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Neural Networks News
Medium
12h
212
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Introduction to Recurrent Neural Networks: Classic RNN, LSTM, and GRU
- Classic RNNs struggle to retain information over long sequences due to the vanishing gradient problem.
- LSTMs are powerful but computationally expensive and require significant memory.
- GRUs may not perform as well as LSTMs when dealing with extremely long or complex dependencies.
- Even the best models, like LSTMs, cannot anticipate unexpected events in the stock market.
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Medium
1d
283
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Recurrent Neural Network (RNN)
- Recurrent Neural Network (RNN) is a type of artificial neural network designed for processing sequential data.
- The main differences between a RNN and a traditional feedforward neural network revolve around how they process data, especially with respect to sequential and temporal data.
- In RNN, the hidden state at each time step depends on the hidden states from previous time steps.
- The parameters (weights and biases) are updated using an optimization algorithm like Stochastic Gradient Descent (SGD).
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Medium
2d
101
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Large Language Models: A short introduction
- A Large Language Model, or LLM, is a machine learning model that generates text based on the next word in a sentence with associated probabilities.
- LLMs are trained on enormous training datasets scraped from the internet to improve their ability to predict the next word with accuracy.
- The Large in LLMs refers not just to the size of the training data, but to the massive number of parameters that need to be adjusted.
- Transformers, initially developed by Google, is a new type of architecture that has allowed parallelization and attention operations in the LLM training process.
- LLMs have revolutionized a range of natural language and information retrieval tasks from translation to chatbots, to recommendation systems.
- They have been productized in a range of applications from Facebook's Meta AI to Google's Gemini and OpenAI's ChatGPT.
- LLMs are nascent, evolving at lightning speed and provide a wealth of opportunities for researchers and companies, and easier access to information for consumers.
- Genomic Language Models, or gMLMs, are also used to accelerate research on genome interactions - a task on which LLMs are proving particularly effective.
- LLMs have been highly successful so far, but improvement in their accuracy is essential to their future development and productivity.
- LLMs are tools that enable fast feedback loops making steady progress in science and helping companies provide customers ameliorated services.
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Medium
5d
118
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Weight in Neural Network
- In a neural network, weights control the strength of the connections between neurons and are adjusted during training to minimize error and learn patterns.
- Weights determine the influence of inputs on neurons and define information flow in a network.
- Backpropagation is the algorithm used to update weights by propagating error through the network.
- The weights are updated by computing the gradient of the loss function and adjusting them in the direction that decreases the loss.
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Medium
1w
367
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Artificial Neural Network (ANN)
- Artificial Neural Network (ANN) serves as the framework for training neural networks, allowing them to learn by iteratively adjusting their weights and biases to minimize the loss function.
- ANN begins with an input layer and may have multiple hidden layers, depending on the individual’s choice. The input layer passes data to the hidden layers without performing any computations or transformations.
- The hidden layers consist of artificial neurons that perform computations to extract higher-level features from the inputs. These layers enable the network to capture non-linear relationships in the data.
- The output layer is the final layer of a neural network, responsible for producing the network's predictions or results based on the learned patterns. It receives inputs from the preceding hidden layer and applies transformations to deliver the desired output in a format suitable for the task.
- During training, weights are updated based on the gradients of the loss function with respect to each weight. This update process is facilitated by backpropagation and an optimization algorithm like stochastic gradient descent (SGD) or Adam.
- ANNs are sensitive to hyperparameters such as the learning rate, number of layers, and number of neurons. Proper initialization, optimization, and regularization of weights are crucial to ensure that the network converges to a good solution and generalizes well to unseen data.
- ANN comes with several drawbacks like high computational cost, sensitivity to hyperparameters, vanishing or exploding gradients, which limit its effectiveness in certain scenarios.
- Proper preparation and feeding of data into the input layer are crucial for ensuring the network can efficiently learn meaningful patterns during training. Backpropagation ensures that the network learns hierarchically, refining the lower-level features learned in earlier layers and the higher-level abstractions learned in later layers.
- Choosing the right loss function is vital for achieving task-specific goals in image-based neural networks. It must align with the data's nature and the model's expected output, as it directly influences the network's ability to learn effectively and generalize to unseen data.
