Learn TensorFlow in an Hour
Learn to build a Handwriting Recognition deep neural network using Computer Vision in this one hour course.
Machine Learning,Artificial Intelligence,Neural Networks,ChatGPT,TensorFlow,
Lectures -12
Duration -50 mins
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Course Description
Tensorflow is a program that helps engineers build and train machine learning models. In this course, you will learn about Tensorflow and how to build AI models using TensorFlow.
TensorFlow is flexible and powerful. It can work work with any type of dataset: small or large. In this course, you will learn what Tensors are, what Tensorflow is, and how to work with it in detail.
Tensorflow can also work with GPUs and TPUs, which are types of computer chips built to extend TensorFlow capabilities. These chips make Tensorflow run faster, which is helpful when you have a lot of data to work with.
At the end of the course, you will have a good understanding of what TensorFlow is and how to use it to model data. We will also be building a computer vision project where we will build a simple tensorflow model to recognize handwritten images.
Google colab notebook: https://colab.research.google.com/drive/1y-R4PnqIAcjB2Y41CwbeYM6sWRZVHRht#scrollTo=AiXxTaIrGXB8
Let's get started!
Goals
What will you learn in this course:
The goal of our project is to create a model that can predict handwritten digits, which is a common problem in the field of machine learning.
To get started, we're going to be using a dataset called MNIST, which is a collection of 70,000 handwritten digits labeled from 0 to 9. We'll use this dataset to train our model to recognize different digits and make predictions on new, unseen images.
We'll design our neural network architecture using TensorFlow's Keras API. This is a really cool tool that allows us to build complex neural networks with just a few lines of code. Our network will consist of multiple layers, including a convolutional layer, a pooling layer, and a dense layer. These layers will be used to extract relevant features from the images and classify them into the correct digit category.
Once we've designed our model, we'll train it using the training dataset and evaluate its performance on the testing dataset. This will help us see how well our model is able to generalize to new, unseen images. We'll also fine-tune our model by adjusting the hyperparameters and repeating the training process until we achieve satisfactory accuracy.
By the end of this course, you'll have a functional deep-learning model that can accurately predict handwritten digits. You will build a fully functional neural network using Google Colab notebook. Once you are done with the course, you can share the links to your notebook in the class gallery and comment on other's notebooks. I ll add my feedback to your notebooks as well.
Prerequisites
What are the prerequisites for this course?
Basic knowledge of how machine learning works. If you are new to machine learning, here are two videos to help you get started.
Machine learning basics: https://www.youtube.com/watch?v=ukzFI9rgwfU
Deep Learning basics: https://www.youtube.com/watch?v=6M5VXKLf4D4
Curriculum
Check out the detailed breakdown of what’s inside the course
Introduction
2 Lectures
- Welcome to the course 01:16 01:16
- Tensors and Tensorflow 06:18 06:18
Tensorflow Basics
5 Lectures
Building the Model
2 Lectures
Training the Model
2 Lectures
Conclusion
1 Lectures
Instructor Details
Manish Shivanandhan
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