Learning Path:Tensorflow: The Road To Tensorflow-2Nd Edition

Discover deep learning and machine learning with Python and TensorFlow

What you'll learn

Build Python packages to efficiently create reusable code

Become proficient at creating tools and utility programs in Python

Design and train a multilayer neural network with TensorFlow

Understand convolutional neural networks for image recognition

Create pipelines to deal with real-world input data

Set up and run cross domain-specific examples (economics, medicine, text classification, and advertising)

Learn how to go from concept to a production-ready machine learning setup/pipeline capable of real-world usage

Requirements

Requires a firm understanding of Python and the Python ecosystem.

Basic data science knowledge would be an added advantage

Description

Packt’s Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it.
It can be hard to get started with machine learning, particularly as new frameworks like TensorFlow start to gain traction across enterprise companies. TensorFlow is an open source software library for numerical computation using data flow graphs. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.


This Learning Path begins by covering a mastery on Python with a deep focus on unlocking Python’s secrets. We then move on to understand deep learning as implemented by Python and TensorFlow. Finally, we solve common commercial machine learning problems using TensorFlow.


If you have no prior exposure to one of the most important trends impacting how we do data science in the next few years, this Learning Path will help you get up to speed.


The goal of this Learning Path is to help you understand deep learning and machine learning by getting to know Python first and then TensorFlow.



This Learning Path is authored by some of the best in their fields.
About the Authors
Daniel Arbuckle

Daniel Arbuckle got his Ph.D. In Computer Science from the University of Southern California. He has published numerous papers, along with several books and video courses, and is both a teacher of computer science and a professional programmer.


Eder Santana

Eder Santana is a Ph.D. candidate in Electrical and Computer Engineering. After working for 3 years with kernel machines (SVMs, Information Theoretic Learning, and so on), Eder moved to the field of deep learning 2.5 years ago, when he started learning Theano, Caffe, and other machine learning frameworks. Now, Eder contributes to Keras, the deep learning library for Python. Besides deep learning, he also likes data visualization and teaches machine learning, either on online forums or as a teacher assistant.


Dan Van Boxel

Dan Van Boxel is a data scientist and machine learning engineer with over 10 years of experience. He is well-known for "Dan Does Data", a YouTube livestream demonstrating the power and pitfalls of neural networks. He has developed and applied novel statistical models of machine learning to topics such as accounting for truck traffic on highways, travel time outlier detection, and other areas. Dan has also published research and presented findings at the Transportation Research Board and other academic journals.


Shams Ul Azeem



Shams Ul Azeem is an undergraduate student of NUST Islamabad, Pakistan, in Electrical Engineering. He’s pursuing his career in machine learning, particularly in deep learning, by doing medical-related freelance projects with different companies.

Overview

Section 1: Mastering Python - Second Edition

Lecture 1 The Course Overview

Lecture 2 Python Basic Syntax and Block Structure

Lecture 3 Built-in Data Structures and Comprehensions

Lecture 4 First-Class Functions and Classes

Lecture 5 Extensive Standard Library

Lecture 6 New in Python 3.5

Lecture 7 Downloading and Installing Python

Lecture 8 Using the Command-Line and the Interactive Shell

Lecture 9 Installing Packages with pip

Lecture 10 Finding Packages in the Python Package Index

Lecture 11 Creating an Empty Package

Lecture 12 Adding Modules to the Package

Lecture 13 Importing One of the Package's Modules from Another

Lecture 14 Adding Static Data Files to the Package

Lecture 15 PEP 8 and Writing Readable Code

Lecture 16 Using Version Control

Lecture 17 Using venv to Create a Stable and Isolated Work Area

Lecture 18 Getting the Most Out of docstrings 1: PEP 257 and docutils

Lecture 19 Getting the Most Out of docstrings 2: doctest

Lecture 20 Making a Package Executable via python -m

Lecture 21 Handling Command-Line Arguments with argparse

Lecture 22 Interacting with the User

Lecture 23 Executing Other Programs with Subprocess

Lecture 24 Using Shell Scripts or Batch Files to Run Our Programs

Lecture 25 Using concurrent.futures

Lecture 26 Using Multiprocessing

Lecture 27 Understanding Why This Isn't Like Parallel Processing

Lecture 28 Using the asyncio Event Loop and Coroutine Scheduler

Lecture 29 Waiting for Data to Become Available

Lecture 30 Synchronizing Multiple Tasks

Lecture 31 Communicating Across the Network

Lecture 32 Using Function Decorators

Lecture 33 Function Annotations

Lecture 34 Class Decorators

Lecture 35 Metaclasses

Lecture 36 Context Managers

Lecture 37 Descriptors

Lecture 38 Understanding the Principles of Unit Testing

Lecture 39 Using the unittest Package

Lecture 40 Using unittest.mock

Lecture 41 Using unittest's Test Discovery

Lecture 42 Using Nose for Unified Test Discover and Reporting

Lecture 43 What Does Reactive Programming Mean?

