Tensorflow 2.0: Deep Learning and Artificial Intelligence
4.6 (3,069 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.
18,463 students enrolled

Tensorflow 2.0: Deep Learning and Artificial Intelligence

Neural Networks for Computer Vision, Time Series Forecasting, NLP, GANs, Reinforcement Learning, and More!
Bestseller
4.6 (3,069 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.
18,463 students enrolled
Last updated 7/2020
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Current price: $139.99 Original price: $199.99 Discount: 30% off
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This course includes
  • 21 hours on-demand video
  • 2 articles
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • Artificial Neural Networks (ANNs) / Deep Neural Networks (DNNs)
  • Predict Stock Returns
  • Time Series Forecasting
  • Computer Vision
  • How to build a Deep Reinforcement Learning Stock Trading Bot
  • GANs (Generative Adversarial Networks)
  • Recommender Systems
  • Image Recognition
  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs)
  • Use Tensorflow Serving to serve your model using a RESTful API
  • Use Tensorflow Lite to export your model for mobile (Android, iOS) and embedded devices
  • Use Tensorflow's Distribution Strategies to parallelize learning
  • Low-level Tensorflow, gradient tape, and how to build your own custom models
  • Natural Language Processing (NLP) with Deep Learning
  • Demonstrate Moore's Law using Code
  • Transfer Learning to create state-of-the-art image classifiers
Course content
Expand all 127 lectures 20:52:46
+ Welcome
3 lectures 22:26
Outline
12:47
Where to get the code
05:36
+ Google Colab
4 lectures 41:01
Tensorflow 2.0 in Google Colab
07:54
Uploading your own data to Google Colab
11:41
Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn?
08:54
+ Machine Learning and Neurons
11 lectures 01:36:44
What is Machine Learning?
14:26
Code Preparation (Classification Theory)
15:59
Beginner's Code Preamble
04:38
Classification Notebook
08:40
Code Preparation (Regression Theory)
07:19
Regression Notebook
10:34
The Neuron
09:58
How does a model "learn"?
10:54
Making Predictions
06:45
Saving and Loading a Model
04:28
Suggestion Box
03:03
+ Feedforward Artificial Neural Networks
9 lectures 01:36:21
Artificial Neural Networks Section Introduction
06:00
Forward Propagation
09:40
The Geometrical Picture
09:43
Activation Functions
17:18
Multiclass Classification
08:41
How to Represent Images
12:36
Code Preparation (ANN)
12:42
ANN for Image Classification
08:36
ANN for Regression
11:05
+ Convolutional Neural Networks
11 lectures 01:57:05
What is Convolution? (part 1)
16:38
What is Convolution? (part 2)
05:56
What is Convolution? (part 3)
06:41
Convolution on Color Images
15:58
CNN Architecture
20:58
CNN Code Preparation
15:13
CNN for Fashion MNIST
06:46
CNN for CIFAR-10
04:28
Data Augmentation
08:51
Batch Normalization
05:14
Improving CIFAR-10 Results
10:22
+ Recurrent Neural Networks, Time Series, and Sequence Data
18 lectures 03:12:29
Sequence Data
18:27
Forecasting
10:35
Autoregressive Linear Model for Time Series Prediction
12:01
Proof that the Linear Model Works
04:12
Recurrent Neural Networks
21:34
RNN Code Preparation
05:50
RNN for Time Series Prediction
11:11
Paying Attention to Shapes
08:27
GRU and LSTM (pt 1)
16:09
GRU and LSTM (pt 2)
11:36
A More Challenging Sequence
09:19
Demo of the Long Distance Problem
19:26
RNN for Image Classification (Theory)
04:41
RNN for Image Classification (Code)
04:00
Stock Return Predictions using LSTMs (pt 1)
12:03
Stock Return Predictions using LSTMs (pt 2)
05:45
Stock Return Predictions using LSTMs (pt 3)
11:59
Other Ways to Forecast
05:14
+ Natural Language Processing (NLP)
6 lectures 54:35
Embeddings
13:12
Code Preparation (NLP)
13:17
Text Preprocessing
05:30
Text Classification with LSTMs
08:19
CNNs for Text
08:07
Text Classification with CNNs
06:10
+ Recommender Systems
2 lectures 22:27
Recommender Systems with Deep Learning Theory
13:10
Recommender Systems with Deep Learning Code
09:17
+ Transfer Learning for Computer Vision
6 lectures 44:48
Transfer Learning Theory
08:12
Some Pre-trained Models (VGG, ResNet, Inception, MobileNet)
05:41
Large Datasets and Data Generators
07:03
2 Approaches to Transfer Learning
04:51
Transfer Learning Code (pt 1)
10:49
Transfer Learning Code (pt 2)
08:12
Requirements
  • Know how to code in Python and Numpy
  • For the theoretical parts (optional), understand derivatives and probability
Description

