Mastering Deep Learning: Implementing a Convolutional Neural Network from Scratch with Keras

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In this video we show a simple CNN architecture that will learn how to model from scratch with Keras and train it on a small data set called CIFAR-10.
By the end of the tutorial, you will have a solid understanding of how CNNs work and how to implement them using Keras, which will enable you to tackle a wide range of image classification and other deep learning tasks. So whether you're a beginner or an experienced practitioner, join us and master deep learning with Keras!
Topics Covered
✅Load the CIFAR-10 Dataset
✅Dataset Preprocessing
✅Dataset and Training Configuration Parameters
✅CNN Model Implementation in Keras
✅Adding Dropout to the Model
✅Saving and Loading Models
✅Model Evaluation
❓FAQ on Keras/TensorFlow
What is a convolutional neural network (CNN) and how does it differ from other types of neural networks?
What are some practical applications of CNNs in computer vision and image processing?
What programming languages and libraries are commonly used for implementing CNNs, and why?
How do I prepare my data (images, videos, etc.) for use in a CNN, and what are some common preprocessing techniques?
What are the key components of a CNN architecture, such as convolutional layers, pooling layers, and fully connected layers, and how do they work?
How do I train a CNN using TensorFlow and Keras, and what are some common optimization techniques?
How can I monitor the performance of my CNN during training, and what metrics should I use to evaluate its accuracy?
How can I fine-tune a pre-trained CNN on my own data, and what are some best practices for transfer learning?
How do I test my CNN on new data, and what are some common approaches for visualizing and interpreting its outputs?
What are some current research trends and challenges in CNNs, such as deep learning interpretability, adversarial attacks, and computational efficiency?
⭐️ Time Stamps:⭐️
00:00-00:34: Introduction
00:34-01:57: Preview
01:57-02-50: Normalizing Image Data
02:50-03:30: CIFAR-10
03:30-03:50: Defining a simple CNN Model in Keras
03:50-04:39: General Structure
04:39-06:36: Convolutional Blocks
06:36-07:34: Flatenning Activation Maps
07:34-08:10: Creating the Model
08:10-08:47: Compiling the Model
08:47-10:00: Training the Model
10:00-11:09: Results
11:09-12:48: Dropout
12:48-13:22: Training & Validation Curves
13:22-14:32: Saving & Loading Models
14:32-15:14:Model Evaluation
15:14-16:38: Predict Method
16:38-18:34: Confusion Matrix
18:34-19:13: Conclusion
Resources:
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Processing, Image Classification, Object Detection, Face Detection, Face Recognition, YOLO, Segmentation, Pose Estimation, and many more using OpenCV(Python/C++), PyTorch, and TensorFlow.
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[@joesthumbsticksarena2019]

Thanks for teaching. ❤

[@Trakushun]

Love it! So complete! I have a new sub

[@LearnOpenCV]

Thanks for subscribing! Welcome to our community.

[@vigneshsuvarna9452]

good one

[@LearnOpenCV]

Thank you so much!

[@xXMaDGaMeR]

transformer and vision transformer next

[@xXMaDGaMeR]

transformer and vision transformer next

[@LearnOpenCV]

Hi, you can check out our blog post to know more about vision transformers using pytorch. learnopencv.com/the-future-of-image-recognition-is-here-pytorch-vision-transformer/