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.聽Other courses and tutorials have tended to聽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聽Neural Networks

Recurrent Neural Networks

AutoEncoders

Reinforcement Learning

OpenAI聽Gym

and much more!

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

TensorFlow聽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 DONT SKIP THIS LECTURE! Thanks 馃檪

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

How long do I have access to the course materials?

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!