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 discover in Modern Deep Discovering in Python

  1. Apply energy to backpropagation to educate neural networks
  2. Use adaptive discovering rate procedures such as AdaGrad, RMSprop, as well as Adam to backpropagation to train neural networks
  3. Understand the fundamental building blocks of Theano
  4. Construct a semantic network in Theano
  5. Understand the basic building blocks of TensorFlow
  6. Develop a semantic network in TensorFlow
  7. Construct a semantic network that does well on the MNIST dataset
  8. Understand the distinction between complete slope descent, batch gradient descent, as well as stochastic slope descent
  9. Understand and execute failure regularization in Theano as well as TensorFlow
  10. Understand as well as apply batch normalization in Theano and also Tensorflow
  11. Compose a semantic network using Keras
  12. Create a semantic network utilizing PyTorch
  13. Compose a semantic network utilizing CNTK
  14. Compose a neural network making use of MXNet


This program proceeds where my first training course, Deep Learning in Python, ended. You currently recognize just how to construct a man-made semantic network in Python, and you have a plug-and-play manuscript that you can make use of for TensorFlow. Neural networks are among the staples of artificial intelligence, and they are always a top competitor in Kaggle contests. If you wish to enhance your abilities with semantic networks as well as deep learning, this is the training course for you.

You currently learned about backpropagation, yet there were a lot of unanswered questions. How can you modify it to improve training speed? In this course you will certainly learn about set and also stochastic gradient descent, two typically utilized strategies that permit you to educate on simply a tiny example of the information at each version, considerably accelerating training time.

You will additionally learn about energy, which can be valuable for carrying you with neighborhood minima and also avoid you from needing to be too traditional with your understanding price. You will certainly likewise discover methods like,, and also which can also help accelerate 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 :3.59 gb
Publisher :Lazy Programmer Inc.
Updated and Published:07 Jul,2022

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File name: Modern-Deep-Learning-in-Python.rar
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Course duration:7 hours
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