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Learn to use Python for Deep Learning with Google's latest Tensorflow 2 library and Keras!

What you'll learn

Learn to use TensorFlow 2.0 for Deep Learning

Leverage the Keras API to quickly build models that run on Tensorflow 2

Perform Image Classification with Convolutional Neural Networks

Use Deep Learning for medical imaging

Forecast Series data with Recurrent Neural Networks

Use Generative Adversarial Networks (GANs) to generate images

Use deep learning for style transfer

Generate text with RNNs and Natural Language Processing

Serve Tensorflow Models through an API

Use GPUs for accelerated deep learning

Requirements

Know how to code in Python

Some math basics such as derivatives

Description

This course will guide you through how to use Google's latest TensorFlow 2 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 2 framework in a way that is easy to understand.We'll focus on understanding the latest updates to TensorFlow and leveraging the Keras API (TensorFlow 2.0's official API) to quickly and easily build models. In this course we will build models to forecast future price homes, classify medical images, predict future sales data, generate complete new text artificially and much more!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, includingNumPy Crash CoursePandas Data Analysis Crash CourseData Visualization Crash CourseNeural Network BasicsTensorFlow BasicsKeras Syntax BasicsArtificial Neural NetworksDensely Connected NetworksConvolutional Neural NetworksRecurrent Neural NetworksAutoEncodersGANs - Generative Adversarial Networks Deploying TensorFlow into Productionand much more!Keras, a user-friendly API standard for machine learning, will be the central high-level API used to build and train models. The Keras API makes it easy to get started with TensorFlow 2. Importantly, Keras provides several model-building APIs (Sequential, Functional, and Subclassing), so you can choose the right level of abstraction for your project. TensorFlow’s implementation contains enhancements including eager execution, for immediate iteration and intuitive debugging, and tf.data, for building scalable input pipelines.TensorFlow 2 makes it easy to take new ideas from concept to code, and from model to publication. TensorFlow 2.0 incorporates a number of features that enables the definition and training of state of the art models without sacrificing speed or performanceIt 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 deep learning guru today! We'll see you inside the course!

Overview

Section 1: Course Overview, Installs, and Setup

Lecture 1 Auto-Welcome Message

Lecture 2 Course Overview

Lecture 3 Course Setup and Installation

Lecture 4 FAQ - Frequently Asked Questions

Section 2: COURSE OVERVIEW CONFIRMATION

Section 3: NumPy Crash Course

Lecture 5 Introduction to NumPy

Lecture 6 NumPy Arrays

Lecture 7 Numpy Index Selection

Lecture 8 NumPy Operations

Lecture 9 NumPy Exercises

Lecture 10 Numpy Exercises - Solutions

Section 4: Pandas Crash Course

Lecture 11 Introduction to Pandas

Lecture 12 Pandas Series

Lecture 13 Pandas DataFrames - Part One

Lecture 14 Pandas DataFrames - Part Two

Lecture 15 Pandas Missing Data

Lecture 16 GroupBy Operations

Lecture 17 Pandas Operations

Lecture 18 Data Input and Output

Lecture 19 Pandas Exercises

Lecture 20 Pandas Exercises - Solutions

Section 5: Visualization Crash Course

Lecture 21 Introduction to Python Visualization

Lecture 22 Matplotlib Basics

Lecture 23 Seaborn Basics

Lecture 24 Data Visualization Exercises

Lecture 25 Data Visualization Exercises - Solutions

Section 6: Machine Learning Concepts Overview

Lecture 26 What is Machine Learning?

