Building Neural Network Model from scratch using MNIST dataset : Part I
Welcome to this hands-on tutorial where we'll delve into the exciting world of Neural Networks and explore the process of building a powerful image classification model from scratch using the famous MNIST dataset. The MNIST dataset consists of 28x28 grayscale images of handwritten digits from 0 to 9, making it an ideal starting point for understanding the fundamentals of deep learning.
Important: In this part of building model I am going to calculate error and accuracy
and all of the rest training and evaluation will be cover in next part.
In this video, we'll start from the basics and guide you through each step of constructing your very own Neural Network without relying on pre-built libraries. By doing so, you'll gain a comprehensive understanding of the key components that constitute a Neural Network and how they come together to make predictions.
Here's a sneak peek of what we'll cover in this tutorial:
1. Introduction to MNIST dataset: We'll kick things off with a brief overview of the MNIST dataset, its structure, and its significance as a benchmark in the field of machine learning.
2. Data Preprocessing: Before feeding the data to our Neural Network, we need to preprocess it by normalizing, reshaping, and encoding the labels for training purposes.
3. Building Blocks of a Neural Network: Understand the key components, such as neurons, layers, and activation functions, that constitute a Neural Network architecture.
4. Initialization and Forward Propagation: Dive into the mechanics of initializing the weights and biases of our model and implementing the forward propagation step to make predictions.
5. Implementing Backpropagation: Discover the crucial process of backpropagation, where we fine-tune our model's parameters to improve its performance and accuracy.
6. Training the Model: Witness the model learning from the training data and see how it gradually minimizes the error using gradient descent optimization.
7. Testing and Evaluation: Evaluate the performance of our trained model on the test data to assess its accuracy and generalization capabilities.
8. Challenges and Improvements: Discuss common challenges faced during building Neural Networks from scratch and explore potential improvements to the model.
Throughout this journey, we'll provide clear explanations, code demonstrations, and insightful tips to help you grasp the essential concepts effectively. By the end of this tutorial, you'll have gained invaluable insights into Neural Networks and be well-equipped to embark on your own deep learning projects.
Join us in this exciting adventure of creating a Neural Network model from scratch, and let's empower ourselves with the knowledge to unlock the potential of artificial intelligence! Don't forget to like, subscribe, and hit the notification bell to stay updated with the latest content. Happy coding!
Welcome to this hands-on tutorial where we’ll delve into the exciting world of Neural Networks and explore the process of building a powerful image classification model from scratch using the famous MNIST dataset. The MNIST dataset consists of 28×28 grayscale images of handwritten digits from 0 to 9, making it an ideal starting point for understanding the fundamentals of deep learning.
Important: In this part of building model I am going to calculate error and accuracy
and all of the rest training and evaluation will be cover in next part.
In this video, we’ll start from the basics and guide you through each step of constructing your very own Neural Network without relying on pre-built libraries. By doing so, you’ll gain a comprehensive understanding of the key components that constitute a Neural Network and how they come together to make predictions.
Here’s a sneak peek of what we’ll cover in this tutorial:
1. Introduction to MNIST dataset: We’ll kick things off with a brief overview of the MNIST dataset, its structure, and its significance as a benchmark in the field of machine learning.
2. Data Preprocessing: Before feeding the data to our Neural Network, we need to preprocess it by normalizing, reshaping, and encoding the labels for training purposes.
3. Building Blocks of a Neural Network: Understand the key components, such as neurons, layers, and activation functions, that constitute a Neural Network architecture.
4. Initialization and Forward Propagation: Dive into the mechanics of initializing the weights and biases of our model and implementing the forward propagation step to make predictions.
5. Implementing Backpropagation: Discover the crucial process of backpropagation, where we fine-tune our model’s parameters to improve its performance and accuracy.
6. Training the Model: Witness the model learning from the training data and see how it gradually minimizes the error using gradient descent optimization.
7. Testing and Evaluation: Evaluate the performance of our trained model on the test data to assess its accuracy and generalization capabilities.
8. Challenges and Improvements: Discuss common challenges faced during building Neural Networks from scratch and explore potential improvements to the model.
Throughout this journey, we’ll provide clear explanations, code demonstrations, and insightful tips to help you grasp the essential concepts effectively. By the end of this tutorial, you’ll have gained invaluable insights into Neural Networks and be well-equipped to embark on your own deep learning projects.
Join us in this exciting adventure of creating a Neural Network model from scratch, and let’s empower ourselves with the knowledge to unlock the potential of artificial intelligence! Don’t forget to like, subscribe, and hit the notification bell to stay updated with the latest content. Happy coding!
