AI-Deep Learning


Course Description:

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
  • 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

9. Distributed TensorFlow

  • Distributed Computing
  • Where Does the Parallelization Take Place?
  • What Is the Goal of Parallelization?
  • TensorFlow Elements
  • Clusters and Servers
  • Replicating a Computational Graph Across Devices

10. 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

11. Retail sales Deep Learning model

12. Fraud detection Deep learning model

12. Fraud detection Deep learning model

13. Stock prediction Deep Learning model

Join Now

负责人:Kevin Wang
联系方式:416-665-1888 Ext:1
微信号:mariasunvic12 mariasunvic12