Tensorflow 2.0: Deep Learning and Artificial Intelligence from Udemy

Ever wondered how AI technologies like OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion really work? In this course, you will learn the foundations of these groundbreaking applications.

Welcome to Tensorflow 2.0.

What an exciting time. It's been nearly 4 years since Tensorflow was released, and the library has evolved to its official second version.

Tensorflow is Google's library for deep learning and artificial intelligence.

Deep Learning has been responsible for some amazing achievements recently, such as:

Generating beautiful, photo-realistic images of people and things that never existed (GANs)
Beating world champions in the strategy game Go, and complex video games like CS:GO and Dota 2 (Deep Reinforcement Learning)
Self-driving cars (Computer Vision)
Speech recognition (e.g. Siri) and machine translation (Natural Language Processing)
Even creating videos of people doing and saying things they never did (DeepFakes - a potentially nefarious application of deep learning)

Tensorflow is the world's most popular library for deep learning, and it's built by Google, whose parent Alphabet recently became the most cash-rich company in the world (just a few days before I wrote this). It is the library of choice for many companies doing AI and machine learning.

In other words, if you want to do deep learning, you gotta know Tensorflow.

This course is for beginner-level students all the way up to expert-level students. How can this be?

If you've just taken my free Numpy prerequisite, then you know everything you need to jump right in. We will start with some very basic machine learning models and advance to state of the art concepts.

Along the way, you will learn about all of the major deep learning architectures, such as Deep Neural Networks, Convolutional Neural Networks (image processing), and Recurrent Neural Networks (sequence data).

Current projects include:

Natural Language Processing (NLP)
Recommender Systems
Transfer Learning for Computer Vision
Generative Adversarial Networks (GANs)
Deep Reinforcement Learning Stock Trading Bot

Even if you've taken all of my previous courses already, you will still learn about how to convert your previous code so that it uses Tensorflow 2.0, and there are all-new and never-before-seen projects in this course such as time series forecasting and how to do stock predictions.

This course is designed for students who want to learn fast, but there are also "in-depth" sections in case you want to dig a little deeper into the theory (like what is a loss function, and what are the different types of gradient descent approaches).

Advanced Tensorflow topics include:

Deploying a model with Tensorflow Serving (Tensorflow in the cloud)
Deploying a model with Tensorflow Lite (mobile and embedded applications)
Distributed Tensorflow training with Distribution Strategies
Writing your own custom Tensorflow model
Converting Tensorflow 1.x code to Tensorflow 2.0
Constants, Variables, and Tensors
Eager execution
Gradient tape

Instructor's Note: This course focuses on breadth rather than depth, with less theory in favor of building more cool stuff. If you are looking for a more theory-dense course, this is not it. Generally, for each of these topics (recommender systems, natural language processing, reinforcement learning, computer vision, GANs, etc.) I already have courses singularly focused on those topics.

Thanks for reading, and I’ll see you in class.

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)

UNIQUE FEATURES

Every line of code explained in detail - email me any time if you disagree
No wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratch
Not afraid of university-level math - get important details about algorithms that other courses leave out

What's inside

Learning objectives

Artificial neural networks (anns) / deep neural networks (dnns)
Predict stock returns
Time series forecasting
Computer vision
How to build a deep reinforcement learning stock trading bot
Gans (generative adversarial networks)
Recommender systems
Image recognition
Convolutional neural networks (cnns)
Recurrent neural networks (rnns)
Use tensorflow serving to serve your model using a restful api

