Deep Learning and Neural Networks in Python 2023-12 – Download

Description

Data Science: Deep Learning and Neural Networks in Python is the name of the Neural Networks and Deep Learning Bootcamp published by the Udemy Academy. This course will get you started building your first artificial neural network using deep learning techniques and creating full nonlinear neural networks using Python and Numpy. This course extends the previous binary classification model to multiple classes using the softmax function and derives a very important training method called “backpropagation” using first principles. It will show you how to first find the “slow lane” and then the “fast lane” in Numpy coding using Numpy features. Next, it implements a neural network using Google’s new TensorFlow library. If you want to start your journey to becoming a deep learning master, or if you are interested in machine learning and data science in general, you should take this course. We’re going beyond basic models like logistic regression and linear regression, and I’m going to show you something that automatically learns features. This course gives you lots of practical examples so you can really see how deep learning can be used in anything. During the course, we will complete a course project that will show you how to predict user actions on a website based on user data, such as whether that user is on a mobile device or not. , the number of products viewed, the duration of their stay. on your site, whether they return or not, and what time of day they visited it. Another project at the end of the course will show you how to use deep learning for facial expression recognition. Imagine being able to predict someone’s feelings from a single photo! After familiarizing yourself with the basics, a brief overview of some of the most recent developments in the field of neural networks (slightly modified architectures and their use cases) is presented.

What you will learn in the Data Science: Deep Learning and Neural Networks in Python course:

Find out how deep learning really works
Discover how a neural network is built from the basic elements (neurons).
Code a neural network from scratch in python and numpy
Coding a Neural Network Using Google’s TensorFlow
Describe the different types of neural networks and the different types of problems they are used for
Derive the law of backpropagation from first principles
Create a neural network with output with classes K>2 using softmax software
Explain the different terms associated with neural networks such as “activation”, “backpropagation” and “feedforward”.
Install TensorFlow

Course Details

Editor: Udemy
Moderator: Lazy Programmer Inc.
French language
Education Level: Introduction to Advanced
Number of courses: 90
Training duration: 12 hours and 7 minutes

Course titles

Deep Data Science Learning and Neural Networks in Python

Course prerequisites

Basic mathematics (calculus derivatives, matrix arithmetic, probability)

Don’t worry about installing TensorFlow, we will do it in the courses.

Knowing the content of my logistic regression course (cross-entropy cost, gradient descent, neurons, XOR, donut) will give you the proper context for this course.

course images

Deep Data Science Learning and Neural Networks in Python

Course introduction video

installation guide

After ripping, view with your favorite player.

Subtitle: English

Quality: 1080p

Changes:

Version 2023/12 compared to 2021/6 increased the number by 1 lesson and the duration by 54 minutes. Additionally, the course quality has been increased from 720p to 1080p.