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Development Data Science Deep Learning

Complete Guide to TensorFlow for Deep Learning with Python

Learn how to use Google's Deep Learning Framework - TensorFlow with Python! Solve problems with cutting edge techniques!
Rating: 4.5 out of 54.5 (15,822 ratings)
87,261 students
Created by Jose Portilla
Last updated 4/2020
English
English [Auto], French [Auto], 
30-Day Money-Back Guarantee

What you'll learn

  • Understand how Neural Networks Work
  • Build your own Neural Network from Scratch with Python
  • Use TensorFlow for Classification and Regression Tasks
  • Use TensorFlow for Image Classification with Convolutional Neural Networks
  • Use TensorFlow for Time Series Analysis with Recurrent Neural Networks
  • Use TensorFlow for solving Unsupervised Learning Problems with AutoEncoders
  • Learn how to conduct Reinforcement Learning with OpenAI Gym
  • Create Generative Adversarial Networks with TensorFlow
  • Become a Deep Learning Guru!
Curated for the Udemy for Business collection

Course content

13 sections • 96 lectures • 14h 9m total length

  • Preview03:04
  • Course Overview -- PLEASE DON'T SKIP THIS LECTURE! Thanks :)
    09:23
  • FAQ - Frequently Asked Questions
    00:10

  • Quick Note for MacOS and Linux Users
    00:54
  • Installing TensorFlow and Environment Setup
    Preview12:01

  • Machine Learning Overview
    Preview17:16

  • Crash Course Section Introduction
    01:12
  • NumPy Crash Course
    15:32
  • Pandas Crash Course
    04:23
  • Data Visualization Crash Course
    07:41
  • SciKit Learn Preprocessing Overview
    09:04
  • Preview02:07
  • Crash Course Review Exercise - Solutions
    05:58

  • Introduction to Neural Networks
    01:06
  • Introduction to Perceptron
    05:12
  • Neural Network Activation Functions
    06:30
  • Cost Functions
    03:40
  • Gradient Descent Backpropagation
    03:20
  • Preview08:48
  • Manual Creation of Neural Network - Part One
    06:16
  • Manual Creation of Neural Network - Part Two - Operations
    07:55
  • Manual Creation of Neural Network - Part Three - Placeholders and Variables
    08:57
  • Manual Creation of Neural Network - Part Four - Session
    09:48
  • Manual Neural Network Classification Task
    16:27

  • Introduction to TensorFlow
    01:26
  • TensorFlow Basic Syntax
    Preview12:40
  • TensorFlow Graphs
    05:48
  • Variables and Placeholders
    05:57
  • TensorFlow - A Neural Network - Part One
    07:47
  • TensorFlow - A Neural Network - Part Two
    19:50
  • TensorFlow Regression Example - Part One
    19:43
  • TensorFlow Regression Example _ Part Two
    22:04
  • TensorFlow Classification Example - Part One
    14:00
  • TensorFlow Classification Example - Part Two
    12:46
  • TF Regression Exercise
    Preview03:20
  • TF Regression Exercise Solution Walkthrough
    12:34
  • Preview04:26
  • TF Classification Exercise Solution Walkthrough
    11:27
  • Saving and Restoring Models
    05:54

  • Introduction to Convolutional Neural Network Section
    00:49
  • Review of Neural Networks
    02:32
  • New Theory Topics
    Preview14:50
  • Quick note on MNIST lecture
    00:05
  • Preview04:46
  • MNIST Basic Approach Part One
    08:29
  • MNIST Basic Approach Part Two
    16:47
  • CNN Theory Part One
    18:41
  • CNN Theory Part Two
    04:32
  • CNN MNIST Code Along - Part One
    17:25
  • CNN MNIST Code Along - Part Two
    06:05
  • Introduction to CNN Project
    06:01
  • CNN Project Exercise Solution - Part One
    15:25
  • CNN Project Exercise Solution - Part Two
    12:59