Use tensorflow lite to export your model for mobile (android, ios) and embedded devices
Use tensorflow's distribution strategies to parallelize learning
Low-level tensorflow, gradient tape, and how to build your own custom models
Natural language processing (nlp) with deep learning
Demonstrate moore's law using code
Transfer learning to create state-of-the-art image classifiers
Earn the tensorflow developer certificate
Understand important foundations for openai chatgpt, gpt-4, dall-e, midjourney, and stable diffusion
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Artificial neural networks (anns) / deep neural networks (dnns)
Predict stock returns
Time series forecasting
Computer vision
How to build a deep reinforcement learning stock trading bot
Gans (generative adversarial networks)
Recommender systems
Image recognition
Convolutional neural networks (cnns)
Recurrent neural networks (rnns)
Use tensorflow serving to serve your model using a restful api
Use tensorflow lite to export your model for mobile (android, ios) and embedded devices
Use tensorflow's distribution strategies to parallelize learning
Low-level tensorflow, gradient tape, and how to build your own custom models
Natural language processing (nlp) with deep learning
Demonstrate moore's law using code
Transfer learning to create state-of-the-art image classifiers
Earn the tensorflow developer certificate
Understand important foundations for openai chatgpt, gpt-4, dall-e, midjourney, and stable diffusion
Show more
Show less

Syllabus

Welcome

Introduction

Outline

Get Your Hands Dirty, Practical Coding Experience, Data Links

Where to get the code, notebooks, and data

Google Colab

Intro to Google Colab, how to use a GPU or TPU for free

Tensorflow 2.0 in Google Colab

Uploading your own data to Google Colab

Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn?

How to Succeed in This Course

Temporary 403 Errors

Machine Learning and Neurons

What is Machine Learning?

Code Preparation (Classification Theory)

Classification Notebook

Code Preparation (Regression Theory)

Regression Notebook

The Neuron

How does a model "learn"?

Making Predictions

Saving and Loading a Model

Why Keras?

Suggestion Box

Feedforward Artificial Neural Networks

Artificial Neural Networks Section Introduction

Beginners Rejoice: The Math in This Course is Optional

Forward Propagation

The Geometrical Picture

Activation Functions

Multiclass Classification

How to Represent Images

Color Mixing Clarification

Code Preparation (ANN)

ANN for Image Classification

ANN for Regression

Convolutional Neural Networks

What is Convolution? (part 1)

What is Convolution? (part 2)

What is Convolution? (part 3)

Convolution on Color Images

CNN Architecture

CNN Code Preparation

CNN for Fashion MNIST

CNN for CIFAR-10

Data Augmentation

Batch Normalization

Improving CIFAR-10 Results

Recurrent Neural Networks, Time Series, and Sequence Data

Sequence Data

Forecasting

Autoregressive Linear Model for Time Series Prediction

Proof that the Linear Model Works

Recurrent Neural Networks

RNN Code Preparation

RNN for Time Series Prediction

Paying Attention to Shapes

GRU and LSTM (pt 1)

GRU and LSTM (pt 2)

A More Challenging Sequence

Demo of the Long Distance Problem

RNN for Image Classification (Theory)

RNN for Image Classification (Code)

Stock Return Predictions using LSTMs (pt 1)

Stock Return Predictions using LSTMs (pt 2)

Stock Return Predictions using LSTMs (pt 3)

Other Ways to Forecast

Natural Language Processing (NLP)

Embeddings

Code Preparation (NLP)

Text Preprocessing

Text Classification with LSTMs

CNNs for Text

Text Classification with CNNs

Recommender Systems

Recommender Systems with Deep Learning Theory

Recommender Systems with Deep Learning Code

Transfer Learning for Computer Vision

Transfer Learning Theory

Some Pre-trained Models (VGG, ResNet, Inception, MobileNet)

Large Datasets and Data Generators

2 Approaches to Transfer Learning

Transfer Learning Code (pt 1)

Transfer Learning Code (pt 2)

GANs (Generative Adversarial Networks)

GAN Theory

GAN Code

Deep Reinforcement Learning (Theory)

Deep Reinforcement Learning Section Introduction

Elements of a Reinforcement Learning Problem

States, Actions, Rewards, Policies

Markov Decision Processes (MDPs)

The Return

Value Functions and the Bellman Equation

What does it mean to “learn”?

