The Complete Self-Driving Car Course - Applied Deep Learning

Learn to load and display a road image for lane-line identification using OpenCV in Python, using cv2.imread, cv2.imshow, and cv2.waitKey to render the test image.

Learn canny edge detection to reveal object boundaries by analyzing intensity gradients, and implement grayscale conversion with numpy and OpenCV before applying Gaussian blur for road image edge detection.

Convert the image to grayscale, then reduce noise with a five by five gaussian blur to smooth the image for more accurate edge detection.

Apply canny edge detection to identify image edges by computing gradients in x and y on a blurred image, using thresholds 50 and 150 to trace strongest edges.

Apply canny edge detection to identify lane lines by defining a triangular region of interest and masking to isolate the road using fill poly.

Use binary numbers and bitwise and to create a region of interest mask for a canny image, enabling lane detection with a triangular polygon region of interest and line extraction.

Apply the Hough transform to the gradient image to detect straight lane lines, mapping lines in Cartesian space to rho and theta in Hough space and voting for the line.

Apply the Hough transform to detect lane lines from a cropped gradient image, tune rho and theta resolutions and thresholds, draw lines, and blend them onto the original road image.

Optimize lane visualization by averaging detected lines from the Hough transform into left and right lane lines, using slope-intercept and coordinate calculations, then blend with the color image.

Extend a lane-detection algorithm from images to video by processing each frame with Canny and Hough transforms, averaging detected lines, and displaying results in real time.

Explore neural networks inspired by biological neurons, from the perceptron to backpropagation, and learn how gradient descent trains weights to predict steering angles for autonomous driving.

Explore machine learning systems that learn from experience to analyze data, improve, and predict outcomes, including driving, speech recognition, and object detection, with supervised and unsupervised methods.

Explore supervised learning with a friendly linear regression example that predicts house price from size using a best-fit line and labeled data for self-driving car applications.

Learn how classification, a supervised learning approach, uses past labeled data to map inputs like blood sugar and age to diabetes or not, via a separating line in self-driving cars.

Build and optimize a linear model to classify health data by age and blood sugar, using weights and bias to separate diabetic from non-diabetic patterns via a score.

Explore the perceptron, a neural network node that sums weighted inputs like age and blood sugar with a bias, then uses a step activation to classify diabetic versus non-diabetic.

The lecture explains how a perceptron uses a weighted linear combination and an activation function to classify data as 0 or 1, showing blood sugar's higher influence than age.

Set up the initial stages of the neural network project by generating two data regions and plotting them with NumPy and Matplotlib in Jupyter Lab, preparing a linear separation task.

Start with a random line defined by weights w1 and w2 and iteratively adjust them via gradient descent to minimize classification error, guided by the learning rate.

demonstrate how the sigmoid activation provides probabilities instead of a step function and how a continuous error function drives weight updates in a perceptron-based model to minimize misclassifications.

Implement the sigmoid function in a perceptron model to predict point probabilities, include a bias, and compute the linear combination via matrix multiplication, enabling gradient descent and cross-entropy later.

Compare linear models by applying cross entropy to favor high probabilities for positive points and low probabilities for negative ones, then compute the cross entropy formula for gradient descent.

Apply cross-entropy as the error measure for a linear classifier, compute labels and probabilities, and prepare to minimize the total error with gradient descent.

Apply gradient descent to minimize the cross-entropy error with a linear model by updating weights and bias via the gradient scaled by a learning rate.

Apply gradient descent to minimize the error and update a linear classifier's weights with a learning rate alpha. Initialize parameters to zeros and avoid hard-coding to improve data classification.

Review the single perceptron: apply gradient descent to minimize error and adjust weights and bias, forming a supervised learning model that classifies labeled data and predicts inputs via feed forward.

Harness perceptrons to train a labeled-data linear model with minimal error that predicts new input data, then explore easier implementations that reveal how training occurs under the hood.

Learn to use the keras library to build a simple perceptron-based neural network with concise code and easy training of a basic deep network.

Train a simple two-input perceptron with a sigmoid output using Keras on top of TensorFlow, and monitor loss and accuracy while installing TensorFlow and Keras via pip to begin coding.

Learn to build a perceptron in Keras with a two-input, one-output dense layer using sigmoid activation. Train with Adam and binary cross-entropy; plot accuracy and loss with matplotlib.

Train and visualize a neural network's decision boundary with Keras predictions by plotting a 50-by-50 grid and contouring probabilities. Learn to predict a single point and evaluate model accuracy.

Leverage the Keras library to train deep neural networks, from simple perceptrons to multi-layered convolutional networks, and prepare for the next section.

Explore deep neural networks and the move from perceptrons to complex models, then use convolutional neural networks to extract features from traffic signs for classifying them and guiding steering.

Combine two perceptrons to form non-linear boundaries by weighting their outputs, adding a bias, and applying a sigmoid activation, revealing how linear models create complex neural networks.

