Mastering Artificial Intelligence
Welcome to our tutorial on building a custom QnA agent with memory, using Wikipedia as the information source! In this code, we dive deep into the process of creating an intelligent agent that can remember previous interactions, providing more accurate and contextually relevant answers over time. Import LLM Import Tool Bind Tool with LLM Create […]
In Natural Language Processing (NLP), the way we represent text has a profound impact on the performance of our models. Tokenization, the process of breaking down text into smaller manageable units called tokens, is a foundational step in preparing text data for NLP tasks. While simple techniques like word-level tokenization exist, advanced methods like Byte […]
Quantization is a process used in machine learning and signal processing to reduce the precision or number of bits used to represent numerical values. The goal is to compress the data or model parameters, leading to reduced storage requirements, faster computation, and lower memory bandwidth. In the context of large language models (LLMs) like GPT, […]
Quantization and compression are two related but distinct concepts when it comes to large language models (LLMs) like GPT-3.5. Let’s explore the differences between quantization and compression in the context of LLMs: In summary, quantization specifically refers to the reduction of numerical precision in the model’s parameters, while compression is a broader concept that encompasses […]
Weight pruning is a technique used to reduce the size of a neural network by removing certain weights, typically those with small magnitudes, without significantly affecting the model’s performance. The idea is to identify and eliminate connections in the network that contribute less to the overall computation. This process helps in reducing the memory footprint […]
Introduction In the field of deep learning, activation functions play a crucial role in introducing non-linearity to neural networks, enabling them to model complex relationships. One such activation function that has gained popularity is the Gelu activation function. Gelu stands for Gaussian Error Linear Unit, and it offers a smooth and continuous non-linear transformation. In […]
Introduction The BLEU score, which stands for Bilingual Evaluation Understudy, is a metric commonly used to evaluate the quality of machine-generated translations compared to human translations. It measures the similarity between the machine-generated translation and one or more reference translations, assigning a numerical score between 0 and 1. The higher the BLEU score, the closer […]
Q1. For a multi-channel input feature map, we apply Max-pooling independently on each channel and then concatenate the results along the channel axis? True False Q2. A fully convolutional network can take as input the image of any size? True False Q3. In R-CNN, the bounding box loss is only calculated for positive samples (samples […]
Q1. The values in a filter/mask are called as Coefficients Weights Both of the above None of the above Q2. Which of the following networks uses the idea of Depthwise Separable Convolutions? AlexNet MobileNet ResNet VGG16 Q3. What is the output of a Region Proposal Network (RPN) at each sliding window location if we have […]
Q1. Which of the following object detection networks uses a ROI Pooling layer? R-CNN Fast R-CNN YOLO All of the above Q2. Which of the following techniques can be used to reduce the number of channels/feature maps? Pooling Padding 1×1 convolution Batch Normalization Q3. Which of the following networks has the fastest prediction time? R-CNN […]