Deep Learning with Python: Guide to Tensor Flow

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In this course you will learn to use Google’s TensorFlow framework to create artificial neural networks using the deep learning with python techniques.

Last updated on June 15, 2021 6:54 pm

Deep Learning with Python: Complete Guide to Tensor Flow

What does Tensor Flow mean?

Complete-Guide-to-TensorFlow-for-Deep-Learning-with-Python

Tensor Flow is a free software library focused on machine learning created by Google. Initially, it was released as part of the Apache 2.0 open-source license, Tensor Flow was originally developed by engineers and researchers of the Google Brain Team, mainly for internal use. It is considered the closed-source application Dist Belief’s successor and is presently used by Google for research and production purposes. Tensor Flow is considered the first serious implementation of a framework focused on deep learning.

What is Deep Learning?

Deep learning is also known as deep structured learning, part of a broader family of machineDeep Learning learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised, or unsupervised. Deep learning architectures such as deep neural networks, deep belief networks, recurrent neural networks, and convolutional neural networks have been applied to fields including computer vision, machine vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection, and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance.

“Machine learning is the science of getting computers to learn without being explicitly programmed”.

– Sebastian Thrun

About Python

Python is an interpreted, high-level, general-purpose programming language. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects.

Python is hugely typed and garbage-collected. It supports multiple programming paradigms, including structured object-oriented and functional programming. Python is often described as a “batteries included” language due to its comprehensive standard library.

About  this Course

In this course on tensor flow for deep learning with python, you will learn how to use Google’s Tensor Flow framework to create artificial neural networks for deep learning.  Also, you will learn with an easy-to-understand guide to the complexities of Google’s Tensor Flow framework.

What you will learn from this course

This deep learning with python course is designed to balance theory and practical implementation, with complete ‘Jupiter’ notebook guides of code and easy to reference slides and notes. And you will also have plenty of exercises to test your new skills along the way.

  • You will understand how Neural Networks Work.
  • Use Tensor Flow for Classification and Regression Tasks
  • Use Tensor Flow for Time Series Analysis with Recurrent Neural Networks
  • Learn how to conduct Reinforcement Learning with Open AI Gym
  • Become a Deep Learning Guru!
  • Build your own Neural Network from Scratch with Python
  • Use Tensor Flow for Image Classification with Convolutional Neural Networks
  • Use Tensor Flow for solving Unsupervised Learning Problems with Auto Encoders.
  • Create Generative Adversarial Networks with Tensor Flow

Syllabus

In this course on a complete guide to TensorFlow for deep learning with python, you will be having:

1. Course introduction

  • Introduction to the course on TensorFlow for deep learning with python.
  • How to install and set up and also machine learning.

2. Crash course overview

  • Crash course selection introduction and also skills about Numpy, Pandas, and Data visualization.

3. Introduction to neural network

  • Introduction to Neural network
  • Neural Network Activation Functions and cost function.
  • Gradient Descent Backpropagation.
  • Manual Creation of Neural Network – Part One, Part Two (operation), Part Three (placeholders and variables) and Part Four – Session.
  • Manual Neural Network Classification Task.

4. Tensor flow Basics

  • Introduction to Convolutional Neural Network Section.
  • A quick note on MNIST lecture.
  • MNIST Basic Approach Part One and Part Two.
  • CNN Theory Part One and Part Two.
  • CNN MNIST Code Along – Part One and Part Two
  • Introduction to CNN Project.
  • CNN Project Exercise Solution – Part One and Part Two.

5. Convolutional Neural Network

  • Introduction to Convolutional Neural Network Sections.
  • MNIST Basic Approach Part One and Part Two.
  • CNN Theory Part One and Part Two.
  • CNN MNIST Code Along – Part One and Part Two.
  • Introduction to CNN Project.
  • CNN Project Exercise Solution – Part One And Part Two

6. Recurrent Neural Networks

  • Introduction to RNN Section and RNN Theory.
  • Manual Creation of RNN.
  • Vanishing Gradients.
  • LSTM and GRU Theory.
  • Introduction to RNN with Tensor Flow API.
  • RNN with Tensor Flow – Part One, Part Two, and Part Three.
  • Quick Note on RNN Plotting Part 3.
  • Time Series Exercise Solution.
  • Quick Note on Word2Vec.
  • Word2Vec Code Along – Part One and Part Two.

