with Python 3 & OpenCV

In this tutorial, we will learn how to build a software pipeline for tracking road lanes using computer vision techniques.

*challenges in road lane detection ?*

The lines on the road that show us where the lanes are act as our constant reference. We are using canny detector-Hough transform based lane detection.

*PostgreSQL*

In your Data science journey you wont be receiving data in excel sheets. Data will mostly in DB from where we have to extract necessary data. Here we will see SQL data base postgresql and how to access it in your python code for your AI or Data Science activities.

*What is a DataBase?*

A database is a collection of tables related to each other via columns. For most real-world projects, a database is a must. We can use SQL (Structured Query Language) to create, access, and manipulate data. …

*Residual Neural network on CIFAR10*

In my previous posts we have gone through

- Deep Learning — Artificial Neural Network(ANN)
- Tensors — Basics of PyTorch programming
- Linear Regression with PyTorch
- Image Classification with PyTorch
*— logistic regression* - Training Deep Neural Networks on a GPU with PyTorch
- Image Classification with CNN

*This Article is Based on Deep Residual Learning for Image Recognition from He et al. [2] (Microsoft Research): **https://arxiv.org/pdf/1512.03385.pdf*

Residual Network (ResNet) is a Convolutional Neural Network (CNN) architecture which can support hundreds or more convolutional layers. ResNet can add many layers with strong performance, while previous architectures had a drop…

*PyTorch on CIFAR10*

In my previous posts we have gone through

- Deep Learning — Artificial Neural Network(ANN)
- Tensors — Basics of PyTorch programming
- Linear Regression with PyTorch
- Image Classification with PyTorch
*— logistic regression* - Training Deep Neural Networks on a GPU with PyTorch

*Let us try to classify images using Convolution*

*MNIST using feed forward neural networks*

In my previous posts we have gone through

- Deep Learning — Artificial Neural Network(ANN)
- Tensors — Basics of PyTorch programming
- Linear Regression with PyTorch
- Image Classification with PyTorch
*— logistic regression*

*Let us try to by using feed forward neural network on MNIST data set.*

*Step 1 : Import libraries & Explore the data and data preparation*

With necessary libraries imported and data is loaded as pytorch tensor,MNIST data set contains 60000 labelled images. Data is split into training and validation set with 50000 and 10000 respectively with random split.

`val_size = 10000 train_size…`

*CIFAR 10 Data set using logistic regression*

In my previous posts we have gone through

- Deep Learning — Artificial Neural Network(ANN)
- Tensors — Basics of pytorch programming
- Linear Regression with PyTorch

*Let us try to solve image classification of **CIFAR-10** data set with Logistic regression.*

*Step 1 : Import necessary libraries & Explore the data set*

We are importing the necessary libraries pandas , numpy , matplotlib ,torch ,torchvision. With basic EDA we could infer that CIFAR-10 data set contains 10 classes of image, with training data set size of 50000 images , test data set size of 10000.Each image…

*Your first step towards deep learning*

In my previous posts we have gone through

Here we will try to solve the classic linear regression problem using pytorch tensors.

**1 What is Linear regression ?**

y = Ax + B.

A = slope of curve

B = bias (point that intersect y-axis)

y=target variable

x=feature variable

We have discussed the concept of deep learning and neural network in our previous post. In this post we will discuss about the basic element of a neural network program which is tensor.

**1 What is a Tensor ?**

A tensor is the primary data structure of neural network. Mathematical generalization of a tensor can be understood more precisely with the following table.

*Building your first neural network in less than 30 lines of code.*

**1.What is Deep Learning ?**

Deep learning is that AI function which is able to learn features directly from the data without any human intervention ,where the data can be unstructured and unlabeled.

**1.1 Why deep learning?**

ML techniques became insufficient as the amount of data is increased. The success of a model heavily relied on feature engineering till last decade where these models fell under the category of Machine learning. Where deep learning models deals with finding these features automatically from the raw data.

**1.2 Machine learning…**

MBA,M.Tech-ML Engineer/Data scientist