Solving the Bellman Equation with Reinforcement Learning (pt 1)

Solving the Bellman Equation with Reinforcement Learning (pt 2)

Epsilon-Greedy

Q-Learning

Deep Q-Learning / DQN (pt 1)

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Focuses on constructing usable tools and models to deploy in industry practices

Teaches students how to build and deploy products using TensorFlow 2.0

Provides foundational knowledge of machine learning and AI concepts

Introduces students to the latest advancements in AI and deep learning

Offers a hands-on, beginner-friendly approach

Suitable for students with varying skill levels, from beginners to experts

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 Tensorflow 2.0: Deep Learning and Artificial Intelligence with these activities:

Join a TensorFlow study group

Show steps

Collaborate with fellow learners in a study group. Discuss course material, share insights, and work through problems together to enhance your understanding.

Show steps

Find or create a TensorFlow study group
Meet regularly to discuss topics

Learn Keras fundamentals

Show steps

Keras is a widely-used library for deep learning in Python. Familiarize yourself with its basic concepts to enhance your understanding of TensorFlow.

Browse courses on Keras

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Browse official Keras documentation
Follow beginner-friendly tutorials on YouTube

Practice building simple neural networks

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Reinforce your understanding of neural networks by building them from scratch. Start with simple architectures and gradually increase their complexity.

Browse courses on Neural Networks

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Use TensorFlow's Sequential API
Create and train perceptrons
Build basic feedforward neural networks

Two other activities

Expand to see all activities and additional details

Show all five activities

Practice data preprocessing for deep learning

Show steps

Master the techniques of data preprocessing for deep learning models. Learn how to clean, transform, and augment data to improve model performance.

Browse courses on Data Preprocessing

Show steps

Implement data normalization and standardization
Create pipelines for data augmentation

Explore TensorFlow Lite for mobile and embedded devices

Show steps

Expand your knowledge by learning how to deploy TensorFlow models on mobile and embedded devices using TensorFlow Lite. This will enhance your understanding of real-world applications.

Browse courses on TensorFlow Lite

Show steps

Read TensorFlow Lite documentation
Follow tutorials on model conversion

Career center

Learners who complete Tensorflow 2.0: Deep Learning and Artificial Intelligence will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

Tensorflow 2.0: Deep Learning and Artificial Intelligence introduces the fundamental concepts of deep learning and artificial intelligence using the popular Tensorflow library. This course is perfectly suited for those aspiring to become Machine Learning Engineers, as it covers a wide range of deep learning architectures and provides hands-on experience in building and deploying models. The course's focus on practical coding ensures that learners gain the necessary skills to excel in this field.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Tensorflow 2.0: Deep Learning and Artificial Intelligence is an excellent choice for individuals looking to advance their careers as Data Scientists. The course provides a comprehensive overview of deep learning techniques, including natural language processing, computer vision, and time series forecasting. By mastering these skills, Data Scientists can gain a competitive edge in analyzing and interpreting complex data to drive informed decision-making.

See salaries and explore the career path for Data Scientist

Artificial Intelligence Engineer

For those seeking to specialize in Artificial Intelligence Engineering, Tensorflow 2.0: Deep Learning and Artificial Intelligence offers a solid foundation. The course covers the theoretical underpinnings of AI, as well as practical applications in areas such as natural language processing and computer vision. With a focus on hands-on coding, learners will develop the technical proficiency required for this in-demand field.

See salaries and explore the career path for Artificial Intelligence Engineer

Software Engineer

Tensorflow 2.0: Deep Learning and Artificial Intelligence can be beneficial for Software Engineers interested in incorporating deep learning into their projects. The course provides a comprehensive overview of the Tensorflow library and its applications in various domains. By gaining proficiency in Tensorflow, Software Engineers can enhance their skills and create innovative solutions that leverage the power of deep learning.

See salaries and explore the career path for Software Engineer

Quantitative Analyst

Tensorflow 2.0: Deep Learning and Artificial Intelligence may be useful for Quantitative Analysts looking to enhance their modeling capabilities. The course covers advanced topics in deep learning, such as time series forecasting and stock return predictions. By applying these techniques, Quantitative Analysts can refine their models and make more accurate predictions in financial markets.

