Deep Learning for Beginner (AI) - Data Science

Explore deep learning as a subset of machine learning, using multi-layer neural networks to tackle complex patterns, and study artificial, convolutional, and recurrent neural networks with Python-based projects.

Compare biological and artificial neural networks, highlighting inputs, weights, summation, and activation functions across input, hidden, and output layers, with dendrites and neuron firing in a fully connected network.

Explain how activation functions decide whether neurons fire, enabling hidden and output layers, and why nonlinear activations solve complex patterns, with linear vs nonlinear types to be explored.

Explore how activation functions in the hidden layer enable non-linear modeling in a neural network, a key concept in deep learning, and how skipping them yields a simple linear model.

Explore how activation functions enable neural networks to handle nonlinear data, transforming linear boundaries into nonlinear mappings for complex problems.

Explore eight activation functions in deep learning, including step, sign, linear, rectified linear unit, leaky rectified linear unit, tanh, sigmoid, and softmax. Learn definitions, mathematics, and graphical perspectives.

Analyze the step function as a linear activation for regression, producing only 0 or 1 outputs in CNN layers and its vanishing gradient issue for multiclass classification problems.

Explore the sine activation function as a second linear activation for classification, producing -1 or 1 in hidden and output layers, and note its vanishing gradient and multiclass limitations.

Explore the linear activation function, defined as f(x)=x, with outputs spanning all real numbers, and its use in hidden and output layers for multiclass classification.

Explain the rectified linear unit (ReLU) activation function, its zeroing of negative inputs, and its use in regression and hidden layers, with vanishing gradient descent problem in multiclass classification.

Explore the leaky ReLU activation function, a modified linear function with a small negative slope k for negative inputs, allowing negative outputs and smoother gradients in hidden and output layers.

Explore the tangent hyperbolic activation function, a nonlinear option for classification in hidden and output layers, with a -1 to 1 range and vanishing gradient concerns.

Describe how the sigmoid activation function enables two-class classification using f(x)=1/(1+e^{-x}) with a 0.5 threshold, and its vanishing gradient drawback and multiclass limitations.

Master softmax activation, the extension of the sigmoid function in the output layer for multiclass classification. Explore its mathematical form and graphical representation, with outputs across all real numbers.

Explore artificial neural networks, the first category of deep learning, as information processing models inspired by the human brain. They learn by example and excel at pattern recognition and classification.

Explore how an artificial neural network is structured with input, hidden, and output layers, showing cell connectivity and configurations with multiple inputs, hidden layers, and a single output.

Explore a complex artificial neural network model with multiple inputs, several hidden layers, and multiple outputs, highlighting full inter-layer connectivity where every cell links to the next layer.

Explore a labelled artificial neural network model, detailing inputs, weights, biases, hidden and output layers, and how summation, activation functions, and backpropagation optimize outputs.

Explain forward propagation in an artificial neural network by moving from the input layer to a dense layer with weights and bias, and then to the output.

Describe backward propagation, moving from the output toward the input to update weights and biases during training, with epochs combining forward and backward passes.

Import libraries, load moon dataset, and build a sequential neural network in Python with two hidden layers and sigmoid output. Train for 150 epochs to reach 97 percent accuracy.

Explore convolutional neural networks (CNNs) in deep learning as models that analyze images, detect patterns, and perform classification using convolutional layers and the convolutional operation.

Explore the block diagram of a convolutional neural network, detailing convolutional layers, filters, kernels, padding, stride, activation, pooling, then flattening and a fully connected layer for image feature extraction.

Use a filter kernel as a feature detector to detect image features and support image classification in CNNs. Treat the kernel as a small matrix with configurable rows and columns.

explore how a 3x3 kernel reduces a 5x5 image to a 3x3 feature map in a CNN, using dot products, stride, and the output formula.

Discover how low level filters in convolutional neural networks detect minor image details, like lines, dots, and small spots, by focusing on small features rather than the whole image.

Explore mid-level filters in CNNs and how they detect sufficient image details—like eyes, nose, and mouth—compared to low-level filters that capture only minor features.

Explore how high level filters in cnn detect image details, such as full faces or whole bodies, and how they differ from low and mid level filters across three types.

Define stride as the symmetrical movement of a kernel or input image in a convolutional neural network, and show how it reduces dimensions with values like 1, 2, or 3.

Demonstrate how stride in a convolutional neural network governs dimension reduction by sliding a 3x3 kernel over a 5x5 image to yield 3x3 or 2x2 feature maps.

Explore padding in convolutional neural networks, a technique that adds border pixels with zeros to preserve all image features and resolve border issues, alongside stride trade-offs.

Explain padding in CNNs using a 5x5 input and 3x3 kernel to preserve information and produce a 5x5 feature map, with stride one and zero border padding.

Master pooling in convolutional neural networks as a dimensionality reduction method that preserves important features while reducing the feature map size with stride to speed up processing and save memory.

Explore max and average pooling in CNNs, with max pooling selecting the maximum value. Shows a 2x2 pooling on a 5x5 feature map for dimensionality reduction.

Apply average pooling in CNNs with a 2x2 filter and stride 2 to reduce a 5x5 feature map to 2x2, averaging four pixels, then flatten for a fully connected layer.

Harness flattening to convert a 3d feature map into a 1d column in a CNN, preparing data with strides and padding for the fully connected layer.

Learn how a CNN performs feature extraction by reducing an eight-by-eight input to a two-by-two feature map, flattening it to one dimension, and feeding it into a fully connected layer.

Build a Python cnn using a sequential model with 32 and 64 filter conv layers, max pooling, flatten, dense layers, and train with ten epochs, then evaluate accuracy around 69%.

Discover the basics of recurrent neural networks for sequential data, where the output reconnects to the same node to create a periodic operation in deep learning.

Explore a graphical representation of a recurrent neural network (RNN) model, showing input, hidden, and output layers with feedback connections that enable sequential data processing such as translation.

Depict the typical recurrent neural network with input, hidden, and output layers, where outputs reconnect to the same hidden layer for sequential data, differing from PN and CNN models.

Explore a simple recurrent neural network with input, hidden, and output layers, using recurrent connections in the hidden layer to handle sequential data via summation and activation.

Explore the 1 to 1 recurrent neural network, a simple model with one input, one output, and a single hidden layer, illustrated by image classification that outputs the same image.

Explore the one-to-many rnn model, where a single input yields multiple outputs through connected hidden layers in sequential data, illustrated by image captioning that turns an image into several captions.

Explore many-to-many recurrent neural networks with multiple inputs and outputs, driven by sequential data and hidden-layer connections, illustrated by Google translator translating French paragraphs to English.

Explains many-to-one recurrent neural network models with multiple inputs and a single output, highlighting sequential data and hidden-layer connections, with examples like translation, rating systems for SEO, and image captioning.