7. Miscellaneous Topics

  • Introduction to Miscellaneous Topics.
  • Deep Nets with Tensor flow Abstractions API – Part One.
  • Deep Nets with Tensor flow Abstractions API – Estimator API, Keras, and Layers.
  • Tenso board.

8. Auto Encoders

  • Autoencoder Basics.
  • Dimensionality Reduction with Linear Autoencoder.
  • Linear Autoencoder PCA Exercise Overview and solution.

9. Reinforcement Learning  with open AI Gym

  • Introduction to Reinforcement Learning with Open AI Gym.
  • Extra Resources for Reinforcement Learning.
  • Introduction to Open AI Gym.
  • Open AI Gym Setup, Env Basics and Observations, Simple Neural Network Game, Policy Gradient Theory Code, Part One, and Part Two.

10. GAN Generative Adversarial Networks

  • Introduction to GANs.
  • GAN Code Along – Part One, Part Two, and Part Three.

Prerequisites:

  • You should be familiar with Some knowledge of programming (preferably Python)
  • Some basic knowledge of math (mean, standard deviation, etc.)

Note: Your review matters

If you have already done this course, kindly drop your review in our reviews section. It would help others to get useful information and better insight into the course offered.

FAQ

$194.99

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  • Udemy
  • Lazy Programmer Team Inc.
  • Online Course
  • Self-paced
  • Intermediate
  • 1-4 Weeks
  • Paid Course (Paid certificate)
  • English
  • Data Science Data Science with 'Python' Deep learning TensorFlow
Expert Score
9
Learner rating
9
PROS: Good instructors narration flow and technical expertise Easily understandable content with high efficiency.
CONS: It should focus on key parts of the tensor flow code. Need to spend more time on basic mathematical background knowledge. Matrix multiplication and some other pillars in linear algebra needs more information.

Description

Deep Learning with Python: Complete Guide to Tensor Flow

What does Tensor Flow mean?

Complete-Guide-to-TensorFlow-for-Deep-Learning-with-Python

Tensor Flow is a free software library focused on machine learning created by Google. Initially, it was released as part of the Apache 2.0 open-source license, Tensor Flow was originally developed by engineers and researchers of the Google Brain Team, mainly for internal use. It is considered the closed-source application Dist Belief’s successor and is presently used by Google for research and production purposes. Tensor Flow is considered the first serious implementation of a framework focused on deep learning.

What is Deep Learning?

Deep learning is also known as deep structured learning, part of a broader family of machineDeep Learning learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised, or unsupervised. Deep learning architectures such as deep neural networks, deep belief networks, recurrent neural networks, and convolutional neural networks have been applied to fields including computer vision, machine vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection, and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance.

“Machine learning is the science of getting computers to learn without being explicitly programmed”.

– Sebastian Thrun

About Python

Python is an interpreted, high-level, general-purpose programming language. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects.

Python is hugely typed and garbage-collected. It supports multiple programming paradigms, including structured object-oriented and functional programming. Python is often described as a “batteries included” language due to its comprehensive standard library.

About  this Course

In this course on tensor flow for deep learning with python, you will learn how to use Google’s Tensor Flow framework to create artificial neural networks for deep learning.  Also, you will learn with an easy-to-understand guide to the complexities of Google’s Tensor Flow framework.

What you will learn from this course

This deep learning with python course is designed to balance theory and practical implementation, with complete ‘Jupiter’ notebook guides of code and easy to reference slides and notes. And you will also have plenty of exercises to test your new skills along the way.

