Tensorflow Bootcamp [certificate]

Complete Guide to TensorFlow for Deep Learning with Python!

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!

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!

What am I going to get from this course?

• 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

  • Introduction
  • Course Overview
  • FAQ - Frequently Asked Questions

• Installation and Setup

  • Quick Note for MacOS and Linux Users
  • Installing TensorFlow Environment

• 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

• 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 Classification Exercise
  • Saving and Restoring Models

• Convolutional Neural Networks

  • Introduction to Convolutional Neural Network Section
  • Review of Neural Networks
  • New Theory Topics
  • Quick note on MNIST lecture
  • 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
  • CNN Project Exercise

• 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
  • Quick Note on RNN Plotting Part 3
  • RNN with TensorFlow - Part Three
  • Time Series Exercise
  • Quick Note on Word2Vec
  • Word2Vec Theory
  • Word2Vec Code Along - Part One
  • Word2Vec Code Along - Part Two

• Miscellaneous Topics

  • Intro to Miscellaneous Topics
  • Deep Nets with Tensorflow Abstractions API - Part One
  • Deep Nets with Tensorflow Abstractions API - Estimator API
  • Deep Nets with Tensorflow Abstractions API - Keras
  • Deep Nets with Tensorflow Abstractions API - Layers
  • Tensorboard

• AutoEncoders

  • Autoencoder Basics
  • Dimensionality Reduction with Linear Autoencoder
  • Linear Autoencoder PCA Exercise
  • Stacked Autoencoder

• Reinforcement Learning with OpenAI Gym

  • Introduction to Reinforcement Learning with OpenAI Gym
  • Extra Resources for Reinforcement Learning
  • Introduction to OpenAI Gym
  • OpenAI Gym Setup
  • OpenAI Gym Env Basics
  • OpenAI Gym Observations
  • OpenAI Gym Actions
  • Simple Neural Network Game
  • Policy Gradient Theory
  • Policy Gradient Code Along Part One
  • Policy Gradient Code Along Part Two

• GAN - Generative Adversarial Networks

  • Introduction to GANs
  • GAN Code Along - Part One
  • GAN Code Along - Part Two
  • GAN Code Along - Part Three