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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!
Instructor:
Jose Portilla
59,847 students enrolled
English [Auto-generated] More
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!

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!

Introduction

1
Introduction
2
Course Overview -- PLEASE DON'T SKIP THIS LECTURE! Thanks :)
3
FAQ - Frequently Asked Questions

Installation and Setup

1
Quick Note for MacOS and Linux Users
2
Installing TensorFlow and Environment Setup

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

What is Machine Learning?

1
Machine Learning Overview

Crash Course Overview

1
Crash Course Section Introduction
2
NumPy Crash Course
3
Pandas Crash Course
4
Data Visualization Crash Course
5
SciKit Learn Preprocessing Overview
6
Crash Course Review Exercise
7
Crash Course Review Exercise - Solutions

Introduction to Neural Networks

1
Introduction to Neural Networks
2
Introduction to Perceptron
3
Neural Network Activation Functions
4
Cost Functions
5
Gradient Descent Backpropagation
6
TensorFlow Playground
7
Manual Creation of Neural Network - Part One
8
Manual Creation of Neural Network - Part Two - Operations
9
Manual Creation of Neural Network - Part Three - Placeholders and Variables
10
Manual Creation of Neural Network - Part Four - Session
11
Manual Neural Network Classification Task

TensorFlow Basics

1
Introduction to TensorFlow
2
TensorFlow Basic Syntax
3
TensorFlow Graphs
4
Variables and Placeholders
5
TensorFlow - A Neural Network - Part One
6
TensorFlow - A Neural Network - Part Two
7
TensorFlow Regression Example - Part One
8
TensorFlow Regression Example _ Part Two
9
TensorFlow Classification Example - Part One
10
TensorFlow Classification Example - Part Two
11
TF Regression Exercise
12
TF Regression Exercise Solution Walkthrough
13
TF Classification Exercise
14
TF Classification Exercise Solution Walkthrough
15
Saving and Restoring Models

Convolutional Neural Networks

1
Introduction to Convolutional Neural Network Section
2
Review of Neural Networks
3
New Theory Topics
4
Quick note on MNIST lecture
5
MNIST Data Overview
6
MNIST Basic Approach Part One
7
MNIST Basic Approach Part Two
8
CNN Theory Part One
9
CNN Theory Part Two
10
CNN MNIST Code Along - Part One
11
CNN MNIST Code Along - Part Two
12
Introduction to CNN Project
13
CNN Project Exercise Solution - Part One
14
CNN Project Exercise Solution - Part Two

Recurrent Neural Networks

1
Introduction to RNN Section
2
RNN Theory
3
Manual Creation of RNN
4
Vanishing Gradients
5
LSTM and GRU Theory
6
Introduction to RNN with TensorFlow API
7
RNN with TensorFlow - Part One
8
RNN with TensorFlow - Part Two
9
Quick Note on RNN Plotting Part 3
10
RNN with TensorFlow - Part Three
11
Time Series Exercise Overview
12
Time Series Exercise Solution
13
Quick Note on Word2Vec
14
Word2Vec Theory
15
Word2Vec Code Along - Part One
16
Word2Vec Part Two

Miscellaneous Topics

1
Intro to Miscellaneous Topics
2
Deep Nets with Tensorflow Abstractions API - Part One
3
Deep Nets with Tensorflow Abstractions API - Estimator API
4
Deep Nets with Tensorflow Abstractions API - Keras
5
Deep Nets with Tensorflow Abstractions API - Layers
6
Tensorboard

AutoEncoders

1
Autoencoder Basics
2
Dimensionality Reduction with Linear Autoencoder
3
Linear Autoencoder PCA Exercise Overview
4
Linear Autoencoder PCA Exercise Solutions
5
Stacked Autoencoder

Reinforcement Learning with OpenAI Gym

1
Introduction to Reinforcement Learning with OpenAI Gym
2
Extra Resources for Reinforcement Learning
3
Introduction to OpenAI Gym
4
OpenAI Gym Steup
5
Open AI Gym Env Basics
6
Open AI Gym Observations
7
OpenAI Gym Actions
8
Simple Neural Network Game
9
Policy Gradient Theory
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