Deep Learning with TensorFlow (beginner to expert level)

Deep Learning with TensorFlow (beginner to expert level), available at $34.99, has an average rating of 3.45, with 89 lectures, 1 quizzes, based on 89 reviews, and has 23883 subscribers.

You will learn about End-to-end knowledge of TensorFlow TensorFlow concepts, development, coding, applications TensorFlow components & pipelines TensorFlow examples Introduction to Python, Linear Algebra, Matplotlib, NumPy, Pandas Introduction to Files Introduction to Machine Learning TensorFlow Playground & Perceptrons TensorFlow and Artificial Intelligence Building Artificial Neural Networks (ANN) with TensorFlow Types of ANN and Components of Neural Networks TensorFlow Classification and Linear Regression TensorFlow vs. PyTorch vs. Theano vs. Keras Object Identification in TensorFlow TensorFlow Superkeyword CNN & RNN, RNN Time Series TensorBoard – TensorFlows visualization toolkit This course is ideal for individuals who are Machine Learning & Deep Learning Engineers or Data Scientists & Senior Data Scientists or Beginners and newbies aspiring for a career in Machine Learning / Deep Learning or Data Analysts & Advanced Data Analytics Professionals or TensorFlow Engineers or Machine Learning Developers – TensorFlow/Hadoop or Software Developers – AI/ML/Deep Learning or Anyone wishing to learn TensorFlow algorithms and applications or Deep Learning Engineers – Python/TensorFlow or Artificial Intelligence Engineers and Senior ML/DL Engineers or Researchers and PhD students or Data Engineers or AI & RPA Developers – TensorFlow/ML or AI/ML Developers or Machine Learning Leads & Enthusiasts or TensorFlow and Advanced ML Developers or Research Scientists (Deep Learning) It is particularly useful for Machine Learning & Deep Learning Engineers or Data Scientists & Senior Data Scientists or Beginners and newbies aspiring for a career in Machine Learning / Deep Learning or Data Analysts & Advanced Data Analytics Professionals or TensorFlow Engineers or Machine Learning Developers – TensorFlow/Hadoop or Software Developers – AI/ML/Deep Learning or Anyone wishing to learn TensorFlow algorithms and applications or Deep Learning Engineers – Python/TensorFlow or Artificial Intelligence Engineers and Senior ML/DL Engineers or Researchers and PhD students or Data Engineers or AI & RPA Developers – TensorFlow/ML or AI/ML Developers or Machine Learning Leads & Enthusiasts or TensorFlow and Advanced ML Developers or Research Scientists (Deep Learning).

Enroll now: Deep Learning with TensorFlow (beginner to expert level)

Summary

Title: Deep Learning with TensorFlow (beginner to expert level)

Price: $34.99

Average Rating: 3.45

Number of Lectures: 89

Number of Quizzes: 1

Number of Published Lectures: 89

Number of Published Quizzes: 1

Number of Curriculum Items: 90

Number of Published Curriculum Objects: 90

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

End-to-end knowledge of TensorFlow

TensorFlow concepts, development, coding, applications

TensorFlow components & pipelines

TensorFlow examples

Introduction to Python, Linear Algebra, Matplotlib, NumPy, Pandas

Introduction to Files

Introduction to Machine Learning

TensorFlow Playground & Perceptrons

TensorFlow and Artificial Intelligence

Building Artificial Neural Networks (ANN) with TensorFlow

Types of ANN and Components of Neural Networks

TensorFlow Classification and Linear Regression

TensorFlow vs. PyTorch vs. Theano vs. Keras

Object Identification in TensorFlow

TensorFlow Superkeyword

CNN & RNN, RNN Time Series

TensorBoard – TensorFlows visualization toolkit

Who Should Attend

Machine Learning & Deep Learning Engineers

Data Scientists & Senior Data Scientists

Beginners and newbies aspiring for a career in Machine Learning / Deep Learning

Data Analysts & Advanced Data Analytics Professionals

TensorFlow Engineers

Machine Learning Developers – TensorFlow/Hadoop

Software Developers – AI/ML/Deep Learning

Anyone wishing to learn TensorFlow algorithms and applications

Deep Learning Engineers – Python/TensorFlow

Artificial Intelligence Engineers and Senior ML/DL Engineers

Researchers and PhD students

Data Engineers

AI & RPA Developers – TensorFlow/ML

AI/ML Developers

Machine Learning Leads & Enthusiasts

TensorFlow and Advanced ML Developers

Research Scientists (Deep Learning)

Target Audiences

Machine Learning & Deep Learning Engineers

Data Scientists & Senior Data Scientists

Beginners and newbies aspiring for a career in Machine Learning / Deep Learning

Data Analysts & Advanced Data Analytics Professionals

TensorFlow Engineers

Machine Learning Developers – TensorFlow/Hadoop

Software Developers – AI/ML/Deep Learning

Anyone wishing to learn TensorFlow algorithms and applications

Deep Learning Engineers – Python/TensorFlow

Artificial Intelligence Engineers and Senior ML/DL Engineers

Researchers and PhD students

Data Engineers

AI & RPA Developers – TensorFlow/ML

AI/ML Developers

Machine Learning Leads & Enthusiasts

TensorFlow and Advanced ML Developers

Research Scientists (Deep Learning)

A warm welcome to the Deep Learning with TensorFlowcourse by Uplatz.

