Here are summary of top Deep Learning algorithms for beginner in Data Science:
- Convolutional Neural Networks (CNNs): ConvNets or CNNs are a type of Neural Network specifically designed for image recognition and processing tasks. They are used for tasks such as image classification, object detection, and segmentation. A CNN consists of multiple layers, including convolutional layers, pooling layers, and fully connected layers. The convolutional layer is the core building block of a CNN, where a filter slides over the input image, computing dot products between the filter and overlapping regions of the image. These dot products are used to produce a feature map that encodes local patterns in the image. Pooling layers are used to reduce the spatial dimensions of the feature maps and make the network invariant to small translations in the input. Finally, the fully connected layer takes the feature maps and produces the final prediction using a softmax activation function for classification problems or a linear activation function for regression problems. CNNs have been very successful in image recognition tasks due to their ability to automatically learn hierarchical representations of the input data, including edges, shapes, textures, and higher-level features. They are also computationally efficient, as they can leverage the two-dimensional structure of the input data to reduce the number of parameters in the network.
- Recurrent Neural Networks (RNNs): a type of Neural Network specifically designed for processing sequential data such as time series, text, and speech. They are used for tasks such as language translation, speech recognition, and sentiment analysis. In an RNN, the hidden state of the network at one time step is passed as input to the next time step, allowing the network to maintain an internal representation of the entire sequence. This makes RNNs well suited to modelling sequences where the output at each time step depends on the inputs at all previous time steps. The hidden state is updated at each time step using an activation function, and the final output is typically produced by applying a fully connected layer to the final hidden state. One limitation of traditional RNNs is that they have difficulty modelling long-term dependencies in sequences, as the gradient signals can either vanish or explode during training, causing the network to forget or not effectively incorporate information from earlier time steps. To address this, variants of RNNs have been developed, such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs), which use gating mechanisms to allow the network to selectively retain or discard information from the hidden state.
- Long Short-Term Memory (LSTM): a type of Recurrent Neural Network (RNN) designed to handle the problem of vanishing gradients in traditional RNNs when modelling long-term dependencies in sequential data. LSTMs were introduced to address the issue of traditional RNNs losing information from the hidden state over time, as the hidden state is updated at each time step using an activation function. LSTMs use a gating mechanism to allow the network to selectively retain or discard information from the hidden state, allowing it to better capture long-term dependencies in sequential data. In an LSTM, the hidden state is passed through three different gates: an input gate, a forget gate, and an output gate. The input gate controls the amount of information to be added to the hidden state, the forget gate controls the amount of information to be discarded from the hidden state, and the output gate controls the information to be used to compute the output at each time step. This gating mechanism allows LSTMs to maintain a long-term memory of the sequence, allowing them to effectively model sequences where the output at each time step depends on the inputs at all previous time steps.
- Autoencoder: an unsupervised deep learning technique used for dimensionality reduction and feature learning. It is a type of neural network that is trained to reconstruct its inputs, meaning that it tries to learn a compact representation of the input data. Autoencoders consist of two main components: an encoder and a decoder. The encoder maps the input data to a lower-dimensional representation, known as the bottleneck or latent representation. The decoder then maps the bottleneck representation back to the original input space, reconstructing the input. The key idea behind autoencoders is to force the encoder to learn a compact, useful representation of the input data by minimizing the reconstruction error between the original input and its reconstruction. This allows the autoencoder to learn meaningful features from the input data in an unsupervised manner, without any labelled examples. Autoencoders have a wide range of applications, including data denoising, anomaly detection, generative models, and dimensionality reduction. They can also be used as pre-training layers for supervised deep learning models, providing a useful initialization for the network weights before fine-tuning on the task-specific data.
- Generative Adversarial Networks (GANs): a type of deep learning algorithm used for generative tasks such as image and video synthesis. It 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 deep learning model used for natural language processing tasks such as language translation and language generation. It uses self-attention mechanisms to weigh the importance of different parts of the input data.
- BERT: a transformer-based deep learning model pre-trained on a large corpus of text data and fine-tuned on specific natural language understanding tasks such as sentiment analysis, named entity recognition and question answering.
- YOLO (You Only Look Once): a popular deep learning-based method for object detection that is fast, accurate, and versatile. YOLO divides an image into a grid of cells and makes a prediction for the presence of objects in each cell. The prediction includes the class of the object, the location of the object within the cell, and the confidence score for the prediction. This allows YOLO to perform object detection in real-time, making it suitable for applications such as video analysis, self-driving cars, and security systems. YOLO has several advantages over other object detection methods, including its speed, accuracy, and ability to handle multi-object scenarios. YOLO can process images in real-time, making it well-suited for use in a wide range of applications. Additionally, YOLO can detect multiple objects in an image simultaneously, allowing it to handle complex scenes.
- Mask R-CNN: a popular deep learning-based computer vision technique for object instance segmentation. It is an extension of the Faster R-CNN object detection architecture, and is designed to perform object detection and segmentation in a single pass. Mask R-CNN works by first generating object proposals and then classifying and refining these proposals to produce accurate object detections. Additionally, Mask R-CNN also generates a binary mask for each object, which provides a pixel-level segmentation of the object. This allows the model to not only detect the presence of objects in an image, but also to determine their shape and position within the image. Mask R-CNN has been successfully applied to a variety of computer vision tasks, including instance-level semantic segmentation, medical image analysis, and autonomous vehicle perception. It has become a popular choice for computer vision researchers and practitioners due to its accuracy, speed, and versatility.
- U-Net: a deep learning model used for image segmentation tasks, it uses a convolutional neural network with an encoder-decoder architecture to segment images into different regions or objects.
Here is a list of resources for further reading on deep learning:
- “Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
- “Neural Networks and Deep Learning: A Textbook” by Charu Aggarwal
- “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurélien Géron
- “Deep Learning for Computer Vision” by Rajalingappaa Shanmugamani
- “Python Deep Learning: Exploring Deep Learning Techniques and Neural Network Architectures with PyTorch, Keras, and TensorFlow” by Ivan Vasilev, Daniel Slater, and Gianmario Spacagna
- “An Introduction to Deep Learning” by Charu Aggarwal
- “Deep Learning Cookbook” by Douwe Osinga
- “Deep Learning Specialization” on Coursera by Andrew Ng
- “TensorFlow for Deep Learning” by Bharath Ramsundar and Reza Bosagh Zadeh
- “Deep Learning for Natural Language Processing” by Jason Brownlee
These resources cover a wide range of topics in deep learning, including the fundamentals of neural networks, convolutional neural networks, recurrent neural networks, and advanced deep learning architectures, as well as their applications in various domains such as computer vision, natural language processing, and speech recognition.