Welcome to Tensorflow 2.0!


What an exciting time. It's been nearly 4 years since Tensorflow was released, and the library has evolved to its official second version.

Tensorflow is Google's library for deep learning and artificial intelligence.

Deep Learning has been responsible for some amazing achievements recently, such as:

  • Generating beautiful, photo-realistic images of people and things that never existed (GANs)

  • Beating world champions in the strategy game Go, and complex video games like CS:GO and Dota 2 (Deep Reinforcement Learning)

  • Self-driving cars (Computer Vision)

  • Speech recognition (e.g. Siri) and machine translation (Natural Language Processing)

  • Even creating videos of people doing and saying things they never did (DeepFakes - a potentially nefarious application of deep learning)


Tensorflow is the world's most popular library for deep learning, and it's built by Google, whose parent Alphabet recently became the most cash-rich company in the world (just a few days before I wrote this). It is the library of choice for many companies doing AI and machine learning.

In other words, if you want to do deep learning, you gotta know Tensorflow.


This course is for beginner-level students all the way up to expert-level students. How can this be?

If you've just taken my free Numpy prerequisite, then you know everything you need to jump right in. We will start with some very basic machine learning models and advance to state of the art concepts.

Along the way, you will learn about all of the major deep learning architectures, such as Deep Neural Networks, Convolutional Neural Networks (image processing), and Recurrent Neural Networks (sequence data).

Current projects include:

  • Natural Language Processing (NLP)

  • Recommender Systems

  • Transfer Learning for Computer Vision

  • Generative Adversarial Networks (GANs)

  • Deep Reinforcement Learning Stock Trading Bot

Even if you've taken all of my previous courses already, you will still learn about how to convert your previous code so that it uses Tensorflow 2.0, and there are all-new and never-before-seen projects in this course such as time series forecasting and how to do stock predictions.

This course is designed for students who want to learn fast, but there are also "in-depth" sections in case you want to dig a little deeper into the theory (like what is a loss function, and what are the different types of gradient descent approaches).


Advanced Tensorflow topics include:

  • Deploying a model with Tensorflow Serving (Tensorflow in the cloud)

  • Deploying a model with Tensorflow Lite (mobile and embedded applications)

  • Distributed Tensorflow training with Distribution Strategies

  • Writing your own custom Tensorflow model

  • Converting Tensorflow 1.x code to Tensorflow 2.0

  • Constants, Variables, and Tensors

  • Eager execution

  • Gradient tape


Instructor's Note: This course focuses on breadth rather than depth, with less theory in favor of building more cool stuff. If you are looking for a more theory-dense course, this is not it. Generally, for each of these topics (recommender systems, natural language processing, reinforcement learning, computer vision, GANs, etc.) I already have courses singularly focused on those topics.


Thanks for reading, and I’ll see you in class!

Who this course is for:
  • Beginners to advanced students who want to learn about deep learning and AI in Tensorflow 2.0