AI-Deep Learning
（座位有限，订满为止）
Overview
This course introduces you to deep learning: the state-of-the-art approach to building artificial intelligence algorithms. We cover the basic components of deep learning, what it means, how it works, and develop code necessary to build various algorithms such as deep convolutional networks, variational autoencoders, generative adversarial networks, and recurrent neural networks. A major focus of this course will be to not only understand how to build the necessary components of these algorithms, but also how to apply them for exploring creative applications. We'll see how to train a computer to recognize objects in an image and use this knowledge to drive new and interesting behaviors, from understanding the similarities and differences in large datasets and using them to self-organize, to understanding how to infinitely generate entirely new content or match the aesthetics or contents of another image.
Deep learning offers enormous potential for creative applications and in this course we interrogate what's possible. Through practical applications and guided homework assignments, you'll be expected to create datasets, develop and train neural networks, explore your own media collections using existing state-of-the-art deep nets, synthesize new content from generative algorithms, and understand deep learning's potential for creating entirely new aesthetics and new ways of interacting with large amounts of data.
To practice deep learning model, in the course, we will build following Deep learning models:
· Sales data trend and prediction model
· Fraud detection deep learning model
· Financial stock prediction deep learning model.
1. Introduction
Deep overview
What is TensorFlow
Using TensorFlow for AI Systems
A High-Level Overview
Summary
2. TensorFlow Working environment
Installing TensorFlow
Hello World
MNIST
Softmax Regression
Summary
3. Understanding TensorFlow Basics
Computation Graphs
What Is a Computation Graph?
The Benefits of Graph Computations
Graphs, Sessions, and Fetches
Creating a Graph
Creating a Session and Running It
Constructing and Managing Our Graph
Fetches
Flowing Tensors
Nodes Are Operations, Edges Are Tensor Objects
Data Types
Tensor Arrays and Shapes
Names
Variables, Placeholders, and Simple Optimization
Variables
Placeholders
Optimization
Summary
4. Convolutional Neural Networks
Introduction to CNNs
MNIST: Take II
Convolution
Pooling
Dropout
The Model
CIFAR10
Loading the CIFAR10 Dataset
Simple CIFAR10 Models
Summary
5. Working with Text and Sequences, and TensorBoard Visualization
The Importance of Sequence Data
Introduction to Recurrent Neural Networks
Vanilla RNN Implementation
TensorFlow Built-in RNN Functions
RNN for Text Sequences
Text Sequences
Supervised Word Embeddings
LSTM and Using Sequence Length
Training Embeddings and the LSTM Classifier
Summary
6. Word Vectors, Advanced RNN, and Embedding Visualization
Introduction to Word Embeddings
Word2vec
Skip-Grams
Embeddings in TensorFlow
The Noise-Contrastive Estimation (NCE) Loss Function
Learning Rate Decay
Training and Visualizing with TensorBoard
Checking Out Our Embeddings
Pretrained Embeddings, Advanced RNN
Bidirectional RNN and GRU Cells
Summary
7. TensorFlow Abstractions and Simplifications
Chapter Overview 113
High-Level Survey 115
contrib.learn 117
Linear Regression 118
DNN Classifier 120
FeatureColumn 123
Homemade CNN with contrib.learn 128
TFLearn
Installation
CNN
RNN
Keras
Pretrained models with TF-Slim
Summary
8. Queues, Threads, and Reading Data
The Input Pipeline
TFRecords
Writing with TFRecordWriter
Queues
Enqueuing and Dequeuing
Multithreading
Coordinator and QueueRunner
A Full Multithreaded Input Pipeline
tf.train.string_input_producer() and tf.TFRecordReader()
tf.train.shuffle_batch()
tf.train.start_queue_runners() and Wrapping Up
Summary
Distributed TensorFlow
Distributed Computing
Where Does the Parallelization Take Place?
What Is the Goal of Parallelization?
TensorFlow Elements
tf.app.flags
Clusters and Servers
Replicating a Computational Graph Across Devices
9. Exporting and Serving Models with TensorFlow
Saving and Exporting Our Model
Assigning Loaded Weights
The Saver Class
Introduction to TensorFlow Serving
Overview
Installation
Building and Exporting
Summary
10. Retail sales Deep Learning model
11. Fraud detection Deep learning model
12. Stock prediction Deep Learning model
