[Lazy Programmer Inc.] Tensorflow 2.0: Deep Learning and Artificial Intelligence

Robot · 13 Авг 2021

Neural Networks for Computer Vision, Time Series Forecasting, NLP, GANs, Reinforcement Learning, and More!

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

Google Colab 41:01

Intro to Google Colab, how to use a GPU or TPU for free 12:32
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 01:29:03

What is Machine Learning? 14:26
Code Preparation (Classification Theory) 15:59
Classification Notebook 08:40
Code Preparation (Regression Theory) 07:19
Regresion Notebook 10:34
The Neuron 09:58
How does a model "learn"?10:54
Making Predictions 06:45
aving and Loading a Model 04:28

Feedforward Artificial Neural Networks 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 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 03:07:15
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
Natural Language Processing (NLP) 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 22:27
Recommender Systems with Deep Learning Theory 13:10
Recommender Systems with Deep Learning Code 09:17
Transfer Learning for Computer Vision 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
GANs (Generative Adversarial Networks) 28:01
GAN Theory 15:51
GAN Code 12:10
Deep Reinforcement Learning (Theory) 02:21:10
Deep 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) 09:48
Solving the Bellman Equation with Reinforcement Learning (pt 2) 12:01
Epsilon-Greedy 06:09
Q-Learning 14:15
Deep Q-Learning / DQN (pt 1) 14:05
Deep Q-Learning / DQN (pt 2) 10:25
How to Learn Reinforcement Learning 05:57
Stock Trading Project with Deep Reinforcement Learning 01:03:06
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
Advanced Tensorflow Usage 47:43
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
Using the TPU 00:35
Low-Level Tensorflow 43:27
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
VIP: DeepDream 56:03
DeepDream Theory 07:26
DeepDream Code Outline (pt 1) 07:54
DeepDream Code (pt 1) 16:47
DeepDream Code Outline (pt 2) 04:11
DeepDream Code (pt 2) 05:06
DeepDream Code Outline (pt 3) 06:45
DeepDream Code (pt 3) 07:54
In-Depth: Loss Functions 23:15
Mean Squared Error 09:11
Binary Cross Entropy 05:58
Categorical Cross Entropy08:06
In-Depth: Gradient Descent 41:41
Gradient Descent 07:52
Stochastic Gradient Descent 04:36
Momentum 06:10
Variable and Adaptive Learning Rates 11:45
Adam 11:18
Extras 00:54
Links to TF2.0 Notebooks 00:54
+Appendix / FAQ 02:30:40
What is the Appendix? 02:48
Windows-Focused Environment Setup 2018 20:20
How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow 17:30
Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced? 22:04
How to Code Yourself (part 1) 15:54
How to Code Yourself (part 2) 09:23
Proof that using Jupyter Notebook is the same as not using it 12:29
How to Succeed in this Course (Long Version) 10:24
Is Theano Dead? 10:03
What order should I take your courses in? (part 1) 11:18
What order should I take your courses in? (part 2) 16:07
Bonus: Where to get discount coupons and FREE deep learning material 02:20
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

Temporary VIP Section (join NOW and get it before it's gone!):

DeepDream (3 exercises)

Instructor's Note: Since Tensorflow 2.0 is still in beta, some features are not yet finalized. Stay tuned!
Instructor's Note 2: 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!
Требования

Know how to code in Python and Numpy
For the theoretical parts, understand derivatives and probability