Complete Tensorflow 2 and Keras Deep Learning Bootcamp

Complete Tensorflow 2 and Keras Deep Learning Bootcamp, available at $119.99, has an average rating of 4.63, with 116 lectures, 1 quizzes, based on 8403 reviews, and has 52488 subscribers.

You will learn about 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 This course is ideal for individuals who are Python developers interested in learning about TensorFlow 2 for deep learning and artificial intelligence It is particularly useful for Python developers interested in learning about TensorFlow 2 for deep learning and artificial intelligence.

Enroll now: Complete Tensorflow 2 and Keras Deep Learning Bootcamp

Summary

Title: Complete Tensorflow 2 and Keras Deep Learning Bootcamp

Price: $119.99

Average Rating: 4.63

Number of Lectures: 116

Number of Quizzes: 1

Number of Published Lectures: 116

Number of Published Quizzes: 1

Number of Curriculum Items: 117

Number of Published Curriculum Objects: 117

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

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, including

NumPy Crash Course

Pandas Data Analysis Crash Course

Data Visualization Crash Course

Neural Network Basics

TensorFlow Basics

Keras Syntax Basics

Artificial Neural Networks

Densely Connected Networks

Convolutional Neural Networks

Recurrent Neural Networks

AutoEncoders

GANs – Generative Adversarial Networks

Deploying TensorFlow into Production

and 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 performance

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!

Become a deep learning guru today! We’ll see you inside the course!

Course Curriculum

Chapter 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

Chapter 2: COURSE OVERVIEW CONFIRMATION

Chapter 3: NumPy Crash Course

Lecture 1: Introduction to NumPy

Lecture 2: NumPy Arrays

Lecture 3: Numpy Index Selection

Lecture 4: NumPy Operations

Lecture 5: NumPy Exercises

Lecture 6: Numpy Exercises – Solutions

Chapter 4: Pandas Crash Course

Lecture 1: Introduction to Pandas

Lecture 2: Pandas Series

Lecture 3: Pandas DataFrames – Part One

Lecture 4: Pandas DataFrames – Part Two

Lecture 5: Pandas Missing Data

Lecture 6: GroupBy Operations

Lecture 7: Pandas Operations

Lecture 8: Data Input and Output

Lecture 9: Pandas Exercises

Lecture 10: Pandas Exercises – Solutions

Chapter 5: Visualization Crash Course

Lecture 1: Introduction to Python Visualization

Lecture 2: Matplotlib Basics

Lecture 3: Seaborn Basics

Lecture 4: Data Visualization Exercises

Lecture 5: Data Visualization Exercises – Solutions

Chapter 6: Machine Learning Concepts Overview

Lecture 1: What is Machine Learning?

Lecture 2: Supervised Learning Overview

Lecture 3: Overfitting

Lecture 4: Evaluating Performance – Classification Error Metrics

Lecture 5: Evaluating Performance – Regression Error Metrics

Lecture 6: Unsupervised Learning

Chapter 7: Basic Artificial Neural Networks – ANNs

Lecture 1: Introduction to ANN Section

Lecture 2: Perceptron Model

Lecture 3: Neural Networks

Lecture 4: Activation Functions

Lecture 5: Multi-Class Classification Considerations

Lecture 6: Cost Functions and Gradient Descent

Lecture 7: Backpropagation

Lecture 8: TensorFlow vs. Keras Explained

Lecture 9: Keras Syntax Basics – Part One – Preparing the Data

Lecture 10: Keras Syntax Basics – Part Two – Creating and Training the Model

Lecture 11: Keras Syntax Basics – Part Three – Model Evaluation

Lecture 12: Keras Regression Code Along – Exploratory Data Analysis

Lecture 13: Keras Regression Code Along – Exploratory Data Analysis – Continued

Lecture 14: Keras Regression Code Along – Data Preprocessing and Creating a Model

Lecture 15: Keras Regression Code Along – Model Evaluation and Predictions

Lecture 16: Keras Classification Code Along – EDA and Preprocessing

Lecture 17: Keras Classification – Dealing with Overfitting and Evaluation

Lecture 18: TensorFlow 2.0 Keras Project Options Overview

Lecture 19: TensorFlow 2.0 Keras Project Notebook Overview

Lecture 20: Keras Project Solutions – Exploratory Data Analysis

Lecture 21: Keras Project Solutions – Dealing with Missing Data

Lecture 22: Keras Project Solutions – Dealing with Missing Data – Part Two

Lecture 23: Keras Project Solutions – Categorical Data

Lecture 24: Keras Project Solutions – Data PreProcessing

Lecture 25: Keras Project Solutions – Creating and Training a Model

Lecture 26: Keras Project Solutions – Model Evaluation

Lecture 27: Tensorboard

Chapter 8: Convolutional Neural Networks – CNNs

Lecture 1: CNN Section Overview

Lecture 2: Image Filters and Kernels

Lecture 3: Convolutional Layers

Lecture 4: Pooling Layers

Lecture 5: MNIST Data Set Overview

Lecture 6: CNN on MNIST – Part One – The Data

Lecture 7: CNN on MNIST – Part Two – Creating and Training the Model

Lecture 8: CNN on MNIST – Part Three – Model Evaluation

Lecture 9: CNN on CIFAR-10 – Part One – The Data

Lecture 10: CNN on CIFAR-10 – Part Two – Evaluating the Model

Lecture 11: Downloading Data Set for Real Image Lectures

Lecture 12: CNN on Real Image Files – Part One – Reading in the Data

Lecture 13: CNN on Real Image Files – Part Two – Data Processing

Lecture 14: CNN on Real Image Files – Part Three – Creating the Model

Lecture 15: CNN on Real Image Files – Part Four – Evaluating the Model

Lecture 16: CNN Exercise Overview

Lecture 17: CNN Exercise Solutions

Chapter 9: Recurrent Neural Networks – RNNs

Lecture 1: RNN Section Overview

Lecture 2: RNN Basic Theory

Lecture 3: Vanishing Gradients

Lecture 4: LSTMS and GRU

Lecture 5: RNN Batches

Lecture 6: RNN on a Sine Wave – The Data

Lecture 7: RNN on a Sine Wave – Batch Generator

Lecture 8: RNN on a Sine Wave – Creating the Model

Lecture 9: RNN on a Sine Wave – LSTMs and Forecasting

Lecture 10: RNN on a Time Series – Part One

Lecture 11: RNN on a Time Series – Part Two

Lecture 12: RNN Exercise

Lecture 13: RNN Exercise – Solutions

Lecture 14: Bonus – Multivariate Time Series – RNN and LSTMs

Chapter 10: Natural Language Processing

Instructors

Jose Portilla
Head of Data Science at Pierian Training

Pierian Training
Data Science and Machine Learning Training

Rating Distribution

1 stars: 36 votes

2 stars: 52 votes

3 stars: 405 votes

4 stars: 2600 votes

5 stars: 5310 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!