Last updated 6/2022
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 8.27 GB | Duration: 19h 12m
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 Time 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 Downloading 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 Time Series – Part One
Lecture 86 RNN on a Time Series – Part Two
Lecture 87 RNN Exercise
Lecture 88 RNN Exercise – Solutions
Lecture 89 Bonus – Multivariate Time 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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