Complete Guide to TensorFlow for Deep Learning with Python

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

4.58 (16849 reviews)
Udemy
platform
English
language
Data Science
category
instructor
96,323
students
14 hours
content
Apr 2020
last update
$94.99
regular price

What you will 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!

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!

Content

Introduction

Introduction
Course Overview -- PLEASE DON'T SKIP THIS LECTURE! Thanks :)
FAQ - Frequently Asked Questions

Installation and Setup

Quick Note for MacOS and Linux Users
Installing TensorFlow and Environment Setup

What is Machine Learning?

Machine Learning Overview

Crash Course Overview

Crash Course Section Introduction
NumPy Crash Course
Pandas Crash Course
Data Visualization Crash Course
SciKit Learn Preprocessing Overview
Crash Course Review Exercise
Crash Course Review Exercise - Solutions

Introduction to Neural Networks

Introduction to Neural Networks
Introduction to Perceptron
Neural Network Activation Functions
Cost Functions
Gradient Descent Backpropagation
TensorFlow Playground
Manual Creation of Neural Network - Part One
Manual Creation of Neural Network - Part Two - Operations
Manual Creation of Neural Network - Part Three - Placeholders and Variables
Manual Creation of Neural Network - Part Four - Session
Manual Neural Network Classification Task

TensorFlow Basics

Introduction to TensorFlow
TensorFlow Basic Syntax
TensorFlow Graphs
Variables and Placeholders
TensorFlow - A Neural Network - Part One
TensorFlow - A Neural Network - Part Two
TensorFlow Regression Example - Part One
TensorFlow Regression Example _ Part Two
TensorFlow Classification Example - Part One
TensorFlow Classification Example - Part Two
TF Regression Exercise
TF Regression Exercise Solution Walkthrough
TF Classification Exercise
TF Classification Exercise Solution Walkthrough
Saving and Restoring Models

Convolutional Neural Networks

Introduction to Convolutional Neural Network Section
Review of Neural Networks
New Theory Topics
Quick note on MNIST lecture
MNIST Data Overview
MNIST Basic Approach Part One
MNIST Basic Approach Part Two
CNN Theory Part One
CNN Theory Part Two
CNN MNIST Code Along - Part One
CNN MNIST Code Along - Part Two
Introduction to CNN Project
CNN Project Exercise Solution - Part One
CNN Project Exercise Solution - Part Two

Recurrent Neural Networks

Introduction to RNN Section
RNN Theory
Manual Creation of RNN
Vanishing Gradients
LSTM and GRU Theory
Introduction to RNN with TensorFlow API
RNN with TensorFlow - Part One
RNN with TensorFlow - Part Two
Quick Note on RNN Plotting Part 3
RNN with TensorFlow - Part Three
Time Series Exercise Overview
Time Series Exercise Solution
Quick Note on Word2Vec
Word2Vec Theory
Word2Vec Code Along - Part One
Word2Vec Part Two

Miscellaneous Topics

Intro to Miscellaneous Topics
Deep Nets with Tensorflow Abstractions API - Part One
Deep Nets with Tensorflow Abstractions API - Estimator API
Deep Nets with Tensorflow Abstractions API - Keras
Deep Nets with Tensorflow Abstractions API - Layers
Tensorboard

AutoEncoders

Autoencoder Basics
Dimensionality Reduction with Linear Autoencoder
Linear Autoencoder PCA Exercise Overview
Linear Autoencoder PCA Exercise Solutions
Stacked Autoencoder

Reinforcement Learning with OpenAI Gym

Introduction to Reinforcement Learning with OpenAI Gym
Extra Resources for Reinforcement Learning
Introduction to OpenAI Gym
OpenAI Gym Steup
Open AI Gym Env Basics
Open AI Gym Observations
OpenAI Gym Actions
Simple Neural Network Game
Policy Gradient Theory
Policy Gradient Code Along Part One
Policy Gradient Code Along Part Two

