# Deep Learning for coders course (fast.ai)_SGD for a linear model and MNIST dataset with fastai library

This post is based on the course offered by Jeremy Howard and Rachel Thomas (https://course.fast.ai/). The material for this course is a book named Deep Learning for Coders with fastai and PyTorch. Sincere thanks to the book authors, Jeremy Howard and Sylvain Gugger. I also used https://docs.fast.ai/ information in this blog.

Weight assignment is the current value of model parameters. These weight assignments need to be able to update automatically to optimize the model. On the other words, we need some automatics means of testing the effectiveness of the current weight assignment in respect of actual performance. …

A quick summary of built-in methods for training and evaluation in TensorFlow

When we take a data set, it should be split into two sets, the training set and the test set. Most of the time near %80 of the data set goes into the training set and the rest of %20 goes into the test data set. The data set should be able to train, evaluate, and predict the model. TensorFlow provides some built-in APIs for training and validation.

First, we start with the method fit(). We can use fit() for supervised learning. The model has a method fit…

# A general overview of neural network

To understand deep learning, we initially need to understand a single biological neuron or perceptron model. Then, this idea can be extended to a multi-layer perceptron model, and finally, we can understand the deep learning neural network.

First, let’s start by defining what is a perceptron and why we need the adjustments values (weights and bias) in this model.

A perceptron is a form of the neural network. The perceptron model has three biological units. A mathematical expression for each part can be replaced for these biological units. The simplest perceptron model from a mathematical point of view consists of…

# Linear Fit using Gradient Descent with Numpy

Here, we apply the Gradient Descent method for line fitting (one of the simplest models) which is just a specific application of Gradient Descent.

We start by explaining what the Gradient Descent is. Let’s consider a potential field function over a vector space and call the function f(x). We aim to minimize or maximize the function in the vector space. The gradient descent method idea for minimization is to take very small repeated steps and follow the opposite direction of the gradient. If we travel along the direction of the gradient, we get a higher value for f(x). …

# Numpy Vs. Tensorflow

Here I am writing about the most interesting differences and similarities between Numpy and TensorFlow. I will update this post as I am learning more about the topics.

Numpy is a linear algebra library for python, and one of the most important and popular libraries in Data Science. TensorFlow is a reimplementation of the Numpy API and can be accessed as `tf.experimental.numpy`.

Numpy performs a wide variety of numerical computations in Python, and it is the fundamental package for scientific computing. It is generally used for multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to…

# Develop an Image Classifier Using Fastai

This post is based on the course offered by Jeremy Howard and Rachel Thomas (https://course.fast.ai/). The material for this course is a book named Deep Learning for Coders with fastai and PyTorch. Sincere thanks to the book authors, Jeremy Howard and Sylvain Gugger. I also used https://docs.fast.ai/ information in this blog.

Here, we start by providing an example to classify cat and dog breeds. We are using a dataset available in the fastai library. The name of the dataset is Oxford-IIIT Pet Dataset, and it has 37 different breeds types of cats and dogs images.

# Deep Learning for coders course (fast.ai) — Introduction

It is my summary and classification note (lesson 1) of the deep learning course. This course is offered by Jeremy Howard and Rachel Thomas (https://course.fast.ai/). The material for this course is a book named Deep Learning for Coders with fastai and PyTorch. Sincere thanks to the book authors, Jeremy Howard and Sylvain Gugger.In this lesson, we will learn the following topics:

1- A brief history of machine learning (ML)

2- Neural networks: A brief history

3- Arthur Samuel’s view of a machine learning model

4- What is a neural network?

5- The modern deep learning terminology

6- Limitations inherent to… ## Zora Hirbodvash

Ph.D. Candidate in Photonics (University of Ottawa)