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:

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:

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:

The VIP section includes:

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!

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 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.

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

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

The VIP section includes:

**DeepDream (great opportunity to practice implementing custom Tensorflow 2.0 models)****Object Localization (the first step toward Object Detection!)****Making predictions with a trained NLP model****Making recommendations with a trained recommender model**

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!

- Introduction (04:04) (FREE preview available)
- Outline (12:47)
- Where to get the code (05:36)

- Intro to Google Colab, how to use a GPU or TPU for free (12:33)
- Tensorflow 2.0 in Google Colab (07:55)
- Uploading your own data to Google Colab (11:42)
- Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn? (08:55)
- How to Succeed in this Course (05:52)

- What is Machine Learning? (14:26)
- Code Preparation (Classification Theory) (15:59)
- Beginner's Code Preamble (04:39)
- 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 (03:04)

- 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)

- 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)

- 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) (17:35)
- 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)

- 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)
- VIP: Making Predictions with a Trained NLP Model (05:02)

- Recommender Systems with Deep Learning Theory (13:10)
- Recommender Systems with Deep Learning Code (09:17)
- VIP: Making Predictions with a Trained Recommender Model (04:25)

- 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)

- GAN Theory (15:51)
- GAN Code (12:10)

- Reinforcement Learning Section Introduction (06:34)
- Elements of a Reinforcement Learning Problem (20:18)
- States, Actions, Rewards, Policies (09:24)
- Markov Decision Processes (MDPs) (10:07)
- The Return (04:56)
- Value Functions and the Bellman Equation (09:53)
- What does it mean to “learn”? (07:18)
- Solving the Bellman Equation with Reinforcement Learning (pt 1) (09:49)
- Solving the Bellman Equation with Reinforcement Learning (pt 2) (12:01)
- Epsilon-Greedy (06:09)
- Q-Learning (14:15)
- How to Learn Reinforcement Learning (05:56)

- Reinforcement Learning Stock Trader Introduction (05:14)
- Data and Environment (12:22)
- Replay Buffer (05:40)
- Program Design and Layout (06:56)
- Code pt 1 (05:46)
- Code pt 2 (09:40)
- Code pt 3 (06:27)
- Code pt 4 (07:25)
- Reinforcement Learning Stock Trader Discussion (03:36)
- Help! Why is the code slower on my machine? (08:20)

- What is a Web Service? (Tensorflow Serving pt 1) (05:55)
- Tensorflow Serving pt 2 (16:56)
- Tensorflow Lite (TFLite) (08:30)
- Why is Google the King of Distributed Computing? (08:47)
- Training with Distributed Strategies (07:00)

- Differences Between Tensorflow 1.x and Tensorflow 2.x (10:02)
- Constants and Basic Computation (09:39)
- Variables and Gradient Tape (12:59)
- Build Your Own Custom Model (10:47)

- DeepDream Theory (07:27)
- DeepDream Code Outline (pt 1) (07:55)
- DeepDream Code (pt 1) (16:48)
- DeepDream Code Outline (pt 2) (04:12)
- DeepDream Code (pt 2) (05:07)
- DeepDream Code Outline (pt 3) (06:46)
- DeepDream Code (pt 3) (07:55)

- Localization Introduction and Outline (13:37)
- Localization Code Outline (pt 1) (10:40)
- Localization Code (pt 1) (09:11)
- Localization Code Outline (pt 2) (04:53)
- Localization Code (pt 2) (11:03)
- Localization Code Outline (pt 3) (03:19)
- Localization Code (pt 3) (04:17)
- Localization Code Outline (pt 4) (03:20)
- Localization Code (pt 4) (02:07)
- Localization Code Outline (pt 5) (07:43)
- Localization Code (pt 5) (08:40)
- Localization Code Outline (pt 6) (07:07)
- Localization Code (pt 6) (07:38)
- Localization Code Outline (pt 7) (04:59)
- Localization Code (pt 7) (12:08)

- Mean Squared Error (09:11)
- Binary Cross Entropy (05:58)
- Categorical Cross Entropy (08:06)

- Gradient Descent (07:52)
- Stochastic Gradient Descent (04:36)
- Momentum (06:11)
- Variable and Adaptive Learning Rates (11:46)
- Adam Optimization (pt 1) (13:15)
- Adam Optimization (pt 2) (11:14)

- Windows-Focused Environment Setup 2018 (20:21)
- How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow (17:33)

- How to Code Yourself (part 1) (15:55)
- How to Code Yourself (part 2) (09:24)
- Proof that using Jupyter Notebook is the same as not using it (12:29)
- Python 2 vs Python 3 (04:38)
- Is Theano Dead? (10:04)

- How to Succeed in this Course (Long Version) (10:25)
- Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced? (22:05)
- What order should I take your courses in? (part 1) (11:19)
- What order should I take your courses in? (part 2) (16:07)

- What is the Appendix? (02:48)
- Where to get discount coupons and FREE deep learning material (05:31)

- Link to Regular Notebooks
- Link to VIP Notebooks