Complete Guide to TensorFlow for Deep Learning with Python

Complete Guide to TensorFlow for Deep Learning with Python

English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 14 Hours | 2.26 GB

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!

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!

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!
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 Manual Creation of Neural Network – Part Four – Session
16 Manual Neural Network Classification Task
17 Introduction to Perceptron
18 Neural Network Activation Functions
19 Cost Functions
20 Gradient Descent Backpropagation
21 TensorFlow Playground
22 Manual Creation of Neural Network – Part One
23 Manual Creation of Neural Network – Part Two – Operations
24 Manual Creation of Neural Network – Part Three – Placeholders and Variables

TensorFlow Basics
25 Introduction to TensorFlow
26 TensorFlow Classification Example – Part Two
27 TF Regression Exercise
28 TF Regression Exercise Solution Walkthrough
29 TF Classification Exercise
30 TF Classification Exercise Solution Walkthrough
31 Saving and Restoring Models
32 TensorFlow Basic Syntax
33 TensorFlow Graphs
34 Variables and Placeholders
35 TensorFlow – A Neural Network – Part One
36 TensorFlow – A Neural Network – Part Two
37 TensorFlow Regression Example – Part One
38 TensorFlow Regression Example Part Two
39 TensorFlow Classification Example – Part One

Convolutional Neural Networks
40 Introduction to Convolutional Neural Network Section
41 CNN MNIST Code Along – Part One
42 CNN MNIST Code Along – Part Two
43 Introduction to CNN Project
44 CNN Project Exercise Solution – Part One
45 CNN Project Exercise Solution – Part Two
46 Review of Neural Networks
47 New Theory Topics
48 Quick note on MNIST lecture
49 MNIST Data Overview
50 MNIST Basic Approach Part One
51 MNIST Basic Approach Part Two
52 CNN Theory Part One
53 CNN Theory Part Two

Recurrent Neural Networks
54 Introduction to RNN Section
55 RNN with TensorFlow – Part Three
56 Time Series Exercise Overview
57 Time Series Exercise Solution
58 Quick Note on Word2Vec
59 Word2Vec Theory
60 Word2Vec Code Along – Part One
61 Word2Vec Part Two
62 RNN Theory
63 Manual Creation of RNN
64 Vanishing Gradients
65 LSTM and GRU Theory
66 Introduction to RNN with TensorFlow API
67 RNN with TensorFlow – Part One
68 RNN with TensorFlow – Part Two
69 Quick Note on RNN Plotting Part 3

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 Policy Gradient Code Along Part One
83 Policy Gradient Code Along Part Two
84 Extra Resources for Reinforcement Learning
85 Introduction to OpenAI Gym
86 OpenAI Gym Steup
87 Open AI Gym Env Basics
88 Open AI Gym Observations
89 OpenAI Gym Actions
90 Simple Neural Network Game
91 Policy Gradient Theory

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