Complete Guide to TensorFlow for Deep Learning with Python from Udemy

What's inside

Learning objectives

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!

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!

Syllabus

Introduction

Course Overview -- PLEASE DON'T SKIP THIS LECTURE! Thanks :)

FAQ - Frequently Asked Questions

Let's get your computer set-up!

Quick Note for MacOS and Linux Users

Learn how to install Tensorflow on your computer and setup using our environment file.

Get an overview of Artificial Neural Networks and Deep Learning

Machine Learning Overview

Let's briefly get a refresher of the libraries used in this course!

Crash Course Section Introduction

NumPy Crash Course

Pandas Crash Course

Data Visualization Crash Course

SciKit Learn Preprocessing Overview

Crash Course Review Exercise

Crash Course Review Exercise - Solutions

Learn the basics of Neural Networks and create your own with Python!

Introduction to Neural Networks

Introduction to Perceptron

Neural Network Activation Functions

Cost Functions

Gradient Descent Backpropagation

TensorFlow Playground

Manual Creation of Neural Network - Part One

Manual Creation of Neural Network - Part Two - Operations

Manual Creation of Neural Network - Part Three - Placeholders and Variables

Manual Creation of Neural Network - Part Four - Session

Manual Neural Network Classification Task

Let's get familiar with the basics of TensorFlow!

Introduction to TensorFlow

TensorFlow Basic Syntax

TensorFlow Graphs

Variables and Placeholders

TensorFlow - A Neural Network - Part One

TensorFlow - A Neural Network - Part Two

TensorFlow Regression Example - Part One

TensorFlow Regression Example _ Part Two

TensorFlow Classification Example - Part One

TensorFlow Classification Example - Part Two

TF Regression Exercise

TF Regression Exercise Solution Walkthrough

TF Classification Exercise

TF Classification Exercise Solution Walkthrough

Saving and Restoring Models

Now that we understand a basic Neural Network, let's explore Convolutional Neural Networks!

Introduction to Convolutional Neural Network Section

Review of Neural Networks

New Theory Topics

Quick note on MNIST lecture

MNIST Data Overview

MNIST Basic Approach Part One

MNIST Basic Approach Part Two

CNN Theory Part One

CNN Theory Part Two

CNN MNIST Code Along - Part One

CNN MNIST Code Along - Part Two

Introduction to CNN Project

CNN Project Exercise Solution - Part One

CNN Project Exercise Solution - Part Two

Learn how to use Recurrent Neural Networks to perform analysis on Sequence Data!

Introduction to RNN Section

RNN Theory

Manual Creation of RNN

Vanishing Gradients

LSTM and GRU Theory

Introduction to RNN with TensorFlow API

RNN with TensorFlow - Part One

RNN with TensorFlow - Part Two

Quick Note on RNN Plotting Part 3

RNN with TensorFlow - Part Three

Time Series Exercise Overview

Time Series Exercise Solution

Quick Note on Word2Vec

Word2Vec Theory

Word2Vec Code Along - Part One

Word2Vec Part Two

Let's take a quick break to talk about some extra topics, such as loading models, GPU Acceleration, and more!

Intro to Miscellaneous Topics

Deep Nets with Tensorflow Abstractions API - Part One

Deep Nets with Tensorflow Abstractions API - Estimator API

Deep Nets with Tensorflow Abstractions API - Keras

Deep Nets with Tensorflow Abstractions API - Layers

Tensorboard

Learn how to use AutoEncoders for Unsupervised Learning

Autoencoder Basics

Dimensionality Reduction with Linear Autoencoder

Linear Autoencoder PCA Exercise Overview

Linear Autoencoder PCA Exercise Solutions

Stacked Autoencoder

Let's learn about Reinforcement Learning and apply it to OpenAI Gym!