TensorFlowis an end-to-end open-source machine learning / deep learning platform. It has a comprehensive ecosystem of libraries, tools, and community resources that lets AI/ML engineers, scientists, analysts build and deploy ML-powered deep learning applications. The name TensorFlowis derived from the operations which neural networks perform on multidimensional data arrays or tensors. Deep learning is a subfield of machine learning that is a set of algorithms that is inspired by the structure and function of the brain.

TensorFlow is a machine learning framework that Google created and used to design, build, and train deep learning models. You can use the TensorFlow library do to numerical computations, which in itself doesn’t seem all too special, but these computations are done with data flow graphs. In these graphs, nodes represent mathematical operations, while the edges represent the data, which usually are multidimensional data arrays or tensors, that are communicated between these edges.

In simple words, TensorFlow is an open-source and most popular deep learning library for research and production. TensorFlow in Python is a symbolic math library that uses dataflow and differentiable programming to perform various tasks focused on training and inference of deep neural networks. TensorFlow manages to combine a comprehensive and flexible set of technical features with great ease of use.

There have been some remarkable developments lately in the world of artificial intelligence, from much publicized progress with self-driving cars to machines now composing imitations or being really good at video games. Central to these advances are a number of tools around to help derive deep learning and other machine learning models, with Torch, Caffe, and Theano amongst those at the fore. However, since Google Brain went open source in November 2015 with their own framework, TensorFlow, the popularity of this software library has skyrocketed to be the most popular deep learning framework.

TensorFlow enables you to build dataflow graphs and structures to define how data moves through a graph by taking inputs as a multi-dimensional array called Tensor. It allows you to construct a flowchart of operations that can be performed on these inputs, which goes at one end and comes at the other end as output.

Top organizations such as Google, IBM, Netflix, Disney, Twitter, Micron, all use TensorFlow. 

Uplatzprovides this extensive course on TensorFlow. This TensorFlow course covers TensorFlow basics, components, pipelines to advanced topics like linear regression, classifier, create, train and evaluate a neural network like CNN, RNN, auto encoders etc. with TensorFlow examples.

The TensorFlow training is designed in such a way that you’ll be able to easily implement deep learning project on TensorFlow in an easy and efficient way. In this TensorFlow course you will learn the fundamentals of neural networks and how to build deep learning models using TensorFlow. This TensorFlow training provides a practical approach to deep learning for software engineers. You’ll get hands-on experience building your own state-of-the-art image classifiers and other deep learning models. You’ll also use your TensorFlow models in the real world on mobile devices, in the cloud, and in browsers. Finally, you’ll use advanced techniques and algorithms to work with large datasets. You will acquire skills necessary to start creating your own AI applications and models.

You’ll master deep learning concepts and models using TensorFlow frameworks and implement deep learning algorithms, preparing you for a career as Deep Learning Engineer. Learn how to build a neural network and how to train, evaluate and optimize it with TensorFlow.

TensorFlow is completely based on Python. This course also provides a sound introduction to Python programming concepts, NumPy, Matplotlib, and Pandas so that you can acquire those skills in this course itself before moving on to learn the TensorFlow concepts. The aim of this TensorFlow tutorial is to describe all TensorFlow objects and method.

This TensorFlow course also includes a comprehensive description of TensorBoard visualization tool. You will gain an understanding of the mechanics of this tool by using it to solve a general numerical problem, quite outside of what machine learning usually involves, before introducing its uses in deep learning with a simple neural network implementation.

TensorFlow Architecture

TensorFlow architecture works in three parts:

Preprocessing the data

Build the model

Train and estimate the model

It is called TensorFlow because it takes input as a multi-dimensional array, also known as tensors. You can construct a sort of flowchart of operations (called a Graph) that you want to perform on that input. The input goes in at one end, and then it flows through this system of multiple operations and comes out the other end as output.

This is why it is called TensorFlow because the tensor goes in it flows through a list of operations, and then it comes out the other side.