Lecture 27 Supervised Learning Overview

Lecture 28 Overfitting

Lecture 29 Evaluating Performance - Classification Error Metrics

Lecture 30 Evaluating Performance - Regression Error Metrics

Lecture 31 Unsupervised Learning

Section 7: Basic Artificial Neural Networks - ANNs

Lecture 32 Introduction to ANN Section

Lecture 33 Perceptron Model

Lecture 34 Neural Networks

Lecture 35 Activation Functions

Lecture 36 Multi-Class Classification Considerations

Lecture 37 Cost Functions and Gradient Descent

Lecture 38 Backpropagation

Lecture 39 TensorFlow vs. Keras Explained

Lecture 40 Keras Syntax Basics - Part One - Preparing the Data

Lecture 41 Keras Syntax Basics - Part Two - Creating and Training the Model

Lecture 42 Keras Syntax Basics - Part Three - Model Evaluation

Lecture 43 Keras Regression Code Along - Exploratory Data Analysis

Lecture 44 Keras Regression Code Along - Exploratory Data Analysis - Continued

Lecture 45 Keras Regression Code Along - Data Preprocessing and Creating a Model

Lecture 46 Keras Regression Code Along - Model Evaluation and Predictions

Lecture 47 Keras Classification Code Along - EDA and Preprocessing

Lecture 48 Keras Classification - Dealing with Overfitting and Evaluation

Lecture 49 TensorFlow 2.0 Keras Project Options Overview

Lecture 50 TensorFlow 2.0 Keras Project Notebook Overview

Lecture 51 Keras Project Solutions - Exploratory Data Analysis

Lecture 52 Keras Project Solutions - Dealing with Missing Data

Lecture 53 Keras Project Solutions - Dealing with Missing Data - Part Two

Lecture 54 Keras Project Solutions - Categorical Data

Lecture 55 Keras Project Solutions - Data PreProcessing

Lecture 56 Keras Project Solutions - Creating and Training a Model

Lecture 57 Keras Project Solutions - Model Evaluation

Lecture 58 Tensorboard

Section 8: Convolutional Neural Networks - CNNs

Lecture 59 CNN Section Overview

Lecture 60 Image Filters and Kernels

Lecture 61 Convolutional Layers

Lecture 62 Pooling Layers

Lecture 63 MNIST Data Set Overview

Lecture 64 CNN on MNIST - Part One - The Data

Lecture 65 CNN on MNIST - Part Two - Creating and Training the Model

Lecture 66 CNN on MNIST - Part Three - Model Evaluation

Lecture 67 CNN on CIFAR-10 - Part One - The Data

Lecture 68 CNN on CIFAR-10 - Part Two - Evaluating the Model

Lecture 69 ing Data Set for Real Image Lectures

Lecture 70 CNN on Real Image Files - Part One - Reading in the Data

Lecture 71 CNN on Real Image Files - Part Two - Data Processing

Lecture 72 CNN on Real Image Files - Part Three - Creating the Model

Lecture 73 CNN on Real Image Files - Part Four - Evaluating the Model

Lecture 74 CNN Exercise Overview

Lecture 75 CNN Exercise Solutions

Section 9: Recurrent Neural Networks - RNNs

Lecture 76 RNN Section Overview

Lecture 77 RNN Basic Theory

Lecture 78 Vanishing Gradients

Lecture 79 LSTMS and GRU

Lecture 80 RNN Batches

Lecture 81 RNN on a Sine Wave - The Data

Lecture 82 RNN on a Sine Wave - Batch Generator

Lecture 83 RNN on a Sine Wave - Creating the Model

Lecture 84 RNN on a Sine Wave - LSTMs and Forecasting

Lecture 85 RNN on a Series - Part One

Lecture 86 RNN on a Series - Part Two

Lecture 87 RNN Exercise

Lecture 88 RNN Exercise - Solutions

Lecture 89 Bonus - Multivariate Series - RNN and LSTMs

Section 10: Natural Language Processing

Lecture 90 Introduction to NLP Section

Lecture 91 NLP - Part One - The Data

Lecture 92 NLP - Part Two - Text Processing

Lecture 93 NLP - Part Three - Creating Batches

Lecture 94 NLP - Part Four - Creating the Model

Lecture 95 NLP - Part Five - Training the Model

Lecture 96 NLP - Part Six - Generating Text

Section 11: AutoEncoders

Lecture 97 Introduction to Autoencoders

Lecture 98 Autoencoder Basics

Lecture 99 Autoencoder for Dimensionality Reduction

Lecture 100 Autoencoder for Images - Part One

Lecture 101 Autoencoder for Images - Part Two - Noise Removal

Lecture 102 Autoencoder Exercise Overview

Lecture 103 Autoencoder Exercise - Solutions

Section 12: Generative Adversarial Networks

Lecture 104 GANs Overview

Lecture 105 Creating a GAN - Part One- The Data

Lecture 106 Creating a GAN - Part Two - The Model

Lecture 107 Creating a GAN - Part Three - Model Training

Lecture 108 DCGAN - Deep Convolutional Generative Adversarial Networks

Section 13: Deployment

Lecture 109 Introduction to Deployment

Lecture 110 Creating the Model

Lecture 111 Model Prediction Function

Lecture 112 Running a Basic Flask Application

Lecture 113 Flask Postman API

Lecture 114 Flask API - Using Requests Programmatically

Lecture 115 Flask Front End

Lecture 116 Live Deployment to the Web

Python developers interested in learning about TensorFlow 2 for deep learning and artificial intelligence

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