Learning PathTensorflow The Road To Tensorflow-2Nd Edition
Last updated 7/2017
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.21 GB | Duration: 10h 21m
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.
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