**Complete Guide to TensorFlow for Deep Learning with Python**

English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 14 Hours | 2.25 GB

eLearning | Skill level: All Levels

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

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!

**+ Table of Contents**

**Introduction**

1 Introduction

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

3 FAQ – Frequently Asked Questions

**Installation and Setup**

4 Quick Note for MacOS and Linux Users

5 Installing TensorFlow Environment

**What is Machine Learning_**

6 Machine Learning Overview

**Crash Course Overview**

7 Crash Course Section Introduction

8 NumPy Crash Course

9 Pandas Crash Course

10 Data Visualization Crash Course

11 SciKit Learn Preprocessing Overview

12 Crash Course Review Exercise

13 Crash Course Review Exercise – Solutions

**Introduction to Neural Networks**

14 Introduction to Neural Networks

15 Introduction to Perceptron

16 Neural Network Activation Functions

17 Cost Functions

18 Gradient Descent Backpropagation

19 TensorFlow Playground

20 Manual Creation of Neural Network – Part One

21 Manual Creation of Neural Network – Part Two – Operations

22 Manual Creation of Neural Network – Part Three – Placeholders and Variables

23 Manual Creation of Neural Network – Part Four – Session

24 Manual Neural Network Classification Task

**TensorFlow Basics**

25 Introduction to TensorFlow

26 TensorFlow Basic Syntax

27 TensorFlow Graphs

28 Variables and Placeholders

29 TensorFlow – A Neural Network – Part One

30 TensorFlow – A Neural Network – Part Two

31 TensorFlow Regression Example – Part One

32 TensorFlow Regression Example _ Part Two

33 TensorFlow Classification Example – Part One

34 TensorFlow Classification Example – Part Two

35 TF Regression Exercise

36 TF Regression Exercise Solution Walkthrough

37 TF Classification Exercise

38 TF Classification Exercise Solution Walkthrough

39 Saving and Restoring Models

**Convolutional Neural Networks**

40 Introduction to Convolutional Neural Network Section

41 Review of Neural Networks

42 New Theory Topics

43 Quick note on MNIST lecture

44 MNIST Data Overview

45 MNIST Basic Approach Part One

46 MNIST Basic Approach Part Two

47 CNN Theory Part One

48 CNN Theory Part Two

49 CNN MNIST Code Along – Part One

50 CNN MNIST Code Along – Part Two

51 Introduction to CNN Project

52 CNN Project Exercise Solution – Part One

53 CNN Project Exercise Solution – Part Two

**Recurrent Neural Networks**

54 Introduction to RNN Section

55 RNN Theory

56 Manual Creation of RNN

57 Vanishing Gradients

58 LSTM and GRU Theory

59 Introduction to RNN with TensorFlow API

60 RNN with TensorFlow – Part One

61 RNN with TensorFlow – Part Two

62 Quick Note on RNN Plotting Part 3

63 RNN with TensorFlow – Part Three

64 Time Series Exercise Overview

65 Time Series Exercise Solution

66 Quick Note on Word2Vec

67 Word2Vec Theory

68 Word2Vec Code Along – Part One

69 Word2Vec Part Two

**Miscellaneous Topics**

70 Intro to Miscellaneous Topics

71 Deep Nets with Tensorflow Abstractions API – Part One

72 Deep Nets with Tensorflow Abstractions API – Estimator API

73 Deep Nets with Tensorflow Abstractions API – Keras

74 Deep Nets with Tensorflow Abstractions API – Layers

75 Tensorboard

**AutoEncoders**

76 Autoencoder Basics

77 Dimensionality Reduction with Linear Autoencoder

78 Linear Autoencoder PCA Exercise Overview

79 Linear Autoencoder PCA Exercise Solutions

80 Stacked Autoencoder

**Reinforcement Learning with OpenAI Gym**

81 Introduction to Reinforcement Learning with OpenAI Gym

82 Extra Resources for Reinforcement Learning

83 Introduction to OpenAI Gym

84 OpenAI Gym Steup

85 Open AI Gym Env Basics

86 Open AI Gym Observations

87 OpenAI Gym Actions

88 Simple Neural Network Game

89 Policy Gradient Theory

90 Policy Gradient Code Along Part One

91 Policy Gradient Code Along Part Two

**GAN – Generative Adversarial Networks**

92 Introduction to GANs

93 GAN Code Along – Part One

94 GAN Code Along – Part Two

95 GAN Code Along – Part Three

**BONUS**

96 Bonus Lecture_ Discounts for for My Other Courses