- ANN is super important for running deep learning, preferably used on a not so large dataset.
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Medium
1w
289
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Prune a Neural Network with Simulated Annealing and Genetic Algorithm (Part 3 — Experiments)
- The primary data source used for this experiment is the well-known MNIST dataset, a standard in the machine learning community.
- The LeNet architecture (Lecun et al., 1998) is a classic convolutional neural network (CNN) widely used for image classification tasks.
- SimulatedAnnealingPruner was applied to prune the trained LeNet with the goal to optimize the pruning mask for each layer to achieve higher sparsity while maintaining the test loss.
- GeneticAlgorithmPruner was applied using a population-based approach to optimize the pruning masks for each layer, striking a balance between model sparsity and accuracy.
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Medium
1w
203
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Neural Networks for Time Series with Tensorflow Keras in Python
- Neural networks can be used for time series analysis in Python using Tensorflow (Keras).
- Different types of neural networks, such as feedforward, recurrent, and LSTM, can be used for time series forecasting.
- Neural networks can handle nonlinear relationships and multivariate data, improving forecasting accuracy.
- However, neural networks are prone to overfitting and are considered a 'black box' model.
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Hackernoon
1w
419
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How Gradient-Free Training Could Decentralize AI
- Recent advancements in building highly efficient large language models (LLMs) with only three types of weights using BitNet b1.58 architecture has made us wonder: would it be possible to directly produce small models without needing to train large models costing millions of dollars? However, training these smaller models would not be easy as gradient descent method does not work efficiently on these smaller models.
- Training LLMs requires massive GPU clusters, and even the smaller models derived from these LLMs require significant computational requirements for distillation and quantization; thereby, widening the gap between training and inference networks.
- Gradient-free solutions like evolutionary algorithms and random search may seem less efficient than gradient descent but are advantageous in cases where derivatives cannot be computed, like in the case of 1.58-bit neural networks that cannot be trained using gradient descent.
- The use of gradient-free training could lead to the repurposing of transistors to build ASICs specifically designed to efficiently run 1.58-bit networks, making the process faster, more scalable and energy-efficient.
- Decentralization in gradient-free training offers greater democratization in AI and could involve any device capable of running the network to participate in the training process. Decentralized systems similar to Bitcoin could be used to 'mine' neural networks, where the ASIC would run an NN very quickly, and those who succeed in finding effective parameters would earn a reward.
- Though it is unclear if gradient-free solutions could be effective, the potential rewards could be significant, and it is a field that needs deeper research. It could lead to the development of new and better LLMs, or even gradient-free fine-tuning, and the potential for decentralization in training is compelling.
- Further discussion on this topic is ongoing in the Reddit thread linked in the article, and more opinions and comments are welcome.
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Medium
3d
143
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Digging into the Surface of Artificial Intelligence
- Artificial Intelligence has become an improvising field after its initial development that started in mid-90s and gained fame in 2010.
- Machine Learning is a term used in AI that implies training machines to work autonomously in certain environments with minimum human intervention.
- To train Machine Learning models, a vast set of well-processed (structured) data are required. Machines are programmed to self-learn from their experience to get precise outcomes.
- Neural Networks use Networks to resemble the neuron network of a human brain to do complex tasks in an easier manner.
- Deep Learning uses various hidden layers to perform even more complex tasks in a much more efficient manner. The number of hidden layers represents the depth of Neural Networks.
- AI can be used in various technologies that are being used in our day-to-day lives, including facial recognition, virtual personal assistants, and fraud detection.
- Deep Learning models are more efficient in processing vast datasets than Machine Learning algorithms. However, certain tasks can be performed more efficiently by machine learning methodologies than deep learning models.
- In the future, advanced and well-trained Artificial Intelligence models will perform crucial tasks in constrained environments where humans cannot sustain normally, like space research and nuclear power plant works.
- Neurocomputing asserts that Artificial Intelligence without restriction surpasses human intelligence with probability one and Artificial Intelligence can reveal secrets of the brain.
- Machine Learning and Artificial Intelligence technology are evolving, making it possible to perform even more complex tasks where human negligence or minor errors can lead to catastrophic events.
