AI-Deep Learning

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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.

 

Additional, we will build model for stock prediction using Tensorflow.

 

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

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. Stock prediction using Tensorflow

 


 

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