Description
Modern Deep Learning in Python is a comprehensive and project-oriented training course on deep learning with the Python programming language, published by Udemy Academy. Python is a high-level programming language used in various fields such as data science, machine learning, deep learning, and artificial intelligence. Based on this programming language, various libraries and frameworks have been developed, among the most important of which we can mention Tensorflow, Theano, Keras, PyTorch, CNTK and MXNet. In this training course, you will learn batch learning techniques and stochastic gradient descent. By using these two techniques, you are able to train the artificial neural network using a limited set of data and speed up the process of learning and training the network.
This training course covers many complex topics in the field of machine learning and deep learning, among the most important of which are momentum, adaptive learning rate and its techniques such as AdaGrad, RMSprop and Adam. He mentioned dropout regularization and batch normalization and their implementation in Theano and TensorFlow libraries. These two libraries have unique advantages over other libraries in terms of pure performance and speed. In these two libraries, the user can use the processing capacity of the graphics card to increase the processing speed. This training course is completely practical and project-oriented, and you will use datasets and real data sets during the training process.
What you will learn in the Modern Deep Learning in Python course:
- Adding momentum to the backpropagation method to develop neural networks
- Adaptive learning rate and related techniques such as AdaGrad, RMSprop and Adamg
- Elements of the Theano library such as variables and functions
- Artificial Neural Network Development with Theano Library
- TensorFlow library and its advantages
- Development of artificial neural network with TensorFlow library
- MNIST dataset
- Gradient Descent Optimization Algorithm (Gradient descent)
- stochastic gradient descent
- Implementation of dropout regularization technique in Theano and TensorFlow libraries
- Implementation of batch normalization technique in Theano and TensorFlow libraries
- Development of artificial neural network with Keras, PyTorch, CNTK and MXNet
Course details
Publisher: Yudmi
Instructor: Lazy Programmer Inc
English language
Training level: introductory to advanced
Number of courses: 87
Training duration: 11 hours and 15 minutes
Course topics on 10/2021
Modern Deep Learning in Python course prerequisites
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.
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
Course images
Modern Deep Learning in Python course introduction video
Installation guide
After Extract, view with your favorite Player.
English subtitle
Quality: 720p
download link
Password file(s): www.downloadly.ir
Size
2.9 GB
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