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
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Instructor: Lazy Programmer Inc
Education Level: Introductory to Advanced
Number of Courses: 87
Training 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 do not yet know about gradient descent, backdrop, and softmax, take my earlier course, Deep Learning in Python, and then return to this course.
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
After Extract, watch with your favorite Player.
Download Part 1 – 1 GB
Download Part 2 – 1 GB
Download Section 3 – 917 MB
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