Tensorflow 2.0: Deep Learning and Artificial Intelligence

Tensorflow 2.0: Deep Learning and Artificial Intelligence, available at $129.99, has an average rating of 4.79, with 169 lectures, based on 12433 reviews, and has 56784 subscribers.

You will learn about 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 This course is ideal for individuals who are Beginners to advanced students who want to learn about deep learning and AI in Tensorflow 2.0 It is particularly useful for Beginners to advanced students who want to learn about deep learning and AI in Tensorflow 2.0.

Enroll now: Tensorflow 2.0: Deep Learning and Artificial Intelligence

Summary

Title: Tensorflow 2.0: Deep Learning and Artificial Intelligence

Price: $129.99

Average Rating: 4.79

Number of Lectures: 169

Number of Published Lectures: 141

Number of Curriculum Items: 169

Number of Published Curriculum Objects: 141

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

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

Who Should Attend

Beginners to advanced students who want to learn about deep learning and AI in Tensorflow 2.0

Target Audiences

Beginners to advanced students who want to learn about deep learning and AI in Tensorflow 2.0

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

Course Curriculum

Chapter 1: Welcome

Lecture 1: Introduction

Lecture 2: Outline

Lecture 3: Where to get the code, notebooks, and data

Chapter 2: Google Colab

Lecture 1: Intro to Google Colab, how to use a GPU or TPU for free

Lecture 2: Tensorflow 2.0 in Google Colab

Lecture 3: Uploading your own data to Google Colab

Lecture 4: Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn?

Lecture 5: How to Succeed in This Course

Lecture 6: Temporary 403 Errors

Chapter 3: Machine Learning and Neurons

Lecture 1: What is Machine Learning?

Lecture 2: Code Preparation (Classification Theory)

Lecture 3: Classification Notebook

Lecture 4: Code Preparation (Regression Theory)

Lecture 5: Regression Notebook

Lecture 6: The Neuron

Lecture 7: How does a model "learn"?

Lecture 8: Making Predictions

Lecture 9: Saving and Loading a Model

Lecture 10: Why Keras?

Lecture 11: Suggestion Box

Chapter 4: Feedforward Artificial Neural Networks

Lecture 1: Artificial Neural Networks Section Introduction

Lecture 2: Beginners Rejoice: The Math in This Course is Optional

Lecture 3: Forward Propagation

Lecture 4: The Geometrical Picture

Lecture 5: Activation Functions

Lecture 6: Multiclass Classification

Lecture 7: How to Represent Images

Lecture 8: Color Mixing Clarification

Lecture 9: Code Preparation (ANN)

Lecture 10: ANN for Image Classification

Lecture 11: ANN for Regression

Chapter 5: Convolutional Neural Networks

Lecture 1: What is Convolution? (part 1)

Lecture 2: What is Convolution? (part 2)

Lecture 3: What is Convolution? (part 3)

Lecture 4: Convolution on Color Images

Lecture 5: CNN Architecture

Lecture 6: CNN Code Preparation

Lecture 7: CNN for Fashion MNIST

Lecture 8: CNN for CIFAR-10

Lecture 9: Data Augmentation

Lecture 10: Batch Normalization

Lecture 11: Improving CIFAR-10 Results

Chapter 6: Recurrent Neural Networks, Time Series, and Sequence Data

Lecture 1: Sequence Data

Lecture 2: Forecasting

Lecture 3: Autoregressive Linear Model for Time Series Prediction

Lecture 4: Proof that the Linear Model Works

Lecture 5: Recurrent Neural Networks

Lecture 6: RNN Code Preparation

Lecture 7: RNN for Time Series Prediction

Lecture 8: Paying Attention to Shapes

Lecture 9: GRU and LSTM (pt 1)

Lecture 10: GRU and LSTM (pt 2)

Lecture 11: A More Challenging Sequence

Lecture 12: Demo of the Long Distance Problem

Lecture 13: RNN for Image Classification (Theory)

Lecture 14: RNN for Image Classification (Code)

Lecture 15: Stock Return Predictions using LSTMs (pt 1)

Lecture 16: Stock Return Predictions using LSTMs (pt 2)

Lecture 17: Stock Return Predictions using LSTMs (pt 3)

Lecture 18: Other Ways to Forecast

Chapter 7: Natural Language Processing (NLP)

Lecture 1: Embeddings

Lecture 2: Code Preparation (NLP)

Lecture 3: Text Preprocessing

Lecture 4: Text Classification with LSTMs

Lecture 5: CNNs for Text

Lecture 6: Text Classification with CNNs

Chapter 8: Recommender Systems

Lecture 1: Recommender Systems with Deep Learning Theory

Lecture 2: Recommender Systems with Deep Learning Code

Chapter 9: Transfer Learning for Computer Vision

Lecture 1: Transfer Learning Theory

Lecture 2: Some Pre-trained Models (VGG, ResNet, Inception, MobileNet)

Lecture 3: Large Datasets and Data Generators

Lecture 4: 2 Approaches to Transfer Learning

Lecture 5: Transfer Learning Code (pt 1)

Lecture 6: Transfer Learning Code (pt 2)

Chapter 10: GANs (Generative Adversarial Networks)

Lecture 1: GAN Theory

Lecture 2: GAN Code

Chapter 11: Deep Reinforcement Learning (Theory)

Lecture 1: Deep Reinforcement Learning Section Introduction

Lecture 2: Elements of a Reinforcement Learning Problem

Lecture 3: States, Actions, Rewards, Policies

Lecture 4: Markov Decision Processes (MDPs)

Lecture 5: The Return

Lecture 6: Value Functions and the Bellman Equation

Lecture 7: What does it mean to “learn”?

Lecture 8: Solving the Bellman Equation with Reinforcement Learning (pt 1)

Lecture 9: Solving the Bellman Equation with Reinforcement Learning (pt 2)

Lecture 10: Epsilon-Greedy

Lecture 11: Q-Learning

Lecture 12: Deep Q-Learning / DQN (pt 1)

Lecture 13: Deep Q-Learning / DQN (pt 2)

Instructors

Lazy Programmer Inc.
Artificial intelligence and machine learning engineer
Lazy Programmer Team
Artificial Intelligence and Machine Learning Engineer

Rating Distribution

1 stars: 87 votes
2 stars: 92 votes
3 stars: 444 votes
4 stars: 3331 votes
5 stars: 8479 votes

Frequently Asked Questions

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