Data Science: Modern Deep Learning in Python

Data Science: Modern Deep Learning in Python, available at $109.99, has an average rating of 4.64, with 97 lectures, based on 3523 reviews, and has 39051 subscribers.

You will learn about Apply momentum to backpropagation to train neural networks Apply adaptive learning rate procedures like AdaGrad, RMSprop, and Adam to backpropagation to train neural networks Understand the basic building blocks of TensorFlow Build a neural network in TensorFlow Write a neural network using Keras Write a neural network using PyTorch Understand the difference between full gradient descent, batch gradient descent, and stochastic gradient descent Understand and implement dropout regularization Understand and implement batch normalization Understand the basic building blocks of Theano Build a neural network in Theano Write a neural network using CNTK Write a neural network using MXNet Understand important foundations for OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion This course is ideal for individuals who are Students and professionals who want to deepen their machine learning knowledge or Data scientists who want to learn more about deep learning or Data scientists who already know about backpropagation and gradient descent and want to improve it with stochastic batch training, momentum, and adaptive learning rate procedures like RMSprop or Those who do not yet know about backpropagation or softmax should take my earlier course, deep learning in Python, first It is particularly useful for Students and professionals who want to deepen their machine learning knowledge or Data scientists who want to learn more about deep learning or Data scientists who already know about backpropagation and gradient descent and want to improve it with stochastic batch training, momentum, and adaptive learning rate procedures like RMSprop or Those who do not yet know about backpropagation or softmax should take my earlier course, deep learning in Python, first.

Enroll now: Data Science: Modern Deep Learning in Python

Summary

Title: Data Science: Modern Deep Learning in Python

Price: $109.99

Average Rating: 4.64

Number of Lectures: 97

Number of Published Lectures: 90

Number of Curriculum Items: 97

Number of Published Curriculum Objects: 90

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Apply momentum to backpropagation to train neural networks
Apply adaptive learning rate procedures like AdaGrad, RMSprop, and Adam to backpropagation to train neural networks
Understand the basic building blocks of TensorFlow
Build a neural network in TensorFlow
Write a neural network using Keras
Write a neural network using PyTorch
Understand the difference between full gradient descent, batch gradient descent, and stochastic gradient descent
Understand and implement dropout regularization
Understand and implement batch normalization
Understand the basic building blocks of Theano
Build a neural network in Theano
Write a neural network using CNTK
Write a neural network using MXNet
Understand important foundations for OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion

Who Should Attend

Students and professionals who want to deepen their machine learning knowledge
Data scientists who want to learn more about deep learning
Data scientists who already know about backpropagation and gradient descent and want to improve it with stochastic batch training, momentum, and adaptive learning rate procedures like RMSprop
Those who do not yet know about backpropagation or softmax should take my earlier course, deep learning in Python, first

Target Audiences

Students and professionals who want to deepen their machine learning knowledge
Data scientists who want to learn more about deep learning
Data scientists who already know about backpropagation and gradient descent and want to improve it with stochastic batch training, momentum, and adaptive learning rate procedures like RMSprop
Those who do not yet know about backpropagation or softmax should take my earlier course, deep learning in Python, first

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.

This course continues where my first course, Deep Learning in Python, left off. You already know how to build an artificial neural network in Python, and you have a plug-and-play script that you can use for TensorFlow. Neural networks are one of the staples of machine learning, and they are always a top contender in Kaggle contests. If you want to improve your skills with neural networks and deep learning, this is the course for you.

You already learned about backpropagation, but there were a lot of unanswered questions. How can you modify it to improve training speed? In this course you will learn about batch and stochastic gradient descent, two commonly used techniques that allow you to train on just a small sample of the data at each iteration, greatly speeding up training time.

You will also learn about momentum, which can be helpful for carrying you through local minima and prevent you from having to be too conservative with your learning rate. You will also learn about adaptive learning rate techniques like AdaGrad, RMSprop, and Adam which can also help speed up your training.

Because you already know about the fundamentals of neural networks, we are going to talk about more modern techniques, like dropout regularizationand batch normalization, which we will implement in both TensorFlow and Theano. The course is constantly being updated and more advanced regularization techniques are coming in the near future.

In my last course, I just wanted to give you a little sneak peak at TensorFlow. In this course we are going to start from the basics so you understand exactly what’s going on – what are TensorFlow variables and expressions and how can you use these building blocks to create a neural network? We are also going to look at a library that’s been around much longer and is very popular for deep learning – Theano. With this library we will also examine the basic building blocks – variables, expressions, and functions – so that you can build neural networks in Theano with confidence.

Theano was the predecessor to all modern deep learning libraries today. Today, we have almost TOO MANY options. Keras, PyTorch, CNTK (Microsoft), MXNet (Amazon / Apache), etc. In this course, we cover all of these! Pick and choose the one you love best.

Because one of the main advantages of TensorFlow and Theano is the ability to use the GPU to speed up training, I will show you how to set up a GPU-instance on AWS and compare the speed of CPU vs GPU for training a deep neural network.

With all this extra speed, we are going to look at a real dataset – the famous MNIST dataset (images of handwritten digits) and compare against various benchmarks. This is THE dataset researchers look at first when they want to ask the question, “does this thing work?”

These images are important part of deep learning history and are still used for testing today. Every deep learning expert should know them well.

This course focuses on “how to build and understand“, not just “how to use”. Anyone can learn to use an API in 15 minutes after reading some documentation. It’s not about “remembering facts”, it’s about “seeing for yourself” via experimentation. It will teach you how to visualize what’s happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.

