Deep Machine Learning in AI - Volume : 1

Deep Machine learning

Deep Machine learning (ML) is a type of artificial intelligence that allows computer systems to improve their performance on a task without being explicitly programmed. This is achieved by training the system on a dataset, allowing it to learn patterns and make predictions or decisions. There are several different types of ML, including supervised learning, unsupervised learning, and reinforcement learning. Each type has its own set of algorithms and techniques, and is used for different types of problems.

we split the below topics into 3 Volumes as follows :Volume : 1

#Deep learning :

#Neural networks:

#Optimization:

Volume : 2

#Regularization:

#Convolutional neural networks (CNNs) :

#Recurrent neural networks (RNNs) :

Volume : 3

#Generative models :

#Reinforcement learning:

#Computer Vision:

Volume : 1

#Deep learning :

Deep learning is a subfield of machine learning that is concerned with the design and development of algorithms inspired by the structure and function of the brain, specifically artificial neural networks. These neural networks are called "deep" because they have multiple layers of interconnected nodes, or "neurons." Each layer processes the input data and passes it on to the next layer, allowing the network to learn increasingly complex features and representations of the data.

Deep learning has been particularly successful in tasks such as image and speech recognition, natural language processing, and computer vision. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are two popular types of deep learning architectures. CNNs are often used for image and video processing tasks, while RNNs are used for sequential data such as speech and language.

Deep learning models require large amounts of data and computational power to train, and have been enabled by advances in hardware, such as graphics processing units (GPUs), and software, such as open-source libraries like TensorFlow and PyTorch. The increasing availability of large datasets and the ability to train deep learning models on them has led to breakthroughs in many fields, such as healthcare, finance and transportation.

In summary, deep learning is a subfield of machine learning that uses deep neural networks with many layers to learn highly abstract and complex features from large datasets, which is proven to be effective in solving a wide range of problems in various industries.

Deep learning is a broad field with many topics and subtopics. Some of the main areas of focus include:

#Neural networks:

The basic building blocks of deep learning, including feedforward networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs).

A neural network is a type of machine learning model inspired by the structure and function of the brain. It consists of layers of interconnected nodes, called neurons, which are organized into input, hidden, and output layers. Each neuron receives input from other neurons, processes it using a set of weights, and generates an output. The weights of the neurons are adjusted during the training process to minimize the error between the model's predictions and the true values.

There are several types of neural networks, each with their own strengths and weaknesses:

Feedforward neural networks: the simplest type of neural network, in which the information flows in one direction from input to output.

Recurrent neural networks (RNNs): a type of neural network in which the output of a neuron is also fed back into itself, allowing the network to process sequential data such as speech and language.

Convolutional neural networks (CNNs): a type of neural network commonly used for image and video processing tasks, in which the neurons are organized into spatial maps and the connections between neurons are constrained to be local.

Autoencoder: a type of neural network that learns a compressed representation of the input data and can then be used for tasks such as dimensionality reduction and anomaly detection.

Generative Adversarial Networks (GANs): a type of neural network that consists of two parts: a generator network that creates new data, and a discriminator network that tries to distinguish the generated data from real data.

Transformer: a type of neural network architecture that uses self-attention mechanism, it was introduced in the field of natural language processing and became the state of the art in many NLP tasks.

Each of these neural networks have their own set of architectures and are used for different types of problems and datasets. With the advancements in deep learning, Neural networks are now able to solve complex problems such as image recognition, natural language processing and game playing with a high level of accuracy.

#Optimization:

Techniques for training deep learning models, such as backpropagation, stochastic gradient descent (SGD), and adaptive learning rate methods.

Optimization is the process of adjusting the parameters of a machine learning model, such as the weights of a neural network, to minimize a loss function, which measures the difference between the model's predictions and the true values. Optimization is a crucial step in training machine learning models, as it allows the model to learn from the data and improve its performance.

There are several optimization techniques that are commonly used in deep learning, including:

Stochastic Gradient Descent (SGD): the most basic optimization algorithm, which updates the parameters in the opposite direction of the gradient of the loss function with respect to the parameters.

Momentum: a variation of SGD that uses a "momentum" term to smooth out the updates and converge faster.

Nesterov Momentum: a variation of Momentum which incorporates the knowledge of where the next update would move before it happens.

AdaGrad: a method that adapts the learning rate for each parameter separately, based on the historical gradient.

Adadelta: a method that adapts the learning rate based on the historical gradient and the historical update.

Adam: a method that combines the ideas of AdaGrad and Momentum, and adapts the learning rate for each parameter separately based on the historical gradient and the historical update.

RMSprop: a method that similar to AdaGrad but uses a moving average of the squared gradient.

L-BFGS, Conjugate Gradient, Newton-Raphson are optimization algorithms which are used in more complex models and can be computationally more expensive than the above methods but can converge faster.

These methods differ in terms of their computational complexity, the amount of memory they require, and their ability to escape local minima. The choice of optimization algorithm depends on the specific problem and the properties of the data, and in practice, a combination of methods is often used.

Follow up : Deep Machine Learning in AI - Volume : 2