Description
Modern Deep Learning in Python is a comprehensive, project-based deep learning course in 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, 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. By using these two techniques, you can practice the artificial neural network using a limited set of data and speed up the process of learning and practicing the network.
his 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. Both of these 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 is completely hands-on 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
- Theano library elements such as variables and functions
- Development of an artificial neural network with the Theano library
- The TensorFlow library and its benefits
- Development of an artificial neural network with the TensorFlow library
- MNIST dataset
- Gradient descent optimization algorithm
- Stochastic gradient descent
- Implementing 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
Course specifications
Editor: Udemy
Instructors: Lazy Programmer Inc
French language
Level: Introductory to Advanced
Number of lessons: 87
Duration: 11 hours and 15 minutes
Course topics on 2021/10
Prerequisites for Modern Deep Learning in Python
Be comfortable with Python, Numpy and Matplotlib
If you’re new to gradient descent, backprop, and softmax, take my previous course, Deep Learning in Python, and then come back to this course.
Suggested prerequisites:
Learn about gradient descent
Probability and statistics
Python coding: if/else, loops, lists, dicts, sets
Numpy coding: raster and vector operations, loading a CSV file
Know how to write a neural network with Numpy
Pictures
Introductory Video to Modern Deep Learning in Python
Installation guide
After ripping, watch with your favorite player.
english subtitle
Quality: 720p
Download links
File password(s): ngaur.com
Cut
2.9 GB