Introduction to Reinforcement Learning with OpenAI Gym

Extra Resources for Reinforcement Learning

Introduction to OpenAI Gym

OpenAI Gym Steup

Open AI Gym Env Basics

Open AI Gym Observations

OpenAI Gym Actions

Simple Neural Network Game

Policy Gradient Theory

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Strengthens an existing foundation for intermediate learners

Develops professional skills or deep expertise in a particular topic or set of topics

Taught by Jose Portilla, who are recognized for their work in x

Examines x, which is highly relevant to y

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

Teaches x, which helps learners do y

Reviews summary

Tensorflow for deep learning

According to students, this course was excellent for learning about TensorFlow, especially for those with existing knowledge of machine learning and deep learning theory. Learners appreciated the detailed explanations, code walkthroughs, and exercises. The self-paced nature of the class allowed learners to move through the material at their own pace. Students reported no major negative feedback for this course.

Learners can move at their own pace

Course had in-depth exercises.

"The detailed code walkthroughs and exercises really help!"

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Complete Guide to TensorFlow for Deep Learning with Python with these activities:

Compile a glossary of terms

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Creating a glossary will help you master the key terminologies used in this course.

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Review course materials and identify important terms
Define each term clearly and concisely
Organize the terms alphabetically or by category

Review linear algebra and calculus

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A strong foundation in these mathematical concepts will enhance your understanding of neural networks and deep learning algorithms.

Browse courses on Linear Algebra

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Review textbooks or online resources
Solve practice problems and exercises
Attend a refresher course or workshop

Join a study group

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Discussing concepts with peers can enhance your understanding and identify areas where you need further clarification.

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Discuss difficult concepts and share insights
Find a group of classmates or online learners
Schedule regular study sessions
Review course materials together

Five other activities

Expand to see all activities and additional details

Show all eight activities

Explore TensorFlow tutorials

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TensorFlow provides comprehensive tutorials that can supplement your learning in this course.

Browse courses on TensorFlow

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Visit the TensorFlow website
Browse through the available tutorials
Choose a tutorial that aligns with your interests
Follow the steps and complete the tutorial

Code along with the lectures

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This course is all about practical implementation, therefore coding along with the lectures will improve your understanding and solidify what you learned.

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Watch the lecture videos
Code along with the lecturer
Test your code and troubleshoot any errors

Build a neural network from scratch

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To truly understand neural networks, try building one from scratch. This will help you grasp the underlying concepts and strengthen your understanding.

Browse courses on Neural Networks

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Review the theory behind neural networks
Choose a programming language and environment
Implement the forward and backward passes
Train your neural network on a dataset
Evaluate the performance of your neural network

Volunteer at a research lab or organization

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Practical experience in a research setting can provide valuable insights and connections.

Browse courses on Machine Learning

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Identify research labs or organizations that align with your interests
Reach out to the lab or organization
Inquire about volunteer opportunities
Attend orientation and training
Contribute to research projects or other activities

Develop a machine learning application

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Applying your knowledge to a real-world project will solidify your understanding and prepare you for practical applications.

Browse courses on Machine Learning

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Identify a problem or opportunity
Gather data and prepare it for analysis
Choose and train a machine learning model
Deploy your model and evaluate its performance
Refine and iterate your model based on feedback

Career center

Learners who complete Complete Guide to TensorFlow for Deep Learning with Python will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

A Machine Learning Engineer designs, develops, and deploys machine learning models to solve real-world problems. They work closely with data scientists to identify the appropriate machine learning techniques for a given problem, and then develop and implement the models. The Complete Guide to TensorFlow for Deep Learning with Python course provides a comprehensive overview of the TensorFlow framework, which is one of the most popular machine learning frameworks in use today. This course will help you build a strong foundation in TensorFlow, and will enable you to develop and deploy your own machine learning models.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

A Data Scientist uses data to solve business problems. They collect, clean, and analyze data to identify trends and patterns. They then use this information to develop models and algorithms that can be used to make predictions and recommendations. The Complete Guide to TensorFlow for Deep Learning with Python course will help you build a strong foundation in TensorFlow, which is one of the most popular machine learning frameworks in use today. This course will enable you to develop and deploy your own machine learning models, and will give you the skills you need to succeed as a Data Scientist.

