuals who are Anyone who want to build and train their own network or Curious of data science or Who want to learning Deep Learning It is particularly useful for Anyone who want to build and train their own network or Curious of data science or Who want to learning Deep Learning.

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Summary

Title: Intro to Deep Learning project in TensorFlow 2.x and Python

Price: $39.99

Average Rating: 4.2

Number of Lectures: 49

Number of Published Lectures: 49

Number of Curriculum Items: 49

Number of Published Curriculum Objects: 49

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

Anyone who want to build and train their own network
Curious of data science
Who want to learning Deep Learning

Target Audiences

Anyone who want to build and train their own network
Curious of data science
Who want to learning Deep Learning

Welcome to the Course Introduction to Deep Learning with TensorFlow 2.0:

In this course, you will learn advanced linear regression technique process and with this, you can be able to build any regression problem. Using this you can solve real-world problems like customer lifetime value, predictive analytics, etc.

What you will Learn

· TensorFlow 2.x

· Google Colab

· Linear Regression

· Gradient Descent Algorithm

· Data Analysis

· Regression

· Feature Engineering and Selection with Lasso Regression.

· Model Evaluation

All the above-mentioned techniques are explained in TensorFlow. In this course, you will work on the Project Customer Revenue (Lifetime value) Prediction using Gradient Descent Algorithm

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

1. Data Analysis & Pre-processing:Analyse customer data and draw the insights w.r.t revenue and based on the insights we will do data pre-processing. In this module, you will learn the following.

1. Necessary Data Analysis

2. Multi-collinearity

3. Factor Analysis

2. Feature Engineering:

1. Lasso Regression

2. Identify the optimal penalty factor.

3. Feature Selection

3. Pipeline Model

4. Evaluation

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

Course Curriculum

Chapter 1: Introduction

Lecture 1: Walk through the Course

Chapter 2: TensorFlow Essentials

Lecture 1: Introduction

Lecture 2: Getting Started to Google Colab

Lecture 3: Tensor Data Structure

Lecture 4: TensorFlow: Convert List to Tensors

Lecture 5: TensorFlow: Convert Numpy Array to Tensors

Lecture 6: TensorFlow: Constant

Lecture 7: TensorFlow 1.x vs TensorFlow 2.x

Lecture 8: Operators

Lecture 9: TensorFlow: Operators

Lecture 10: Data Flow Graph

Lecture 11: Google Colab Integrating to Google Drive

Lecture 12: TensorBoard – Data Flow Graph

Lecture 13: Second Graph

Lecture 14: Assignment – 1

Lecture 15: Assignment -1: Solution

Lecture 16: Dense Network Part-1

Lecture 17: Dense Network Part-2

Lecture 18: Assignment – 2: Question

Lecture 19: Assignment -2 : Solution

Chapter 3: Fitting Linear Model (Linear Regression)

Lecture 1: What you will learn

Lecture 2: Linear Regression Intuition

Lecture 3: Gradient Descent Algorithm

Lecture 4: Linear Model Architecture – Perceptron (Neuron)

Lecture 5: TensorFlow – Linear Regression, Part-1

Lecture 6: TensorFlow – Linear Regression, Part-2

Lecture 7: TensorFlow – Loss Function

Lecture 8: TensorFlow – Gradient Descent

Lecture 9: TensorFlow – Fitting Model

Lecture 10: TensorFlow – Keras – Linear Regression

Chapter 4: Project – 1

Lecture 1: Project Overview

Chapter 5: Data Analysis

Lecture 1: Data and Distribution

Lecture 2: Distribution part-2

Lecture 3: Multicollinearity

Lecture 4: Factor Analysis

Lecture 5: Conclusion of Data Analysis

Lecture 6: Data Preprocessing

Chapter 6: Feature Engineering

Lecture 1: Multiple Linear Regression

Lecture 2: TensorFlow – Multiple Linear Regression

Lecture 3: Lasso Regression – L1 Regularization

Lecture 4: TensorFlow – Lasso Regression and Penalty Factor Slection

Lecture 5: Feature Selection

Chapter 7: Final Pipeline Model

Lecture 1: Split data into Train and Test frames

Lecture 2: Input Pipelines

Lecture 3: Feature Columns

Lecture 4: Training Pipeline Model

Lecture 5: Save and Restore

Lecture 6: Model Evaluation

Chapter 8: BONUS

Lecture 1: Bonus Lecture

Instructors

datascience Anywhere
Team of Engineers
Sudhir G
Data Scientist
Brightshine Learn
Instructor Team

Rating Distribution

1 stars: 3 votes
2 stars: 2 votes
3 stars: 13 votes
4 stars: 24 votes
5 stars: 35 votes

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