Complete Guide to TensorFlow for Deep Learning with Python

Name: Complete Guide to TensorFlow for Deep Learning with Python
Rating: 4.4 (16487 reviews)

Learn how to use Google's Deep Learning Framework - TensorFlow with Python! Solve problems with cutting edge techniques!

(16,487 ratings)

92,701 students

Created by Jose Portilla

Last updated 4/2020

English

English [Auto], French [Auto],

What you'll learn

Understand how Neural Networks Work
Build your own Neural Network from Scratch with Python
Use TensorFlow for Classification and Regression Tasks
Use TensorFlow for Image Classification with Convolutional Neural Networks
Use TensorFlow for Time Series Analysis with Recurrent Neural Networks
Use TensorFlow for solving Unsupervised Learning Problems with AutoEncoders
Learn how to conduct Reinforcement Learning with OpenAI Gym
Create Generative Adversarial Networks with TensorFlow
Become a Deep Learning Guru!

Requirements

Some knowledge of programming (preferably Python)
Some basic knowledge of math (mean, standard deviation, etc..)

Description

Welcome to the Complete Guide to TensorFlow for Deep Learning with Python!

This course will guide you through how to use Google's TensorFlow framework to create artificial neural networks for deep learning! This course aims to give you an easy to understand guide to the complexities of Google's TensorFlow framework in a way that is easy to understand. Other courses and tutorials have tended to stay away from pure tensorflow and instead use abstractions that give the user less control. Here we present a course that finally serves as a complete guide to using the TensorFlow framework as intended, while showing you the latest techniques available in deep learning!

This course is designed to balance theory and practical implementation, with complete jupyter notebook guides of code and easy to reference slides and notes. We also have plenty of exercises to test your new skills along the way!

This course covers a variety of topics, including

Neural Network Basics
TensorFlow Basics
Artificial Neural Networks
Densely Connected Networks
Convolutional Neural Networks
Recurrent Neural Networks
AutoEncoders
Reinforcement Learning
OpenAI Gym
and much more!

There are many Deep Learning Frameworks out there, so why use TensorFlow?

TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.

It is used by major companies all over the world, including Airbnb, Ebay, Dropbox, Snapchat, Twitter, Uber, SAP, Qualcomm, IBM, Intel, and of course, Google!

Become a machine learning guru today! We'll see you inside the course!

Who this course is for:

Python students eager to learn the latest Deep Learning Techniques with TensorFlow

Featured review

Anthony L.

12 courses

4 reviews

3 years ago

I have taken two of Jose's classes and satisfied with him explaining the required basic knowledge of the field taught in the course. I understand and I am experienced with the basics of Python and the libraries he taught in his courses, however, listening to his course reassured my knowledge and helped me engraves the knowledge in my head. Jose also explains the basic concepts under the hood with easily understandable words, at least for me.

Jose Portilla

Head of Data Science at Pierian Training

4.6 Instructor Rating
929,812 Reviews
2,972,275 Students
53 Courses

Jose Marcial Portilla has a BS and MS in Mechanical Engineering from Santa Clara University and years of experience as a professional instructor and trainer for Data Science, Machine Learning and Python Programming. He has publications and patents in various fields such as microfluidics, materials science, and data science. Over the course of his career he has developed a skill set in analyzing data and he hopes to use his experience in teaching and data science to help other people learn the power of programming, the ability to analyze data, and the skills needed to present the data in clear and beautiful visualizations. Currently he works as the Head of Data Science for Pierian Training and provides in-person data science and python programming training courses to employees working at top companies, including General Electric, Cigna, The New York Times, Credit Suisse, McKinsey and many more. Feel free to check out the website link to find out more information about training offerings.