Udemy - Modern Deep Learning in Python 2021
Deep Learning

Udemy – Modern Deep Learning in Python 2021


Modern Deep Learning in Python is a comprehensive, project-based deep learning course in the Python programming language published by Yodemi 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, the most important of which are Tensorflow, Theano, Keras, PyTorch, CNTK, and MXNet. In this course, you will be introduced to batch learning techniques and stochastic gradient descent. Using these two techniques, you can practice artificial neural networks using a limited set of data and speed up the network learning and practice process.

This course covers many complex topics in the field of machine learning and deep learning, the most important of which are momentum, adaptive learning rate, and techniques such as AdaGrad, RMSprop, and Adam, techniques 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 net 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 during the training process, you will use real data and datasets.

What you will learn in Modern Deep Learning in Python:

  • Adding momentum to backpropagation for neural network development
  • Adaptive learning rates and related techniques such as AdaGrad, RMSprop and Adam g
  • Elements of Theano Library such as variables and functions
  • Development of artificial neural network with Theano library
  • TensorFlow Library and its Benefits
  • Development of artificial neural network with TensorFlow library
  • MNIST dataset
  • Gradient descent optimization algorithm
  • 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 networks with Keras, PyTorch, CNTK, and MXNet

See Also:

Udemy – Python Programming Bootcamp 2021

Udemy – A deep understanding of deep learning (with Python intro) 2021

Udemy – Deep Learning with PyTorch for Medical Image Analysis 2021

Udemy – Master statistics & machine learning: intuition, math, code 2021

Udemy – Unreal Engine C ++ Developer: Learn C ++ and Make Video Games 2021

Course specifications

Publisher: Udemi
Instructor: Lazy Programmer Inc
Language: English
Education Level: Introductory to Advanced
Number of Courses: 87
Training Duration: 11 hours and 15 minutes

Course topics on 2021/10


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Prerequisites for Modern Deep Learning in Python

Be comfortable with Python, Numpy, and Matplotlib

If you do not yet know about gradient descent, backdrop, 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 pictures


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Installation guide

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English subtitle

Quality: 720p

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Download Part 1 – 1 GB
Download Part 2 – 1 GB
Download Section 3 – 917 MB
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