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Medium
7d
265
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Understanding Recursive Neural Networks (RvNN) — Intuition and Applications
- Recursive Neural Networks (RvNNs) process data in tree structures.
- RvNNs use recursive processes to combine inputs and scale with input hierarchy.
- They are particularly suited for tasks like natural language processing.
- Despite challenges, advancements have made RvNNs more practical in recent years.
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Medium
1w
96
Pattern field theory: Validated?
- Pattern Field Theory (PFT) predicted three essential components for effective information processing: Quantum-like evolution of patterns, Categorical transformation of patterns, and Information flow dynamics.
- Meta's recent work on Memory Layers at Scale provides validation of PFT's predictions about pattern storage and retrieval.
- The Titans architecture implements PFT's predictions about dynamic learning and pattern evolution.
- The mathematical formulations in both papers provide validation of PFT's theoretical framework.
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Medium
1w
265
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Understanding Artificial Neural Networks (ANNs): A Beginner’s Guide
- An artificial neural network is a system of algorithms that mimics the way the human brain processes information.
- An ANN consists of neurons that work together to solve problems.
- Learning in an ANN involves adjusting weights and biases to minimize error through three main steps.
- ANNs are widely used in various fields.
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Medium
1w
397
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Why Everything You Know About Neural Networks Is Dead Wrong
- Discover how simplicity in neural networks can lead to efficiency and scalability.
- Challenging the notion that complexity is always better.
- Research by Sadamori Kojaku suggests that simpler neural networks can perform just as well or even better.
- This reveals a new understanding of artificial intelligence.
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Medium
2w
296
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Giving AI human eyes through CNN’s
- Our brains are wired to detect familiar shapes by noticing features such as lines, curves, and angles, and comparing them against stored memories.
- Optical Character Recognition (OCR) is a technology that allows machines to interpret text from images, scanned documents, or photos.
- When passing in an image that contains text within, AI models incorporate something called a convolutional neural network.
- A CNN consists of two main components: Convolution and pooling that are usually implemented multiple times to continue reducing images into more compact sizes. After this step, we get to the flattening layer.
- To build our CNN, we can use popular frameworks like PyTorch or TensorFlow, and the Mnist dataset which provides unique images of numbers that challenge our model's predictions.
- We start by importing libraries that store data and help us build the CNN. We then define our convolutional neural network.
- The built-in CrossEntropyLoss function helps us find the error in the neural network's guesses. Error measures how far our prediction is off from the real answer and helps the neural network eventually get the right answer if it is mistaken.
- We can set epochs and learning rates to ensure our model is more accurate, even though it takes longer to learn.
- An optimizer helps update the model's parameters based on the loss and accuracy of the model.
- The author of this article is a 15-year-old high school student who is fascinated by the power of AI and its impact on our society.
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Medium
2w
394
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Recurrent State Space Models — PyTorch implementation
- Recurrent State Space Models (RSSM) are essential for model-based reinforcement learning (MBRL) approaches where reliable models are built to predict the environment’s dynamics, and agents use these models to simulate future trajectories and plan actions in advance.
- This article overviews how to implement and train RSSM models using PyTorch.
- A RSSM relies on several model architectures, such as the Encoder which is a simple CNN projecting the input image to a one-dimensional embedding with BatchNorm used to stabilize training.
- The decoder is a traditional autoencoder architecture mapping the encoded observation back to observation space.
- The reward model uses s and h to output the parameters for a normal distribution from which we can obtain a reward and consists of three layers.
- The dynamics model requires prior and posterior state transition models, which approximate the prior and posterior state distributions using one-layered FFNs and return mean and log-variance of the respective normal distribution from which we can sample the states s.
- The generate_rollout method calls the dynamics model and generates a rollout of latent representations of the environment dynamics.
- Two core components when training the model are the buffer and the agent. The buffer stores past experiences from which we can train RSSM model whereas the agent is the interface between the environment and the RSSM.
- The train method calls the train_batch method, which samples observations, actions, and rewards from the buffer.
- This article provides a general introduction to the implementation of RSSMs in PyTorch, which are powerful in generating future latent state trajectories recurrently, enabling agents to plan future actions.
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