Complete Guide to TensorFlow for Deep Learning with Python
Complete Guide to TensorFlow for Deep Learning with Python, available at $94.99, has an average rating of 4.38, with 108 lectures, based on 16928 reviews, and has 97215 subscribers.
You will learn about 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! This course is ideal for individuals who are Python students eager to learn the latest Deep Learning Techniques with TensorFlow It is particularly useful for Python students eager to learn the latest Deep Learning Techniques with TensorFlow.
Enroll now: Complete Guide to TensorFlow for Deep Learning with Python
Summary
Title: Complete Guide to TensorFlow for Deep Learning with Python
Price: $94.99
Average Rating: 4.38
Number of Lectures: 108
Number of Published Lectures: 96
Number of Curriculum Items: 108
Number of Published Curriculum Objects: 96
Original Price: $189.99
Quality Status: approved
Status: Live
What You Will 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!
Who Should Attend
- Python students eager to learn the latest Deep Learning Techniques with TensorFlow
Target Audiences
- Python students eager to learn the latest Deep Learning Techniques with TensorFlow
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!
Course Curriculum
Chapter 1: Introduction
Lecture 1: Introduction
Lecture 2: Course Overview — PLEASE DON'T SKIP THIS LECTURE! Thanks 🙂
Lecture 3: FAQ – Frequently Asked Questions
Chapter 2: Installation and Setup
Lecture 1: Quick Note for MacOS and Linux Users
Lecture 2: Installing TensorFlow and Environment Setup
Chapter 3: What is Machine Learning?
Lecture 1: Machine Learning Overview
Chapter 4: Crash Course Overview
Lecture 1: Crash Course Section Introduction
Lecture 2: NumPy Crash Course
Lecture 3: Pandas Crash Course
Lecture 4: Data Visualization Crash Course
Lecture 5: SciKit Learn Preprocessing Overview
Lecture 6: Crash Course Review Exercise
Lecture 7: Crash Course Review Exercise – Solutions
Chapter 5: Introduction to Neural Networks
Lecture 1: Introduction to Neural Networks
Lecture 2: Introduction to Perceptron
Lecture 3: Neural Network Activation Functions
Lecture 4: Cost Functions
Lecture 5: Gradient Descent Backpropagation
Lecture 6: TensorFlow Playground
Lecture 7: Manual Creation of Neural Network – Part One
Lecture 8: Manual Creation of Neural Network – Part Two – Operations
Lecture 9: Manual Creation of Neural Network – Part Three – Placeholders and Variables
Lecture 10: Manual Creation of Neural Network – Part Four – Session
Lecture 11: Manual Neural Network Classification Task
Chapter 6: TensorFlow Basics
Lecture 1: Introduction to TensorFlow
Lecture 2: TensorFlow Basic Syntax
Lecture 3: TensorFlow Graphs
Lecture 4: Variables and Placeholders
Lecture 5: TensorFlow – A Neural Network – Part One
Lecture 6: TensorFlow – A Neural Network – Part Two
Lecture 7: TensorFlow Regression Example – Part One
Lecture 8: TensorFlow Regression Example _ Part Two
Lecture 9: TensorFlow Classification Example – Part One
Lecture 10: TensorFlow Classification Example – Part Two
Lecture 11: TF Regression Exercise
Lecture 12: TF Regression Exercise Solution Walkthrough
Lecture 13: TF Classification Exercise
Lecture 14: TF Classification Exercise Solution Walkthrough
Lecture 15: Saving and Restoring Models
Chapter 7: Convolutional Neural Networks
Lecture 1: Introduction to Convolutional Neural Network Section
Lecture 2: Review of Neural Networks
Lecture 3: New Theory Topics
Lecture 4: Quick note on MNIST lecture
Lecture 5: MNIST Data Overview
Lecture 6: MNIST Basic Approach Part One
Lecture 7: MNIST Basic Approach Part Two
Lecture 8: CNN Theory Part One
Lecture 9: CNN Theory Part Two
Lecture 10: CNN MNIST Code Along – Part One
Lecture 11: CNN MNIST Code Along – Part Two
Lecture 12: Introduction to CNN Project
Lecture 13: CNN Project Exercise Solution – Part One
Lecture 14: CNN Project Exercise Solution – Part Two
Chapter 8: Recurrent Neural Networks
Lecture 1: Introduction to RNN Section
Lecture 2: RNN Theory
Lecture 3: Manual Creation of RNN
Lecture 4: Vanishing Gradients
Lecture 5: LSTM and GRU Theory
Lecture 6: Introduction to RNN with TensorFlow API
Lecture 7: RNN with TensorFlow – Part One
Lecture 8: RNN with TensorFlow – Part Two
Lecture 9: Quick Note on RNN Plotting Part 3
Lecture 10: RNN with TensorFlow – Part Three
Lecture 11: Time Series Exercise Overview
Lecture 12: Time Series Exercise Solution
Lecture 13: Quick Note on Word2Vec
Lecture 14: Word2Vec Theory
Lecture 15: Word2Vec Code Along – Part One
Lecture 16: Word2Vec Part Two
Chapter 9: Miscellaneous Topics
Lecture 1: Intro to Miscellaneous Topics
Lecture 2: Deep Nets with Tensorflow Abstractions API – Part One
Lecture 3: Deep Nets with Tensorflow Abstractions API – Estimator API
Lecture 4: Deep Nets with Tensorflow Abstractions API – Keras
Lecture 5: Deep Nets with Tensorflow Abstractions API – Layers
Lecture 6: Tensorboard
Chapter 10: AutoEncoders
Lecture 1: Autoencoder Basics
Lecture 2: Dimensionality Reduction with Linear Autoencoder
Lecture 3: Linear Autoencoder PCA Exercise Overview
Lecture 4: Linear Autoencoder PCA Exercise Solutions
Lecture 5: Stacked Autoencoder
Chapter 11: Reinforcement Learning with OpenAI Gym
Lecture 1: Introduction to Reinforcement Learning with OpenAI Gym
Lecture 2: Extra Resources for Reinforcement Learning
Lecture 3: Introduction to OpenAI Gym
Lecture 4: OpenAI Gym Steup
Lecture 5: Open AI Gym Env Basics
Lecture 6: Open AI Gym Observations
Lecture 7: OpenAI Gym Actions
Lecture 8: Simple Neural Network Game
Lecture 9: Policy Gradient Theory
Instructors
-
Jose Portilla
Head of Data Science at Pierian Training -
Pierian Training
Data Science and Machine Learning Training
Rating Distribution
- 1 stars: 192 votes
- 2 stars: 274 votes
- 3 stars: 1564 votes
- 4 stars: 5887 votes
- 5 stars: 9010 votes
Frequently Asked Questions
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You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
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