Explore how neural networks combine perceptrons through weights and biases, using sigmoid activations to form nonlinear models with hidden layers for complex data classification.

Explore how feedforward neural networks use input, hidden, and output layers, with weighted connections and biases, activated by functions like sigmoid and relu, trained by gradient descent to minimize error.

Apply backpropagation and gradient descent to train a deep neural network, minimizing cross-entropy error through successive feedforward and backpropagation updates of all weights.

Explore how backpropagation reverses feedforward to minimize cross-entropy through gradient descent, updating weights with a 0.03 learning rate in a deep network for nonlinear classification.

Code and train a deep neural network in Jupyter Lab using scikit-learn's make_circles dataset, building a Keras model with dense layers and binary cross-entropy, visualizing accuracy and the decision boundary.

Extend deep neural networks and a Keras-based binary classifier to handle multi-class datasets, moving from binary classification of class one and class zero to multi-class learning.

Explore multi-class classification using real-world problems, like mnist digits and traffic signs. Begin with five two-dimensional x and y points, and learn the new tool in the next video.

Explore how softmax converts multi-class scores into probabilities for three classes, using one-hot encoding and training through gradient descent and backpropagation.

Explore cross entropy as a measure of neural network error for binary and multi-class data, use one-hot encoding, and minimize it with gradient descent and backpropagation in keras.

Design a simple multi-class neural network to classify a three-class blob dataset using one-hot encoding, softmax activation, and categorical cross-entropy, then train and visualize the decision boundary.

Finish multi-class classification and apply learned methods to train a model that classifies between various images, building on work with simple x y dimensional data sets.

Explore the Mnist image recognition dataset and how deep neural networks fit image data, then discuss validation and test sets and their role in generalization and hyperparameter tuning, with code.

Explore the Mnist dataset of handwritten digits and learn how a deep neural network with 784 input nodes, hidden layers, and a 10-node output classifies digits 0 to 9.

Train a neural network to classify handwritten digits using training, validation, and test sets, balance capacity with generalization, and use regularization to prevent overfitting.

Learn how hyperparameters shape learning in deep learning, using training and validation sets to tune learning rate and depth, prevent overfitting, and assess generalization on unseen test data.

Load the Mnist data, verify shapes, visualize samples, one-hot encode labels, normalize and flatten images to 784 features, and prepare the data for training a deep neural network.

Implement a deep neural network to classify handwritten digits from 28x28 images. Use 784 input pixels and 10 output classes with softmax and relu activations, and discuss overfitting.

We explored classifying images with deep neural networks, noting the need for higher capacity and computing power; convolutional neural networks offer a more efficient approach, to be discussed next.

Explore convolutional neural networks, the go-to model for image classification, and learn how convolutional layers extract distinctive features. Discover their computational efficiency and exceptional accuracy in predictions.

Learn how convolutional neural networks unlock efficient image recognition by exploiting spatial structure, reducing parameters through pooling, and outperforming traditional neural nets on MNIST-like tasks.

Convolutional layers apply small kernels to image patches to extract features, forming feature maps that detect edges. Sliding with a stride builds depth and enables translational invariance.

Learn how convolutional neural networks detect image features with kernels, share weights across locations, build feature maps for X and forward slash and backslash features, apply relu, and perform pooling.

Explore pooling in convolutional networks, focusing on max pooling with a 2x2 kernel and stride 2 to reduce dimensionality, curb overfitting, and preserve key features across feature maps.

Design a lenet-based convolutional neural network to classify MNIST digits, using 28×28×1 input, two conv-pool layers, flattening, dense layers, and a softmax output, trained with Adam on Colab.

Implement dropout in a convolutional neural network to reduce overfitting, evaluate with test data, and visualize convolutional features to boost MNIST accuracy.

Apply convolutional networks to classify traffic signs with strong accuracy, extending image classification skills to a traffic sign dataset, with the next section continuing this work.

Classify traffic signs across 43 classes and master preprocessing for effective deep learning. Explore importing data from external sources and assess if the Lenet model suffices for this challenging task.

Preprocess traffic sign data for self-driving cars by converting RGB images to grayscale, equalizing histograms, normalizing, and preparing 43-class train, validation, and test sets for CNNs.

Train a lenet convolutional neural network on traffic sign images, building conv and pooling layers, flattening, a dense layer with dropout, and softmax classification of 43 classes using Adam.

Fine-tune the network to boost accuracy and reduce overfitting on a traffic sign dataset by adjusting learning rate, adding filters and layers, and using dropout.

Use data augmentation with an image data generator including rotations, zooms, shifts, and shear to expand a traffic sign dataset, boosting validation accuracy above 99% and test accuracy near 97–98%.

Explore data augmentation to expand and diversify training data, enhancing the features a neural network extracts, and preview polynomial regression before diving into behavioral cloning and building a self-driving car.