GAN - Generative Adversarial Networks

Introduction to GANs
GAN Code Along - Part One
GAN Code Along - Part Two
GAN Code Along - Part Three

BONUS

Bonus Lecture

Screenshots

Complete Guide to TensorFlow for Deep Learning with Python - Screenshot_01Complete Guide to TensorFlow for Deep Learning with Python - Screenshot_02Complete Guide to TensorFlow for Deep Learning with Python - Screenshot_03Complete Guide to TensorFlow for Deep Learning with Python - Screenshot_04

Reviews

Peter
August 15, 2023
I started the course but I am still struggling with the setup (already for few hours) - imo this course is outdated...
Vivek
July 7, 2023
The course is good but this has not been updated from last 3 years. Please update to latest version of TF
Weizhou
June 18, 2023
Overall a very knowledgable and patient instructor that takes time to walk through the examples and practical knowledges. 1 critical feedback would be - for the chapter entering the TF basics where the manual examples are used for sorta laying out the foundational way to programm the model does not really help - the manual examples is just a manual walk-through how TF syntax looks - but it does not really explain why it it the way it looks. In addition, because of the manual chapter, the syntax basics are not quite well explained, rather in a way "You have seen this in the previous manual chapter, so there you see the same". I have to go back and google the TF syntax anyways to understand the syntax. Would be nice to see that logics updated a bit for a more natural explaination; was a bit chicken and egg for me. Overall, great instructor
Brandon
May 4, 2023
I feel like you guys took my 10 bucks, this course is way to outdated I spent several hours trying to make it work, but its just too old.
Jason
April 26, 2023
The setup process is several years old and Tensorflow 1.x is now longer available. It would be good if course could be updated to use TF 2
Omar
March 18, 2023
the content is good just needed someone to reach for questions cause it seems a little hard to reach someone to fix my issues any way it is good enough
Siavash
March 16, 2023
in my opinion, this course is not as strong as the other courses I had with Jose. the content is halfway between practical and theory. If this is a practical course, its too much theory for a practical course and too little practicality for a theory based course; if its a theory based course, I believe the concept doesnt have enough depth (I have to use google too much). this is my experience so far, might change later.
Dinesh
January 16, 2023
Jose is so awesome in teaching, not only in this Tensorflow for deep learning course, but also in his every other course. I took almost all of his data science courses
Robbe
January 12, 2023
This course teaches Tensorflow 1.0 which by now is completely outdated. The exercises can't even be run anymore because the versions of the libraries are way too old.
Ed
January 12, 2023
It's giving a lot of information and for me it's close to my max. Fortunately Jose is very easy to follow so with his help and some things doing twice I get along.
Sandeep
January 9, 2023
The course is outdated. As on 9th Jan 2023, the Anaconda installation does not work as per the instructor's directions. Also, TensorFlow is now at ver 2.11, but this course is still using the old methods which do not work with the new environment.
Bhaskar
November 4, 2022
Jose Portia is the best. I always want to buy his courses especially. You are my teacher. thank you for keep teaching.
Drew
October 15, 2022
The instructor moves pretty quickly and glosses over a lot of topics. I'm someone who needs a lot of time and details with the underlying theory in order to understand how something works. I didn't feel like I was getting that with this course. I chose another one called "TensorFlow Developer Certificate in 2022: Zero to Mastery" which went way more in depth. It takes a lot longer to complete, but its much better for detail oriented people like me. Hope this helps.
Les
September 18, 2022
So far so good. I haven't fully installed everything so I can't give a full 5 stars untill I'm "up and running". I'm hopeful!
Jose
June 27, 2022
En general el curso explica bien los conceptos básico, sin embargo la parte programatica esta desactulizada, utilizando la version 1.8 cuando nos encontramos actualmente en la version 2.x, haciendo complicado extrapolar ciertas cosas y tener que basicamente aprender con la documentacion propia de TF (muy bien explicada y muy útil)

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1326292
udemy ID
8/20/2017
course created date
8/8/2019
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