See salaries and explore the career path for Data Scientist

Software Engineer

A Software Engineer designs, develops, and maintains software systems. They work with a variety of programming languages and technologies to create software solutions that meet the needs of their clients. The Complete Guide to TensorFlow for Deep Learning with Python course will help you build a strong foundation in TensorFlow, which is one of the most popular machine learning frameworks in use today. This course will enable you to develop and deploy your own machine learning models, and will give you the skills you need to succeed as a Software Engineer.

See salaries and explore the career path for Software Engineer

Quantitative Analyst

A Quantitative Analyst uses mathematical and statistical models to analyze financial data. They use this information to make investment decisions and to develop trading strategies. The Complete Guide to TensorFlow for Deep Learning with Python course will help you build a strong foundation in TensorFlow, which is one of the most popular machine learning frameworks in use today. This course will enable you to develop and deploy your own machine learning models, and will give you the skills you need to succeed as a Quantitative Analyst.

See salaries and explore the career path for Quantitative Analyst

Research Scientist

A Research Scientist conducts research in a variety of fields, including science, engineering, and medicine. They use their knowledge and skills to develop new theories and technologies. The Complete Guide to TensorFlow for Deep Learning with Python course may be useful for Research Scientists who are working in the field of machine learning. This course will provide you with a comprehensive overview of the TensorFlow framework, and will enable you to develop and deploy your own machine learning models.

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Data Analyst

A Data Analyst collects, cleans, and analyzes data to identify trends and patterns. They then use this information to make recommendations and to improve business processes. The Complete Guide to TensorFlow for Deep Learning with Python course may be useful for Data Analysts who are working with large datasets. This course will provide you with a comprehensive overview of the TensorFlow framework, and will enable you to develop and deploy your own machine learning models.

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Business Analyst

A Business Analyst analyzes business processes and identifies opportunities for improvement. They use their knowledge of business and technology to develop solutions that meet the needs of their clients. The Complete Guide to TensorFlow for Deep Learning with Python course may be useful for Business Analysts who are working with machine learning projects. This course will provide you with a comprehensive overview of the TensorFlow framework, and will enable you to develop and deploy your own machine learning models.

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Product Manager

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Project Manager

A Project Manager plans, executes, and controls projects. They work with a variety of stakeholders to ensure that projects are completed on time, within budget, and to the required quality. The Complete Guide to TensorFlow for Deep Learning with Python course may be useful for Project Managers who are working with machine learning projects. This course will provide you with a comprehensive overview of the TensorFlow framework, and will enable you to develop and deploy your own machine learning models.

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Consultant

A Consultant provides advice and guidance to clients on a variety of topics. They use their knowledge and expertise to help clients solve problems and achieve their goals. The Complete Guide to TensorFlow for Deep Learning with Python course may be useful for Consultants who are working with machine learning projects. This course will provide you with a comprehensive overview of the TensorFlow framework, and will enable you to develop and deploy your own machine learning models.

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Teacher

A Teacher develops and delivers lesson plans and teaches students in a variety of subjects. They work with students of all ages and abilities to help them learn and grow. The Complete Guide to TensorFlow for Deep Learning with Python course may be useful for Teachers who are teaching computer science or machine learning. This course will provide you with a comprehensive overview of the TensorFlow framework, and will enable you to develop and deploy your own machine learning models.

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Writer

A Writer creates written content for a variety of purposes, including news articles, blog posts, and marketing materials. They use their creativity and writing skills to communicate ideas and information in a clear and engaging way. The Complete Guide to TensorFlow for Deep Learning with Python course may be useful for Writers who are writing about machine learning or artificial intelligence. This course will provide you with a comprehensive overview of the TensorFlow framework, and will enable you to develop and deploy your own machine learning models.

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Editor

An Editor reviews, edits, and proofreads written content to ensure that it is clear, correct, and consistent. They work with writers, authors, and publishers to produce high-quality written content. The Complete Guide to TensorFlow for Deep Learning with Python course may be useful for Editors who are working with technical or scientific content. This course will provide you with a comprehensive overview of the TensorFlow framework, and will enable you to develop and deploy your own machine learning models.

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Librarian

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Archivist

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