Last updated 7/2017MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: 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 bs 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 Eeering. 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 eeer 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 outlier detection, and other areas. Dan has also published research and presented findings at the Transportation Research Board and other acad journals. Shams Ul Azeem Shams Ul Azeem is an undergraduate student of NUST Islamabad, Pakistan, in Electrical Eeering. 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 ing 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 Sennt 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. HomePage:
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