Anatomy Of A Deep Neural Network (Dnn)

This section outlines the fundamental structure and components of deep neural networks, including layers, activation functions, parameters, training methodologies, and loss functions.

Layers: Input → Hidden → Output

This section introduces the foundational structure of Deep Neural Networks (DNNs), detailing how input layers, hidden layers, and output layers interact.

Activation Functions: Relu, Sigmoid, Tanh

Activation functions are crucial for introducing non-linearity in deep neural networks, with ReLU, Sigmoid, and Tanh being key examples.

Parameters: Weights And Biases

Training: Gradient Descent + Backpropagation

This section introduces the core training techniques of gradient descent and backpropagation in deep learning.

Loss Functions: Cross-Entropy, Mse, Hinge

This section covers fundamental loss functions used in neural network training, specifically cross-entropy, mean squared error (MSE), and hinge loss.

Convolutional Neural Networks (Cnns)

This section covers the essential components and use cases of Convolutional Neural Networks (CNNs), focusing on their architecture and application in image processing tasks.

Use Case: Image Classification, Object Detection, Facial Recognition

This section examines the application of Convolutional Neural Networks (CNNs) for image classification, object detection, and facial recognition.

Key Concepts

This section introduces key architectures used in deep learning, including CNNs, RNNs, Transformers, and GANs.

Popular Architectures

This section discusses advanced deep learning architectures like CNNs, RNNs, Transformers, and GANs, with a focus on their structures and applications.

Recurrent Neural Networks (Rnns) And Lstms

RNNs and LSTMs are neural network architectures tailored for sequential data, capturing dependencies over time.

Use Case: Time Series, Speech Recognition, Nlp

This section highlights the application of recurrent neural networks (RNNs) and long short-term memory networks (LSTMs) in tasks like time series analysis, speech recognition, and natural language processing.

Rnn

This section explores Recurrent Neural Networks (RNNs) and their ability to process sequential data, while introducing Long Short-Term Memory (LSTM) networks as a solution to RNN limitations.

Lstm / Gru

LSTM and GRU are advanced recurrent neural network architectures that effectively handle long-term dependencies and mitigate issues like vanishing gradients.

Transformer Models

This section on Transformer Models introduces their structure and significance in NLP, highlighting the self-attention mechanism and parallel training capabilities.

Use Case: Nlp, Translation, Summarization, Generative Ai

This section explores the use of Transformer models in Natural Language Processing (NLP), emphasizing their applications in translation, summarization, and generative AI.

Key Elements

This section highlights crucial architectures in deep learning, focusing on CNNs, RNNs, LSTMs, Transformers, and GANs, along with their functionalities and applications.

Popular Models

This section covers the most popular deep learning models, including CNNs, RNNs, Transformers, and GANs, along with their applications and structural nuances.

Generative Adversarial Networks (Gans)

Generative Adversarial Networks (GANs) are a class of neural networks designed for generating realistic data by pitting two networks against each other.

Use Case: Image Generation, Deepfakes, Data Augmentation

This section discusses Generative Adversarial Networks (GANs) and their applications in image generation, deepfakes, and data augmentation.

How Gans Work

Generative Adversarial Networks (GANs) consist of a generator that creates fake data and a discriminator that evaluates it, leading to powerful applications like image generation.

Variants

This section introduces variants of advanced deep learning models, highlighting their unique features and applications.

Training Techniques And Optimizers

This section details key training techniques and optimizers used in deep learning, enabling effective model training and performance enhancement.

Technique Purpose

This section outlines the fundamental techniques used in training deep neural networks and their purposes.

Optimizers

Optimizers play a crucial role in training deep neural networks by determining how the model's weights are updated during training.

Regularization

Regularization techniques help prevent overfitting in deep learning models by introducing constraints during the training process.

Learning Rate

The learning rate is a critical factor in the training of deep neural networks, influencing how quickly the model learns from its errors.

Schedulers

Schedulers are tools used in training deep learning models to adjust the learning rate based on certain criteria, improving convergence during training.