Descriptions
Data Science: Modern Deep Learning in Python, 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 regularization and 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.
What you’ll 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
Who this course is for
- 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
Specificatoin of Data Science: Modern Deep Learning in Python
- Publisher : Udemy
- Teacher : Lazy Programmer Inc. , Lazy Programmer Team
- Language: English
- Level : All Levels
- Lectures : 90
- Duration : 11 hours and 23 minutes
Content of Data Science: Modern Deep Learning in Python
Requirements
- Be comfortable with Python, Numpy, and Matplotlib
- If you do not yet know about gradient descent, backprop, and softmax, take my earlier course, Deep Learning in Python, and then return to this course.
Pictures
Sample Clip
Installation Guide
Extract the files and watch with your favorite player
Subtitle : English
Quality: 720p
The 2025/11 version has the duration increased by 1 minutes compared to 2023/3.
Download Links
Downloadly
Rapidgator
Password file(s): www.downloadly.ir
File size
1.74 GB
2 Comments. Leave new
please update the course
Hi
Updated