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30-Day Money-Back Guarantee

This course includes:

  • 21 hours on-demand video
  • 2 articles
  • Full lifetime access
  • Access on mobile and TV
Development Data Science TensorFlow

Tensorflow 2.0: Deep Learning and Artificial Intelligence

Neural Networks for Computer Vision, Time Series Forecasting, NLP, GANs, Reinforcement Learning, and More!
Bestseller
Rating: 4.6 out of 54.6 (4,490 ratings)
25,129 students
Created by Lazy Programmer Inc., Lazy Programmer Team
Last updated 1/2021
English
English [Auto]
30-Day Money-Back Guarantee

What you'll 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

Course content

21 sections • 129 lectures • 21h 13m total length

  • Preview04:03
  • Outline
    12:47
  • Where to get the code
    08:26

  • Intro to Google Colab, how to use a GPU or TPU for free
    Preview12:32
  • Tensorflow 2.0 in Google Colab
    07:54
  • Uploading your own data to Google Colab
    11:41
  • Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn?
    08:54
  • How to Succeed in this Course
    05:51

  • What is Machine Learning?
    14:26
  • Code Preparation (Classification Theory)
    15:59
  • Beginner's Code Preamble
    04:38
  • Classification Notebook
    08:40
  • Code Preparation (Regression Theory)
    07:19
  • Regression Notebook
    10:34
  • The Neuron
    09:58
  • How does a model "learn"?
    10:54
  • Making Predictions
    06:45
  • Saving and Loading a Model
    04:28
  • Suggestion Box
    03:03

  • Artificial Neural Networks Section Introduction
    06:00
  • Beginners Rejoice: The Math in This Course is Optional
    11:48
  • Forward Propagation
    09:40
  • The Geometrical Picture
    09:43
  • Activation Functions
    17:18
  • Multiclass Classification
    08:41
  • How to Represent Images
    12:36
  • Code Preparation (ANN)
    12:42
  • ANN for Image Classification
    08:36
  • ANN for Regression
    11:05

  • What is Convolution? (part 1)
    16:38
  • What is Convolution? (part 2)
    05:56
  • What is Convolution? (part 3)
    06:41
  • Convolution on Color Images
    15:58
  • CNN Architecture
    20:58
  • CNN Code Preparation
    15:13
  • CNN for Fashion MNIST
    06:46
  • CNN for CIFAR-10
    04:28
  • Data Augmentation
    08:51
  • Batch Normalization
    05:14
  • Improving CIFAR-10 Results
    10:22

  • Sequence Data
    18:27
  • Forecasting
    10:35
  • Autoregressive Linear Model for Time Series Prediction
    12:01
  • Proof that the Linear Model Works
    04:12
  • Recurrent Neural Networks
    21:34
  • RNN Code Preparation
    05:50
  • RNN for Time Series Prediction
    11:11
  • Paying Attention to Shapes
    08:27
  • GRU and LSTM (pt 1)
    16:09
  • GRU and LSTM (pt 2)
    11:36
  • A More Challenging Sequence
    09:19
  • Demo of the Long Distance Problem
    19:26
  • RNN for Image Classification (Theory)
    04:41
  • RNN for Image Classification (Code)
    04:00
  • Stock Return Predictions using LSTMs (pt 1)
    12:03
  • Stock Return Predictions using LSTMs (pt 2)
    05:45
  • Stock Return Predictions using LSTMs (pt 3)
    11:59
  • Other Ways to Forecast
    05:14

  • Embeddings
    13:12
  • Code Preparation (NLP)
    13:17
  • Text Preprocessing
    05:30
  • Text Classification with LSTMs
    08:19
  • CNNs for Text
    08:07
  • Text Classification with CNNs
    06:10

  • Recommender Systems with Deep Learning Theory
    13:10
  • Recommender Systems with Deep Learning Code
    09:17

  • Transfer Learning Theory
    08:12
  • Some Pre-trained Models (VGG, ResNet, Inception, MobileNet)
    05:41
  • Large Datasets and Data Generators
    07:03
  • 2 Approaches to Transfer Learning
    04:51
  • Transfer Learning Code (pt 1)
    10:49
  • Transfer Learning Code (pt 2)
    08:12

  • GAN Theory
    15:51
  • GAN Code
    12:10

Requirements

  • Know how to code in Python and Numpy
  • For the theoretical parts (optional), understand derivatives and probability

Description

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)

Who this course is for:

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

Featured review

Tejas Suvarna
Tejas Suvarna
107 courses
16 reviews
Rating: 5.0 out of 5a year ago
This is the finest course on TensorFlow you can ever get. It's a real course that covers up the complex math and the practical stuff in TensorFlow. It is a very well designed course, covers up all topics of Deep Learning with different data sets and code that we don't get elsewhere. LazyProgrammer is a true programmer and he is very authentic about the knowledge. I literally loved it. Thank you so much LazyProgrammer for this.

Instructors

Lazy Programmer Inc.
Artificial intelligence and machine learning engineer
Lazy Programmer Inc.
  • 4.6 Instructor Rating
  • 105,813 Reviews
  • 418,064 Students
  • 28 Courses

Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer.

I received my masters degree in computer engineering with a specialization in machine learning and pattern recognition.

Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark.

I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text.

My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing.

I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. 

Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more.

Lazy Programmer Team
Artificial Intelligence and Machine Learning Engineer
Lazy Programmer Team
  • 4.6 Instructor Rating
  • 39,314 Reviews
  • 144,748 Students
  • 14 Courses

Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer.

I received my masters degree in computer engineering with a specialization in machine learning and pattern recognition.

Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark.

I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text.

My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing.

I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School.

Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more.

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