Lecture 44 Building a Simple Reactive Programming Framework

Lecture 45 Using the Reactive Extensions for Python (RxPY)

Lecture 46 Microservices and the Advantages of Process Isolation

Lecture 47 Building a High-Level Microservice with Flask

Lecture 48 Building a Low-Level Microservice with nameko

Lecture 49 Advantages and Disadvantages of Compiled Code

Lecture 50 Accessing a Dynamic Library Using ctypes

Lecture 51 Interfacing with C Code Using Cython

Section 2: Deep Learning with Python

Lecture 52 The Course Overview

Lecture 53 What Is Deep Learning?

Lecture 54 Open Source Libraries for Deep Learning

Lecture 55 Deep Learning Hello World! Classifying the MNIST Data

Lecture 56 Introduction to Backpropagation

Lecture 57 Understanding Deep Learning with Theano

Lecture 58 Optimizing a Simple Model in Pure Theano

Lecture 59 Keras Behind the Scenes

Lecture 60 Fully Connected or Dense Layers

Lecture 61 Convolutional and Pooling Layers

Lecture 62 Large Scale Datasets, ImageNet, and Very Deep Neural Networks

Lecture 63 Loading Pre-trained Models with Theano

Lecture 64 Reusing Pre-trained Models in New Applications

Lecture 65 Theano "for" Loops – the "scan" Module

Lecture 66 Recurrent Layers

Lecture 67 Recurrent Versus Convolutional Layers

Lecture 68 Recurrent Networks –Training a Sentiment Analysis Model for Text

Lecture 69 Bonus Challenge – Automatic Image Captioning

Lecture 70 Captioning TensorFlow – Google's Machine Learning Library

Section 3: Deep Learning with TensorFlow

Lecture 71 The Course Overview

Lecture 72 Installing TensorFlow

Lecture 73 Simple Computations

Lecture 74 Logistic Regression Model Building

Lecture 75 Logistic Regression Training

Lecture 76 Basic Neural Nets

Lecture 77 Single Hidden Layer Model

Lecture 78 Single Hidden Layer Explained

Lecture 79 Multiple Hidden Layer Model

Lecture 80 Multiple Hidden Layer Results

Lecture 81 Convolutional Layer Motivation

Lecture 82 Convolutional Layer Application

Lecture 83 Pooling Layer Motivation

Lecture 84 Pooling Layer Application

Lecture 85 Deep CNN

Lecture 86 Deeper CNN

Lecture 87 Wrapping Up Deep CNN

Lecture 88 Introducing Recurrent Neural Networks

Lecture 89 skflow

Lecture 90 RNNs in skflow

Lecture 91 Research Evaluation

Lecture 92 The Future of TensorFlow

Section 4: Machine Learning with TensorFlow

Lecture 93 The Course Overview

Lecture 94 Introducing Deep Learning

Lecture 95 Installing TensorFlow on Mac OSX

Lecture 96 Installation on Windows – Pre-Reqeusite Virtual Machine Setup

Lecture 97 Installation on Windows/Linux

Lecture 98 The Hand-Written Letters Dataset

Lecture 99 Automating Data Preparation

Lecture 100 Understanding Matrix Conversions

Lecture 101 The Machine Learning Life Cycle

Lecture 102 Reviewing Outputs and Results

Lecture 103 Getting Started with TensorBoard

Lecture 104 TensorBoard Events and Histograms

Lecture 105 The Graph Explorer

Lecture 106 Our Previous Project on TensorBoard

Lecture 107 Fully Connected Neural Networks

Lecture 108 Convolutional Neural Networks

Lecture 109 Programming a CNN

Lecture 110 Using TensorBoard on Our CNN

Lecture 111 CNN Versus Fully Connected Network Performance

This course is ideal for Python professionals looking to familiarize themselves with deep learning and machine learning. No commercial domain knowledge is required but familiarity with Python and matrix math is expected.