Tensorflow Tutorial: Hands-on AI development with Tensorflow

Tensorflow Tutorial: Hands-on AI development with Tensorflow, available at $19.99, has an average rating of 3.67, with 40 lectures, based on 6 reviews, and has 139 subscribers.

You will learn about Basics of TensorFlow 2.0 Decision Trees and Linear Regression in TensorFlow Keras Foundational algorithms This course is ideal for individuals who are Students who want to learn practical implementation of algorithms in TensorFlow It is particularly useful for Students who want to learn practical implementation of algorithms in TensorFlow.

Enroll now: Tensorflow Tutorial: Hands-on AI development with Tensorflow

Summary

Title: Tensorflow Tutorial: Hands-on AI development with Tensorflow

Price: $19.99

Average Rating: 3.67

Number of Lectures: 40

Number of Published Lectures: 40

Number of Curriculum Items: 40

Number of Published Curriculum Objects: 40

Original Price: $49.99

Quality Status: approved

Status: Live

What You Will Learn

Basics of TensorFlow 2.0
Decision Trees and Linear Regression in TensorFlow
Keras
Foundational algorithms

Who Should Attend

Students who want to learn practical implementation of algorithms in TensorFlow

Target Audiences

Students who want to learn practical implementation of algorithms in TensorFlow

Undoubtedly, TensorFlow is one of the most popular & widely used open-source libraries for machine learning applications. Apart from it, TensorFlow is also heavily used for dataflow and differentiable programming across a range of tasks. Because of this and a lot of other promises, hundreds of individuals are keen on exploring TensorFlow for AI & ML, Data Science, text-based application, video detection & others.

In order to cater to all our student’s needs for learning TensorFlow, we have curated this exclusive practical guide. It will teach you Practical TensorFlow with more from a training perspective rather than just the theoretical knowledge.

What makes this course so unique?

It will help you in understanding both basics and the advanced concepts of TensorFlow along with the codes in a practical manner! Upon completing this course, you will be able to learn various essential aspects of this famous library. It will unfold with the basic introduction covering graphs, Keras, supervised learning and others.

In the later sections, you will learn more about AI & ML models like decision trees, linear regression & logistic regression along with evaluating models, gradient descent & digit classification. Concepts of CNN are also covered along with its architectures, layers, K-means algorithm, K-means implementation, facial recognition & others.

This course includes:

Section 1- TensorFlow 2.0, Graphs, Automatic Differentiation, Keras and TensorFlow, Intro to Machine Learning, Types of Supervised Learning.

Section 2-Decision Trees, Linear Regression, Logistic Regression, Model Evaluation.

Section 3-Gates and Forward Propagation, Complex Decision Boundaries, Backpropagation, Gradient Descent Type and Softmax, Digit Classification.

Section 4- CNN, Layers of CNN, Famous CNN Architectures.

Section 5- K-Means Algorithm, Centroid Initialization, K-Means ++, Number of Clusters, K-Means Implementation, Principal Component Analysis, Facial Recognition using PCA.

Searching for the online course that will teach you TensorFlow practically? Search no more!! Begin with this course today to get your hands dirty with TensorFlow!!

Course Curriculum

Chapter 1: Section 1

Lecture 1: What is TensorFlow 2 Preview

Lecture 2: Basics of TensorFlow

Lecture 3: Graphs

Lecture 4: Automatic Differentiation

Lecture 5: Keras and TensorFlow

Lecture 6: Intro to Machine Learning

Lecture 7: Types of Supervised Learning

Chapter 2: Section 2

Lecture 1: Decision Trees – Theory

Lecture 2: Decision Trees – Implementation

Lecture 3: Linear Regression – Theory

Lecture 4: Linear Regression – Implementation

Lecture 5: Logistic Regression – Theory

Lecture 6: Logistic Regression – Implementation

Lecture 7: Overfitting and Regularization

Lecture 8: Model Evaluation – Theory

Lecture 9: Model Evaluation – Implementation

Chapter 3: Section 3

Lecture 1: Introduction

Lecture 2: Gates and Forward Propagation

Lecture 3: Complex Decision Boundaries

Lecture 4: Backpropagation

Lecture 5: Gradient Descent Type and Softmax

Lecture 6: Digit Classification

Chapter 4: Section 4

Lecture 1: Introduction

Lecture 2: Convolution in CNN (part1)

Lecture 3: Convolution in CNN (part2)

Lecture 4: Layers of CNN

Lecture 5: Digit Classification

Lecture 6: Famous CNN Architectures

Chapter 5: Section 5

Lecture 1: K-Means Algorithm (Part 1)

Lecture 2: K-Means Algorithm (Part 2)

Lecture 3: Centroid Initialization

Lecture 4: K-Means ++

Lecture 5: Number of Clusters

Lecture 6: K-Means Implementation

Lecture 7: Principal Component Analysis

Lecture 8: Facial Recognition using PCA

Chapter 6: Live Projects

Lecture 1: Fashion Clothing Recognition

Lecture 2: CIFAR 10 and CNN

Lecture 3: Cats vs Dogs

Lecture 4: Action Recognition

Instructors

Eduonix Learning Solutions
1+ Million Students Worldwide | 200+ Courses

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1 stars: 1 votes
2 stars: 0 votes
3 stars: 1 votes
4 stars: 2 votes
5 stars: 2 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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