Complete Guide to TensorFlow for Deep Learning with Python

About Course

Get started with Deep Learning with this **free** TensorFlow course. Learn how to use TensorFlow for building artificial neural networks. This course is offered for **free** by Udemy, and covers all the essentials of using TensorFlow.

This comprehensive course will teach you all the fundamentals of TensorFlow, a popular framework for deep learning. You’ll learn about 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.

With this **free** TensorFlow course, you’ll get access to:

Jupyter notebook guides for code
Easy-to-reference slides and notes
Plenty of exercises to test your skills.

Learn from a complete guide to TensorFlow and explore the latest techniques in deep learning. Start learning TensorFlow today for free and become a machine learning expert.

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

Introduction

A Message from the Professor
Introduction

03:04
Course Overview — PLEASE DON’T SKIP THIS LECTURE! Thanks )

09:24

Installation and Setup

What is Machine Learning

Crash Course Overview

Introduction to Neural Networks

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
TensorFlow Playground

08:48
Manual Creation of Neural Network – Part One

06:17
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

16:28

TensorFlow Basics

Introduction to TensorFlow

01:26
TensorFlow Basic Syntax

12:40
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

03:20
TF Regression Exercise Solution Walkthrough

12:34
TF Classification Exercise

04:26
TF Classification Exercise Solution Walkthrough

11:27
Saving and Restoring Models

05:54

Convolutional Neural Networks

Introduction to Convolutional Neural Network Section

00:49
Review of Neural Networks

02:32
New Theory Topics

14:50
MNIST Data Overview

04:46
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

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

20:50
RNN with TensorFlow – Part Two

19:00
RNN with TensorFlow – Part Three

08:01
Time Series Exercise Overview

07:03
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

AutoEncoders

Reinforcement Learning with OpenAI Gym

Introduction to Reinforcement Learning with OpenAI Gym

00:418
Introduction to OpenAI Gym

05:37
OpenAI Gym Steup

07:19
Open AI Gym Env Basics

05:41
Open AI Gym Observations

08:05
OpenAI Gym Actions

08:02
Simple Neural Network Game

16:20
Policy Gradient Theory

07:39
Policy Gradient Code Along Part One

11:25
Policy Gradient Code Along Part Two

12:22

GAN – Generative Adversarial Networks

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Student Ratings & Reviews

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Total 1 Rating

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1 Rating

Zain Ali Shah

1 year ago

This course is good

About Course

What Will You Learn?

Course Content

Introduction

A Message from the Professor

Introduction

Course Overview — PLEASE DON’T SKIP THIS LECTURE! Thanks )

Installation and Setup

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

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

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

Deep Nets with Tensorflow Abstractions API – Part One

Deep Nets with Tensorflow Abstractions API – Estimator API