TensorFlow – Course Syllabus

Course Curriculum

Chapter 1: TensorFlow Introduction

Lecture 1: TensorFlow Introduction

Chapter 2: TensorFlow Applications

Lecture 1: TensorFlow Applications

Chapter 3: TensorFlow Basics

Lecture 1: TensorFlow Basics – part 1

Lecture 2: TensorFlow Basics – part 2

Chapter 4: TensorFlow Components

Lecture 1: TensorFlow Components – part 1

Lecture 2: TensorFlow Components – part 2

Chapter 5: TensorFlow Pipeline

Lecture 1: TensorFlow Pipeline

Chapter 6: TensorFlow Examples

Lecture 1: TensorFlow Examples

Chapter 7: Introduction to Linear Algebra

Lecture 1: Introduction to Linear Algebra – part 1

Lecture 2: Introduction to Linear Algebra – part 2

Chapter 8: Introduction to Python

Lecture 1: Introduction to Python – part 1

Lecture 2: Introduction to Python – part 2

Lecture 3: Introduction to Python – part 3

Lecture 4: Introduction to Python – part 4

Lecture 5: Introduction to Python – part 5

Lecture 6: Introduction to Python – part 6

Lecture 7: Introduction to Python – part 7

Lecture 8: Introduction to Python – part 8

Lecture 9: Introduction to Python – part 9

Lecture 10: Introduction to Python – part 10

Lecture 11: Introduction to Python – part 11

Lecture 12: Introduction to Python – part 12

Lecture 13: Introduction to Python – part 13

Lecture 14: Introduction to Python – part 14

Lecture 15: Introduction to Python – part 15

Chapter 9: Introduction to Matplotlib

Lecture 1: Introduction to Matplotlib

Chapter 10: Introduction to NumPy

Lecture 1: Introduction to NumPy – part 1

Lecture 2: Introduction to NumPy – part 2

Chapter 11: Introduction to Pandas

Lecture 1: Introduction to Pandas – part 1

Lecture 2: Introduction to Pandas – part 2

Lecture 3: Introduction to Pandas – part 3

Lecture 4: Introduction to Pandas – part 4

Lecture 5: Introduction to Pandas – part 5

Lecture 6: Introduction to Pandas – part 6

Lecture 7: Introduction to Pandas – part 7

Lecture 8: Introduction to Pandas – part 8

Chapter 12: File Management

Lecture 1: File Management – part 1

Lecture 2: File Management – part 2

Lecture 3: File Management – part 3

Chapter 13: Machine Learning

Lecture 1: Machine Learning – part 1

Lecture 2: Machine Learning – part 2

Lecture 3: Machine Learning – part 3

Lecture 4: Machine Learning – part 4

Lecture 5: Machine Learning – part 5

Lecture 6: Machine Learning – part 6

Lecture 7: Machine Learning – part 7

Lecture 8: Machine Learning – part 8

Lecture 9: Machine Learning – part 9

Lecture 10: Machine Learning – part 10

Lecture 11: Machine Learning – part 11

Lecture 12: Machine Learning – part 12

Lecture 13: Machine Learning – part 13

Lecture 14: Machine Learning – part 14

Lecture 15: Machine Learning – part 15

Lecture 16: Machine Learning – part 16

Lecture 17: Machine Learning – part 17

Lecture 18: Machine Learning – part 18

Lecture 19: Machine Learning – part 19

Lecture 20: Machine Learning – part 20

Lecture 21: Machine Learning – part 21

Lecture 22: Machine Learning – part 22

Chapter 14: TensorFlow Playground

Lecture 1: TensorFlow Playground

Chapter 15: TensorFlow Perceptrons

Lecture 1: TensorFlow Perceptrons – part 1

Lecture 2: TensorFlow Perceptrons – part 2

Lecture 3: TensorFlow Perceptrons – part 3

Chapter 16: TensorFlow and Artificial Intelligence

Lecture 1: TensorFlow and Artificial Intelligence

Chapter 17: TensorFlow ANN

Lecture 1: TensorFlow ANN

Chapter 18: Types of ANN

Lecture 1: Types of ANN – part 1

Lecture 2: Types of ANN – part 2

Chapter 19: Components of Neural Networks

Lecture 1: Components of Neural Networks

Chapter 20: Classification in TensorFlow

Lecture 1: Classification in TensorFlow – part 1

Lecture 2: Classification in TensorFlow – part 2

Lecture 3: Classification in TensorFlow – part 3

Lecture 4: Classification in TensorFlow – part 4

Lecture 5: Classification in TensorFlow – part 5

Chapter 21: Linear Regression in TensorFlow

Lecture 1: Linear Regression in TensorFlow

Chapter 22: Difference between TensorFlow, PyTorch, Theano, Keras

Lecture 1: Difference between TensorFlow, PyTorch, Theano, Keras

Chapter 23: Object Identification in TensorFlow

Instructors

Uplatz Training
Fastest growing global Technology & Cloud Training Provider

Rating Distribution

1 stars: 9 votes

2 stars: 5 votes

3 stars: 21 votes

4 stars: 17 votes

5 stars: 37 votes

Frequently Asked Questions

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

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!