Complete Guide to TensorFlow for Deep Learning with Python
4.4 (14,828 ratings)
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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!
4.4 (14,828 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
80,401 students enrolled
Created by Jose Portilla
Last updated 4/2020
English
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Current price: $135.99 Original price: $194.99 Discount: 30% off
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This course includes
  • 14 hours on-demand video
  • 7 articles
  • 5 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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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!
Course content
Expand all 96 lectures 14:09:52
+ Introduction
3 lectures 12:37
Course Overview -- PLEASE DON'T SKIP THIS LECTURE! Thanks :)
09:23
FAQ - Frequently Asked Questions
00:10
+ Installation and Setup
2 lectures 12:55
Quick Note for MacOS and Linux Users
00:54

Learn how to install Tensorflow on your computer and setup using our environment file.

Preview 12:01
+ Crash Course Overview
7 lectures 45:57
Crash Course Section Introduction
01:12
NumPy Crash Course
15:32
Pandas Crash Course
04:23
Data Visualization Crash Course
07:41
SciKit Learn Preprocessing Overview
09:04
Crash Course Review Exercise - Solutions
05:58
+ Introduction to Neural Networks
11 lectures 01:17:59
Introduction to Neural Networks
01:06
Introduction to Perceptron
05:12
Neural Network Activation Functions
06:30
Cost Functions
03:40
Gradient Descent Backpropagation
03:20
Manual Creation of Neural Network - Part One
06:16
Manual Creation of Neural Network - Part Two - Operations
07:55
Manual Creation of Neural Network - Part Three - Placeholders and Variables
08:57
Manual Creation of Neural Network - Part Four - Session
09:48
Manual Neural Network Classification Task
16:27
+ TensorFlow Basics
15 lectures 02:39:42
Introduction to TensorFlow
01:26
TensorFlow Graphs
05:48
Variables and Placeholders
05:57
TensorFlow - A Neural Network - Part One
07:47
TensorFlow - A Neural Network - Part Two
19:50
TensorFlow Regression Example - Part One
19:43
TensorFlow Regression Example _ Part Two
22:04
TensorFlow Classification Example - Part One
14:00
TensorFlow Classification Example - Part Two
12:46
TF Regression Exercise Solution Walkthrough
12:34
TF Classification Exercise Solution Walkthrough
11:27
Saving and Restoring Models
05:54
+ Convolutional Neural Networks
14 lectures 02:09:26
Introduction to Convolutional Neural Network Section
00:49
Review of Neural Networks
02:32
Quick note on MNIST lecture
00:05
MNIST Basic Approach Part One
08:29
MNIST Basic Approach Part Two
16:47
CNN Theory Part One
18:41
CNN Theory Part Two
04:32
CNN MNIST Code Along - Part One
17:25
CNN MNIST Code Along - Part Two
06:05
Introduction to CNN Project
06:01
CNN Project Exercise Solution - Part One
15:25
CNN Project Exercise Solution - Part Two
12:59
+ Recurrent Neural Networks
16 lectures 02:38:27
Introduction to RNN Section
01:07
RNN Theory
07:57
Manual Creation of RNN
11:57
Vanishing Gradients
04:37
LSTM and GRU Theory
09:49
Introduction to RNN with TensorFlow API
04:38
RNN with TensorFlow - Part One
20:49
RNN with TensorFlow - Part Two
19:00
Quick Note on RNN Plotting Part 3
00:23
RNN with TensorFlow - Part Three
08:01
Time Series Exercise Solution
18:17
Quick Note on Word2Vec
02:49
Word2Vec Theory
12:02
Word2Vec Code Along - Part One
16:47
Word2Vec Part Two
13:11
+ Miscellaneous Topics
6 lectures 53:55
Intro to Miscellaneous Topics
00:14
Deep Nets with Tensorflow Abstractions API - Part One
07:12
Deep Nets with Tensorflow Abstractions API - Estimator API
07:25
Deep Nets with Tensorflow Abstractions API - Keras
11:55
Deep Nets with Tensorflow Abstractions API - Layers
11:02
Tensorboard
16:07
+ AutoEncoders
5 lectures 54:29
Autoencoder Basics
07:57
Dimensionality Reduction with Linear Autoencoder
17:25
Linear Autoencoder PCA Exercise Overview
01:44
Linear Autoencoder PCA Exercise Solutions
07:51
Stacked Autoencoder
19:32
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