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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!

Course Curriculum

01 Introduction
001 Introduction 00:00:00
002 Course Overview — PLEASE DON’T SKIP THIS LECTURE! Thanks _) 00:00:00
003 FAQ – Frequently Asked Questions 00:00:00
Downloads 00:00:00
02 Installation and Setup
004 Quick Note for MacOS and Linux Users 00:00:00
005 Installing TensorFlow Environment 00:00:00
03 What is Machine Learning_
006 Machine Learning Overview 00:00:00
04 Crash Course Overview
007 Crash Course Section Introduction 00:00:00
008 NumPy Crash Course 00:00:00
009 Pandas Crash Course 00:00:00
010 Data Visualization Crash Course 00:00:00
011 SciKit Learn Preprocessing Overview 00:00:00
012 Crash Course Review Exercise 00:00:00
013 Crash Course Review Exercise – Solutions 00:00:00
05 Introduction to Neural Networks
014 Introduction to Neural Networks 00:00:00
015 Introduction to Perceptron 00:00:00
016 Neural Network Activation Functions 00:00:00
017 Cost Functions 00:00:00
018 Gradient Descent Backpropagation 00:00:00
019 TensorFlow Playground 00:00:00
020 Manual Creation of Neural Network – Part One 00:00:00
021 Manual Creation of Neural Network – Part Two – Operations 00:00:00
022 Manual Creation of Neural Network – Part Three – Placeholders and Variables 00:00:00
023 Manual Creation of Neural Network – Part Four – Session 00:00:00
024 Manual Neural Network Classification Task 00:00:00
06 TensorFlow Basics
025 Introduction to TensorFlow 00:00:00
026 TensorFlow Basic Syntax 00:00:00
027 TensorFlow Graphs 00:00:00
028 Variables and Placeholders 00:00:00
029 TensorFlow – A Neural Network – Part One 00:00:00
030 TensorFlow – A Neural Network – Part Two 00:00:00
031 TensorFlow Regression Example – Part One 00:00:00
032 TensorFlow Regression Example _ Part Two 00:00:00
033 TensorFlow Classification Example – Part One 00:00:00
034 TensorFlow Classification Example – Part Two 00:00:00
035 TF Regression Exercise 00:00:00
036 TF Regression Exercise Solution Walkthrough 00:00:00
037 TF Classification Exercise 00:00:00
038 TF Classification Exercise Solution Walkthrough 00:00:00
039 Saving and Restoring Models 00:00:00
07 Convolutional Neural Networks
040 Introduction to Convolutional Neural Network Section 00:00:00
041 Review of Neural Networks 00:00:00
042 New Theory Topics 00:00:00
044 MNIST Data Overview 00:00:00
045 MNIST Basic Approach Part One 00:00:00
046 MNIST Basic Approach Part Two 00:00:00
047 CNN Theory Part One 00:00:00
048 CNN Theory Part Two 00:00:00
049 CNN MNIST Code Along – Part One 00:00:00
050 CNN MNIST Code Along – Part Two 00:00:00
051 Introduction to CNN Project 00:00:00
052 CNN Project Exercise Solution – Part One 00:00:00
053 CNN Project Exercise Solution – Part Two 00:00:00
08 Recurrent Neural Networks
054 Introduction to RNN Section 00:00:00
055 RNN Theory 00:00:00
056 Manual Creation of RNN 00:00:00
057 Vanishing Gradients 00:00:00
058 LSTM and GRU Theory 00:00:00
059 Introduction to RNN with TensorFlow API 00:00:00
060 RNN with TensorFlow – Part One 00:00:00
061 RNN with TensorFlow – Part Two 00:00:00
062 Quick Note on RNN Plotting Part 3 00:00:00
063 RNN with TensorFlow – Part Three 00:00:00
064 Time Series Exercise Overview 00:00:00
065 Time Series Exercise Solution 00:00:00
066 Quick Note on Word2Vec 00:00:00
067 Word2Vec Theory 00:00:00
068 Word2Vec Code Along – Part One 00:00:00
069 Word2Vec Part Two 00:00:00
09 Miscellaneous Topics
070 Intro to Miscellaneous Topics 00:00:00
071 Deep Nets with Tensorflow Abstractions API – Part One 00:00:00
072 Deep Nets with Tensorflow Abstractions API – Estimator API 00:00:00
073 Deep Nets with Tensorflow Abstractions API – Keras 00:00:00
074 Deep Nets with Tensorflow Abstractions API – Layers 00:00:00
10 AutoEncoders
076 Autoencoder Basics 00:00:00
077 Dimensionality Reduction with Linear Autoencoder 00:00:00
078 Linear Autoencoder PCA Exercise Overview 00:00:00
079 Linear Autoencoder PCA Exercise Solutions 00:00:00
080 Stacked Autoencoder 00:00:00
11 Reinforcement Learning with OpenAI Gym
081 Introduction to Reinforcement Learning with OpenAI Gym 00:00:00
082 Extra Resources for Reinforcement Learning 00:00:00
083 Introduction to OpenAI Gym 00:00:00
084 OpenAI Gym Steup 00:00:00
085 Open AI Gym Env Basics 00:00:00
086 Open AI Gym Observations 00:00:00
087 OpenAI Gym Actions 00:00:00
088 Simple Neural Network Game 00:00:00
089 Policy Gradient Theory 00:00:00
090 Policy Gradient Code Along Part One 00:00:00
091 Policy Gradient Code Along Part Two 00:00:00
12 GAN - Generative Adversarial Networks
092 Introduction to GANs 00:00:00
093 GAN Code Along – Part One 00:00:00
094 GAN Code Along – Part Two 00:00:00
095 GAN Code Along – Part Three 00:00:00
13 BONUS
096 Bonus Lecture_ Discounts for for My Other Courses 00:00:00
TAKE THIS COURSE
  • FREE
  • 10 Days
8 STUDENTS ENROLLED

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