See salaries and explore the career path for Quantitative Analyst

Business Analyst

Tensorflow 2.0: Deep Learning and Artificial Intelligence can be beneficial for Business Analysts seeking to gain a deeper understanding of data science and machine learning techniques. The course provides a practical introduction to deep learning, with a focus on applications in business domains such as recommender systems and natural language processing. By gaining proficiency in these techniques, Business Analysts can enhance their analytical skills and provide valuable insights to businesses.

See salaries and explore the career path for Business Analyst

Data Analyst

Tensorflow 2.0: Deep Learning and Artificial Intelligence may be useful for Data Analysts interested in exploring advanced data analysis techniques. The course provides a hands-on introduction to deep learning, with a focus on practical applications. By gaining proficiency in Tensorflow, Data Analysts can expand their skillset and tackle more complex data analysis challenges.

See salaries and explore the career path for Data Analyst

Product Manager

Tensorflow 2.0: Deep Learning and Artificial Intelligence can be beneficial for Product Managers aspiring to develop AI-powered products. The course provides a comprehensive overview of deep learning concepts and their applications in various industries. By gaining familiarity with deep learning, Product Managers can better understand the potential and limitations of AI and make informed decisions about product development.

See salaries and explore the career path for Product Manager

Consultant

Tensorflow 2.0: Deep Learning and Artificial Intelligence may be useful for Consultants seeking to expand their knowledge and skills in data science and machine learning. The course provides a practical introduction to deep learning, with a focus on real-world applications. By gaining proficiency in Tensorflow, Consultants can offer more comprehensive and innovative solutions to their clients.

See salaries and explore the career path for Consultant

Researcher

Tensorflow 2.0: Deep Learning and Artificial Intelligence can be beneficial for Researchers interested in exploring the latest advancements in deep learning. The course covers advanced topics and provides hands-on experience in building and evaluating deep learning models. By gaining proficiency in Tensorflow, Researchers can contribute to the development of cutting-edge AI solutions.

See salaries and explore the career path for Researcher

Teacher

Tensorflow 2.0: Deep Learning and Artificial Intelligence can be beneficial for Teachers seeking to incorporate AI into their teaching. The course provides a comprehensive overview of deep learning concepts and their applications in various fields. By gaining familiarity with deep learning, Teachers can develop innovative lesson plans and engage students in the exciting world of AI.

See salaries and explore the career path for Teacher

Entrepreneur

Tensorflow 2.0: Deep Learning and Artificial Intelligence can be useful for Entrepreneurs seeking to develop AI-powered products or services. The course provides a practical introduction to deep learning and its applications in various industries. By gaining proficiency in Tensorflow, Entrepreneurs can gain a competitive advantage and create innovative solutions that meet market needs.

See salaries and explore the career path for Entrepreneur

Financial Analyst

Tensorflow 2.0: Deep Learning and Artificial Intelligence may be useful for Financial Analysts seeking to enhance their analytical capabilities. The course covers advanced topics in deep learning, such as time series forecasting and stock return predictions. By applying these techniques, Financial Analysts can refine their models and make more accurate predictions in financial markets.

See salaries and explore the career path for Financial Analyst

Marketing Manager

Tensorflow 2.0: Deep Learning and Artificial Intelligence can be beneficial for Marketing Managers seeking to leverage AI for data-driven marketing campaigns. The course provides a practical introduction to deep learning, with a focus on applications in marketing domains such as customer segmentation and personalized recommendations. By gaining proficiency in Tensorflow, Marketing Managers can enhance their campaigns and achieve better results.

See salaries and explore the career path for Marketing Manager

Operations Manager

Tensorflow 2.0: Deep Learning and Artificial Intelligence may be useful for Operations Managers seeking to improve efficiency and productivity through AI. The course provides a practical introduction to deep learning, with a focus on applications in operations domains such as supply chain management and predictive maintenance. By gaining proficiency in Tensorflow, Operations Managers can optimize their operations and gain a competitive advantage.

See salaries and explore the career path for Operations Manager