“If you can’t implement it, you don’t understand it”

Or as the great physicist Richard Feynman said: “What I cannot create, I do not understand”.
My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch
Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?
After doing the same thing with 10 datasets, you realize you didn’t learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times…

Suggested Prerequisites:

Know about gradient descent
Probability and statistics
Python coding: if/else, loops, lists, dicts, sets
Numpy coding: matrix and vector operations, loading a CSV file
Know how to write a neural network with Numpy

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)

Course Curriculum

Chapter 1: Introduction and Outline

Lecture 1: Introduction and Outline

Lecture 2: Where to get the Code

Lecture 3: How to Succeed in this Course

Chapter 2: Review

Lecture 1: Review (pt 1): Neuron Predictions

Lecture 2: Review (pt 2): Neuron Learning

Lecture 3: Review (pt 3): Artificial Neural Networks

Lecture 4: Review Exercise Prompt

Lecture 5: Review Code (pt 1)

Lecture 6: Review Code (pt 2)

Lecture 7: Review Summary

Chapter 3: Stochastic Gradient Descent and Mini-Batch Gradient Descent

Lecture 1: Stochastic Gradient Descent and Mini-Batch Gradient Descent (Theory)

Lecture 2: SGD Exercise Prompt

Lecture 3: Stochastic Gradient Descent and Mini-Batch Gradient Descent (Code pt 1)

Lecture 4: Stochastic Gradient Descent and Mini-Batch Gradient Descent (Code pt 2)

Chapter 4: Momentum and adaptive learning rates

Lecture 1: Using Momentum to Speed Up Training

Lecture 2: Nesterov Momentum

Lecture 3: Momentum in Code

Lecture 4: Variable and adaptive learning rates

Lecture 5: Constant learning rate vs. RMSProp in Code

Lecture 6: Adam Optimization (pt 1)

Lecture 7: Adam Optimization (pt 2)

Lecture 8: Adam in Code

Lecture 9: Suggestion Box

Chapter 5: Choosing Hyperparameters

Lecture 1: Hyperparameter Optimization: Cross-validation, Grid Search, and Random Search

Lecture 2: Sampling Logarithmically

Lecture 3: Grid Search in Code

Lecture 4: Modifying Grid Search

Lecture 5: Random Search in Code

Chapter 6: Weight Initialization

Lecture 1: Weight Initialization Section Introduction

Lecture 2: Vanishing and Exploding Gradients

Lecture 3: Weight Initialization

Lecture 4: Local vs. Global Minima

Lecture 5: Weight Initialization Section Summary

Chapter 7: Theano

Lecture 1: Theano Basics: Variables, Functions, Expressions, Optimization

Lecture 2: Building a neural network in Theano

Lecture 3: Is Theano Dead?

Chapter 8: TensorFlow

Lecture 1: TensorFlow Basics: Variables, Functions, Expressions, Optimization

Lecture 2: Building a neural network in TensorFlow

Lecture 3: What is a Session? (And more)

Chapter 9: GPU Speedup, Homework, and Other Misc Topics

Lecture 1: Setting up a GPU Instance on Amazon Web Services

Lecture 2: Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer

Lecture 3: Can Big Data be used to Speed Up Backpropagation?

Lecture 4: How to Improve your Theano and Tensorflow Skills

Lecture 5: Theano vs. TensorFlow

Chapter 10: Transition to the 2nd Half of the Course

Lecture 1: Transition to the 2nd Half of the Course

Chapter 11: Project: Facial Expression Recognition

Lecture 1: Facial Expression Recognition Project Introduction

Lecture 2: Facial Expression Recognition Problem Description

Lecture 3: The class imbalance problem

Lecture 4: Utilities walkthrough

Lecture 5: Class-Based ANN in Theano

Lecture 6: Class-Based ANN in TensorFlow

Lecture 7: Facial Expression Recognition Project Summary

Chapter 12: Modern Regularization Techniques

Lecture 1: Modern Regularization Techniques Section Introduction

Lecture 2: Dropout Regularization

Lecture 3: Dropout Intuition

Lecture 4: Noise Injection

Lecture 5: Modern Regularization Techniques Section Summary

Chapter 13: Batch Normalization

Lecture 1: Batch Normalization Introduction

Lecture 2: Exponentially-Smoothed Averages

Lecture 3: Batch Normalization Theory

Lecture 4: Batch Normalization Tensorflow (part 1)

Lecture 5: Batch Normalization Tensorflow (part 2)

Lecture 6: Batch Normalization Theano (part 1)

Lecture 7: Batch Normalization Theano (part 2)

Lecture 8: Noise Perspective

Lecture 9: Batch Normalization Summary

Chapter 14: Keras

Lecture 1: Keras Discussion

Lecture 2: Keras in Code

Lecture 3: Keras Functional API

Lecture 4: How to easily convert Keras into Tensorflow 2.0 code

Chapter 15: PyTorch

Lecture 1: PyTorch Basics

Lecture 2: PyTorch Dropout

Lecture 3: PyTorch Batch Norm

Chapter 16: PyTorch, CNTK, and MXNet

Lecture 1: PyTorch, CNTK, and MXNet

Chapter 17: Deep Learning Review Topics

Lecture 1: What's the difference between "neural networks" and "deep learning"?

Lecture 2: Manually Choosing Learning Rate and Regularization Penalty

Chapter 18: Setting Up Your Environment (FAQ by Student Request)

Lecture 1: Pre-Installation Check

Lecture 2: Anaconda Environment Setup

Lecture 3: How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow

Chapter 19: Extra Help With Python Coding for Beginners (FAQ by Student Request)

Lecture 1: How to Code by Yourself (part 1)

Lecture 2: How to Code by Yourself (part 2)

Instructors

Lazy Programmer Inc.
Artificial intelligence and machine learning engineer

Rating Distribution

1 stars: 33 votes
2 stars: 44 votes
3 stars: 141 votes
4 stars: 1095 votes
5 stars: 2210 votes

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