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ANNs and DNNs 0 to 100 - Python Coding files and references - OneDDL - 08-23-2024
![[Image: 5f938d94e7925e634d8da1669aeae531.jpeg]](https://i123.fastpic.org/big/2024/0823/31/5f938d94e7925e634d8da1669aeae531.jpeg)
Free Download ANNs and DNNs 0 to 100 - Python Coding files and references
Published 8/2024
Created by Ghazal Lalooha
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 18 Lectures ( 4h 42m ) | Size: 3.84 GB
Linear Classifier | SVM | Regularization | Softmax | Gradient Descent | Backpropagation | DNN | Dropout | CNN
What you'll learn:
Linear Classification
Cost function of multi-class SVM
Overfitting and Regularization
Softmax cost function
Cost function optimization
Gradient Descent Algorithm
Back Propagation Algorithm
Multi-layer artificial neural networks
Deep Neural Networks
Problem Solving with Artificial Neural Networks
Advanced Optimization methods
Drop out usage in DNN training
CNN
Requirements:
familiarity with Python programming language
basic familiarity with Machine Learning
Description:
Embark on a comprehensive journey to master Artificial Neural Networks (ANNs) and Deep Neural Networks (DNNs) with my expertly structured course. Designed for both beginners and those looking to deepen their understanding, this course offers a blend of theoretical concepts and practical coding exercises in Python. Key Topics Covered: Linear Classifiers: Understand the foundation of classification algorithms and their role in machine learning. Support Vector Machines (SVM): Dive into SVMs, the powerful supervised learning models used for classification and regression. Overfitting and Regularization: Learn how to identify overfitting in your models and techniques to regularize and prevent it. Softmax: Master the Softmax function for multi-class classification problems. Gradient Descent: Grasp the optimization method crucial for training neural networks. Backpropagation: Gain insight into the algorithm that adjusts weights in the network to minimize error. Deep Neural Networks (DNNs): Explore advanced architectures and how they can vastly improve model performance. Dropout: Implement dropout techniques to prevent overfitting in deep learning models. Convolutional Neural Networks (CNNs): Delve into CNNs for image processing and other applications. Course Features:Comprehensive **Python coding files** and references are provided to enhance hands-on learning. Detailed explanatory sessions combined with practical assignments. Step-by-step guidance through each topic, ensuring a solid understanding of basic concepts to advanced techniques. By the end of this course, you will possess a robust understanding of both theoretical and practical aspects of neural networks, equipped to tackle complex machine learning challenges with confidence. Join now and transform your understanding of ANNs and DNNs from 0 to 100!
Who this course is for:
everyone who wants to get promotion in his job(in every field of work)
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