Deep Learning From Scratch In Python
Deep Learning From Scratch In Python
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Published 3/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.44 GB | Duration: 5h 16m
Understand Convolutional Neural Networks and Implement your Object-Detection Framework From Scratch
What you'll learn
Understand how Deep Neural Networks work, practically and mathematically
Understand Forward- and Backpropagation processes, mathematically and practically
Design and implement a Deep Neural Network for multi-class classification
Understand and implement the building blocks of Convolutional Neural Networks
Understand and Implement cutting-edge Optimization, Regularization and Initialization techniques
Train and validate a Convolutional Model on widely used datasets like MNIST and CIFAR-10
Understand and implement Transfer Learning
Use a Convolutional Model to create a Real-Time, Multi-Object Detection System
Requirements
No prior knowledge is required
Description
This course is for anyone willing to really understand how Convolutional Neural Networks (CNNs) work. Every component of CNNs is first presented and explained mathematically, and the implemented in Python.Interactive programming exercises, executable within the course webpage, allow to gradually build a complete Object-Detection Framework based on an optimized Convolutional Neural Network model. No prior knowledge is required: the dedicated sections about Python Programming Basics and Calculus for Deep Learning provide the necessary knowledge to follow the course and implement Convolutional Neural Networks.In this course, students will be introduced to one of the latest and most successful algorithms for real-time multiple object detection. Throughout the course, they will gain a comprehensive understanding of the Backpropagation process, both from a mathematical and programming perspective, allowing them to build a strong foundation in this essential aspect of neural network training.By the course's conclusion, students will have hands-on experience implementing a sophisticated convolutional neural network framework. This framework will incorporate cutting-edge optimization and regularization techniques, enabling them to tackle complex real-world object detection tasks effectively and achieve impressive performance results. This practical knowledge will empower students to advance their capabilities in the exciting field of Computer Vision and Deep Learning.
Overview
Section 1: Neural Networks Basics
Lecture 1 Introduction
Lecture 2 Intuition about Fully-Connected Networks
Lecture 3 Gradient Descent Algorithms
Lecture 4 Training, Validation and Testing
Section 2: Python Programming Basics
Lecture 5 Python for CNNs
Lecture 6 Working with Lists and Tuples
Lecture 7 Working with NumPy Arrays
Lecture 8 Object-Oriented Programming
Section 3: Calculus for Deep Learning
Lecture 9 The Derivative of a Function
Lecture 10 The Product, Quotient and Power Rules
Lecture 11 Derivatives by the Chain Rule
Section 4: Cost Functions and Backpropagation
Lecture 12 Backpropagation in Fully-Connected Networks
Lecture 13 The Softmax Activation Function
Lecture 14 The Cross-Entropy Cost Function
Lecture 15 Backpropagation in the Output Layer
Section 5: Building Blocks of Convolutional Neural Networks (CNNs)
Lecture 16 Introduction to Convolutional Networks
Lecture 17 Convolutions: Theory
Lecture 18 Convolutions: Implementing an Edge Detector
Lecture 19 Downsampling through Max Pooling
Section 6: Backpropagation in Convolutional Neural Networks
Lecture 20 Backpropagation in Convolutional Layers
Lecture 21 Backpropagation in Pooling Layers
Section 7: Integration of a Convolutional Model
Lecture 22 Defining a Convolutional Model
Lecture 23 The MNIST Dataset
Lecture 24 Filter Visualization
Section 8: Transfer Learning
Lecture 25 What is Transfer Learning
Section 9: Insights into Optimization and Regularization
Lecture 26 Fully-Convolutional Implementation
Lecture 27 The Vanishing Gradient and Dying ReLU Problems
Lecture 28 Parameters Initialization
Lecture 29 Learning Rate Decay
Lecture 30 The Adam Optimizer
Lecture 31 Testing the Optimized Model on MNIST
Lecture 32 Testing the Optimized Model on CIFAR-10
Section 10: Multiple Object Detection in Real-Time
Lecture 33 The YOLO Algorithm
Lecture 34 Testing the YOLO Algorithm
Everyone interested in really understanding Convolutional Neural Networks and willing to create their own Object Detection Framework in Python
rapidgator.net:
Kod:
https://rapidgator.net/file/20388317aa5645891ad9e0a6ae851245/eizyb.Deep.Learning.From.Scratch.In.Python.part1.rar.html
https://rapidgator.net/file/a3b099af07072e33278ce81ee8b0b10f/eizyb.Deep.Learning.From.Scratch.In.Python.part2.rar.html
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