Modern Deep Learning in Python free download

Modern Deep Learning in Python free download
Apply momentum to backpropagation to train neural networks . Apply adaptive learning rate procedures like AdaGrad, RMSprop, and Adam . Understand dropout regularization in Theano and TensorFlow . Build a neural network that performs well on the MNIST dataset . Use Keras to write neural networks using PyTorch and build neural networks that perform well on MNIST . Build neural networks in Tensorflow using Keras and Pytorch . Write neural networks with Keras, write neural network using Py Torch, and write a neural networks on MNIS . Use Python to learn about deep-learning algorithms in Python and use PyPyPyPyython .

What you’ll find out in Modern Deep Knowing in Python

  1. Apply energy to backpropagation to educate semantic networks
  2. Apply adaptive learning price treatments like AdaGrad, RMSprop, and also Adam to backpropagation to educate neural networks
  3. Recognize the basic foundation of Theano
  4. Build a semantic network in Theano
  5. Understand the fundamental foundation of TensorFlow
  6. Develop a semantic network in TensorFlow
  7. Develop a semantic network that performs well on the MNIST dataset
  8. Understand the difference in between full gradient descent, batch gradient descent, as well as stochastic gradient descent
  9. Understand and also implement failure regularization in Theano and TensorFlow
  10. Understand and carry out set normalization in Theano as well as Tensorflow
  11. Write a semantic network using Keras
  12. Create a semantic network utilizing PyTorch
  13. Create a neural network making use of CNTK
  14. Compose a neural network utilizing MXNet

Description

This training course continues where my very first program, Deep Knowing in Python, left off. You currently know just how to develop a synthetic semantic network in Python, as well as you have a plug-and-play script that you can use for TensorFlow. Neural networks are one of the staples of artificial intelligence, and also they are always a top contender in Kaggle contests. If you intend to improve your abilities with neural networks as well as deep knowing, this is the course for you.

You already discovered backpropagation, yet there were a lot of unanswered inquiries. Exactly how can you change it to boost training rate? In this program you will certainly discover batch and also stochastic slope descent, 2 frequently made use of methods that enable you to train on just a tiny example of the data at each version, greatly accelerating training time.

You will also find out about momentum, which can be helpful for bring you via neighborhood minima and stop you from needing to be too traditional with your discovering rate. You will likewise learn more about methods like,, and also which can additionally help speed up your training.

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
File Name :Modern Deep Learning in Python free download
Content Source:udemy
Genre / Category:Data Science
File Size :1.38 gb
Publisher :Lazy Programmer Inc.
Updated and Published:07 Jul,2022
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File name: Modern-Deep-Learning-in-Python.rar
File Size:1.38 gb
Course duration:2 hours
Instructor Name:Lazy Programmer Inc.
Language:English
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