Complete Guide to TensorFlow for Deep Learning with Python

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 | 2.25 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

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