Complete Tensorflow 2 and Keras Deep Learning Bootcamp
4.7 (1,935 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
13,958 students enrolled

Complete Tensorflow 2 and Keras Deep Learning Bootcamp

Learn to use Python for Deep Learning with Google's latest Tensorflow 2 library and Keras!
Highest Rated
4.7 (1,935 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
13,958 students enrolled
Created by Jose Portilla
Last updated 1/2020
English
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Current price: $129.99 Original price: $199.99 Discount: 35% off
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This course includes
  • 19 hours on-demand video
  • 1 article
  • 3 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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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
Course content
Expand all 115 lectures 19:10:59
+ Course Overview, Installs, and Setup
3 lectures 29:31
Course Setup and Installation
21:49
FAQ - Frequently Asked Questions
03:04
+ COURSE OVERVIEW CONFIRMATION
0 lectures 00:00

PLEASE WATCH COURSE OVERVIEW LECTURE

PLEASE WATCH COURSE OVERVIEW LECTURE
1 question
+ NumPy Crash Course
6 lectures 49:02
Numpy Index Selection
11:06
NumPy Operations
08:14
NumPy Exercises
01:18
Numpy Exercises - Solutions
07:05
+ Pandas Crash Course
10 lectures 01:28:50
Introduction to Pandas
03:57
Pandas Series
08:40
Pandas DataFrames - Part One
11:14
Pandas DataFrames - Part Two
09:32
Pandas Missing Data
10:07
GroupBy Operations
09:40
Pandas Operations
13:51
Data Input and Output
11:51
Pandas Exercises
02:41
Pandas Exercises - Solutions
07:17
+ Visualization Crash Course
5 lectures 37:40
Introduction to Python Visualization
01:16
Matplotlib Basics
09:02
Seaborn Basics
16:45
Data Visualization Exercises
02:59
Data Visualization Exercises - Solutions
07:38
+ Machine Learning Concepts Overview
6 lectures 48:37
What is Machine Learning?
05:20
Supervised Learning Overview
08:21
Overfitting
07:59
Evaluating Performance - Classification Error Metrics
16:37
Evaluating Performance - Regression Error Metrics
05:36
Unsupervised Learning
04:44
+ Basic Artificial Neural Networks - ANNs
27 lectures 05:01:21
Introduction to ANN Section
02:15
Perceptron Model
10:39
Neural Networks
07:19
Activation Functions
10:39
Cost Functions and Gradient Descent
18:13
Backpropagation
14:47
TensorFlow vs. Keras Explained
02:13
Keras Syntax Basics - Part One - Preparing the Data
10:49
Keras Syntax Basics - Part Two - Creating and Training the Model
13:59
Keras Syntax Basics - Part Three - Model Evaluation
12:56
Keras Regression Code Along - Exploratory Data Analysis
18:50
Keras Regression Code Along - Exploratory Data Analysis - Continued
13:15
Keras Regression Code Along - Data Preprocessing and Creating a Model
08:42
Keras Regression Code Along - Model Evaluation and Predictions
11:23
Keras Classification Code Along - EDA and Preprocessing
08:05
Keras Classification - Dealing with Overfitting and Evaluation
16:50
TensorFlow 2.0 Keras Project Options Overview
01:40
TensorFlow 2.0 Keras Project Notebook Overview
07:41
Keras Project Solutions - Exploratory Data Analysis
20:35
Keras Project Solutions - Dealing with Missing Data
14:46
Keras Project Solutions - Dealing with Missing Data - Part Two
12:02
Keras Project Solutions - Categorical Data
17:23
Keras Project Solutions - Data PreProcessing
03:45
Keras Project Solutions - Creating and Training a Model
03:57
Keras Project Solutions - Model Evaluation
09:42
Tensorboard
18:22
+ Convolutional Neural Networks - CNNs
17 lectures 02:42:09
CNN Section Overview
01:33
Image Filters and Kernels
11:35
Convolutional Layers
14:01
Pooling Layers
06:47
MNIST Data Set Overview
04:41
CNN on MNIST - Part One - The Data
12:57
CNN on MNIST - Part Two - Creating and Training the Model
16:14
CNN on MNIST - Part Three - Model Evaluation
06:53
CNN on CIFAR-10 - Part One - The Data
11:23
CNN on CIFAR-10 - Part Two - Evaluating the Model
07:05
Downloading Data Set for Real Image Lectures
05:22
CNN on Real Image Files - Part One - Reading in the Data
14:54
CNN on Real Image Files - Part Two - Data Processing
15:37
CNN on Real Image Files - Part Three - Creating the Model
13:37
CNN Exercise Overview
02:10
CNN Exercise Solutions
08:31
+ Recurrent Neural Networks - RNNs
14 lectures 02:35:22
RNN Section Overview
02:38
RNN Basic Theory
07:41
Vanishing Gradients
06:47
LSTMS and GRU
11:23
RNN Batches
07:49
RNN on a Sine Wave - The Data
08:29
RNN on a Sine Wave - Batch Generator
08:15
RNN on a Sine Wave - Creating the Model
15:20
RNN on a Sine Wave - LSTMs and Forecasting
13:24
RNN on a Time Series - Part One
09:49
RNN on a Time Series - Part Two
21:37
RNN Exercise - Solutions
21:49
Bonus - Multivariate Time Series - RNN and LSTMs
16:12
+ Natural Language Processing
7 lectures 57:42
Introduction to NLP Section
05:58
NLP - Part One - The Data
04:31
NLP - Part Two - Text Processing
04:34
NLP - Part Three - Creating Batches
13:10
NLP - Part Four - Creating the Model
10:23
NLP - Part Five - Training the Model
09:48
NLP - Part Six - Generating Text
09:18
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, 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!

Who this course is for:
  • Python developers interested in learning about TensorFlow 2 for deep learning and artificial intelligence