  • Introduction to RNN Section
    01:07
  • RNN Theory
    07:57
  • Manual Creation of RNN
    11:57
  • Vanishing Gradients
    04:37
  • LSTM and GRU Theory
    09:49
  • Introduction to RNN with TensorFlow API
    04:38
  • RNN with TensorFlow - Part One
    20:49
  • RNN with TensorFlow - Part Two
    19:00
  • Quick Note on RNN Plotting Part 3
    00:23
  • RNN with TensorFlow - Part Three
    08:01
  • Time Series Exercise Overview
    Preview07:03
  • Time Series Exercise Solution
    18:17
  • Quick Note on Word2Vec
    02:49
  • Word2Vec Theory
    12:02
  • Word2Vec Code Along - Part One
    16:47
  • Word2Vec Part Two
    13:11

  • Intro to Miscellaneous Topics
    00:14
  • Deep Nets with Tensorflow Abstractions API - Part One
    07:12
  • Deep Nets with Tensorflow Abstractions API - Estimator API
    07:25
  • Deep Nets with Tensorflow Abstractions API - Keras
    11:55
  • Deep Nets with Tensorflow Abstractions API - Layers
    11:02
  • Tensorboard
    16:07

  • Autoencoder Basics
    07:57
  • Dimensionality Reduction with Linear Autoencoder
    17:25
  • Linear Autoencoder PCA Exercise Overview
    01:44
  • Linear Autoencoder PCA Exercise Solutions
    07:51
  • Stacked Autoencoder
    19:32

Requirements

  • Some knowledge of programming (preferably Python)
  • Some basic knowledge of math (mean, standard deviation, etc..)

Description

Welcome to the Complete Guide to TensorFlow for Deep Learning with Python!

This course will guide you through how to use Google's TensorFlow framework to create artificial neural networks for deep learning! This course aims to give you an easy to understand guide to the complexities of Google's TensorFlow framework in a way that is easy to understand. Other courses and tutorials have tended to stay away from pure tensorflow and instead use abstractions that give the user less control. Here we present a course that finally serves as a complete guide to using the TensorFlow framework as intended, while showing you the latest techniques available in deep learning!

This course is designed to balance theory and practical implementation, with complete jupyter notebook guides of code and easy to reference slides and notes. We also have plenty of exercises to test your new skills along the way!

This course covers a variety of topics, including

  • Neural Network Basics
  • TensorFlow Basics
  • Artificial Neural Networks
  • Densely Connected Networks
  • Convolutional Neural Networks
  • Recurrent Neural Networks
  • AutoEncoders
  • Reinforcement Learning
  • OpenAI Gym
  • and much more!

There are many Deep Learning Frameworks out there, so why use TensorFlow?

TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. 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. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.

It is used by major companies all over the world, including Airbnb, Ebay, Dropbox, Snapchat, Twitter, Uber, SAP, Qualcomm, IBM, Intel, and of course, Google!

Become a machine learning guru today! We'll see you inside the course!

Who this course is for:

  • Python students eager to learn the latest Deep Learning Techniques with TensorFlow

Featured review

Anthony Lee
Anthony Lee
12 courses
4 reviews
Rating: 4.5 out of 5a year ago
I have taken two of Jose's classes and satisfied with him explaining the required basic knowledge of the field taught in the course. I understand and I am experienced with the basics of Python and the libraries he taught in his courses, however, listening to his course reassured my knowledge and helped me engraves the knowledge in my head. Jose also explains the basic concepts under the hood with easily understandable words, at least for me.

Instructor

Jose Portilla
Head of Data Science, Pierian Data Inc.
Jose Portilla
  • 4.6 Instructor Rating
  • 735,988 Reviews
  • 2,252,183 Students
  • 32 Courses

  Jose Marcial Portilla has a BS and MS in Mechanical Engineering from Santa Clara University and years of experience as a professional instructor and trainer for Data Science and programming. He has publications and patents in various fields such as microfluidics, materials science, and data science technologies. Over the course of his career he has developed a skill set in analyzing data and he hopes to use his experience in teaching and data science to help other people learn the power of programming the ability to analyze data, as well as present the data in clear and beautiful visualizations. Currently he works as the Head of Data Science for Pierian Data Inc. and provides in-person data science and python programming training courses to employees working at top companies, including General Electric, Cigna, The New York Times, Credit Suisse, McKinsey and many more. Feel free to contact him on LinkedIn for more information on in-person training sessions or group training sessions in Las Vegas, NV.

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