  • You will understand how Neural Networks Work.
  • Use Tensor Flow for Classification and Regression Tasks
  • Use Tensor Flow for Time Series Analysis with Recurrent Neural Networks
  • Learn how to conduct Reinforcement Learning with Open AI Gym
  • Become a Deep Learning Guru!
  • Build your own Neural Network from Scratch with Python
  • Use Tensor Flow for Image Classification with Convolutional Neural Networks
  • Use Tensor Flow for solving Unsupervised Learning Problems with Auto Encoders.
  • Create Generative Adversarial Networks with Tensor Flow

Syllabus

In this course on a complete guide to TensorFlow for deep learning with python, you will be having:

1. Course introduction

  • Introduction to the course on TensorFlow for deep learning with python.
  • How to install and set up and also machine learning.

2. Crash course overview

  • Crash course selection introduction and also skills about Numpy, Pandas, and Data visualization.

3. Introduction to neural network

  • Introduction to Neural network
  • Neural Network Activation Functions and cost function.
  • Gradient Descent Backpropagation.
  • Manual Creation of Neural Network – Part One, Part Two (operation), Part Three (placeholders and variables) and Part Four – Session.
  • Manual Neural Network Classification Task.

4. Tensor flow Basics

  • Introduction to Convolutional Neural Network Section.
  • A quick note on MNIST lecture.
  • MNIST Basic Approach Part One and Part Two.
  • CNN Theory Part One and Part Two.
  • CNN MNIST Code Along – Part One and Part Two
  • Introduction to CNN Project.
  • CNN Project Exercise Solution – Part One and Part Two.

5. Convolutional Neural Network

  • Introduction to Convolutional Neural Network Sections.
  • MNIST Basic Approach Part One and Part Two.
  • CNN Theory Part One and Part Two.
  • CNN MNIST Code Along – Part One and Part Two.
  • Introduction to CNN Project.
  • CNN Project Exercise Solution – Part One And Part Two

6. Recurrent Neural Networks

  • Introduction to RNN Section and RNN Theory.
  • Manual Creation of RNN.
  • Vanishing Gradients.
  • LSTM and GRU Theory.
  • Introduction to RNN with Tensor Flow API.
  • RNN with Tensor Flow – Part One, Part Two, and Part Three.
  • Quick Note on RNN Plotting Part 3.
  • Time Series Exercise Solution.
  • Quick Note on Word2Vec.
  • Word2Vec Code Along – Part One and Part Two.

7. Miscellaneous Topics

  • Introduction to Miscellaneous Topics.
  • Deep Nets with Tensor flow Abstractions API – Part One.
  • Deep Nets with Tensor flow Abstractions API – Estimator API, Keras, and Layers.
  • Tenso board.

8. Auto Encoders

  • Autoencoder Basics.
  • Dimensionality Reduction with Linear Autoencoder.
  • Linear Autoencoder PCA Exercise Overview and solution.

9. Reinforcement Learning  with open AI Gym

  • Introduction to Reinforcement Learning with Open AI Gym.
  • Extra Resources for Reinforcement Learning.
  • Introduction to Open AI Gym.
  • Open AI Gym Setup, Env Basics and Observations, Simple Neural Network Game, Policy Gradient Theory Code, Part One, and Part Two.

10. GAN Generative Adversarial Networks

  • Introduction to GANs.
  • GAN Code Along – Part One, Part Two, and Part Three.

Prerequisites:

  • You should be familiar with Some knowledge of programming (preferably Python)
  • Some basic knowledge of math (mean, standard deviation, etc.)

Note: Your review matters

If you have already done this course, kindly drop your review in our reviews section. It would help others to get useful information and better insight into the course offered.

FAQ

Specification:

  • Udemy
  • Lazy Programmer Team Inc.
  • Online Course
  • Self-paced
  • Intermediate
  • 1-4 Weeks
  • Paid Course (Paid certificate)
  • English
  • Data Science Data Science with 'Python' Deep learning TensorFlow

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Deep Learning with Python: Guide to Tensor Flow
Deep Learning with Python: Guide to Tensor Flow

$194.99

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