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Implementation of Q-Learning with Python

Q-learning is a popular reinforcement learning algorithm used to make decisions in an environment. It enables an agent to learn optimal actions by iteratively updating its Q-values, which represent the expected rewards for taking certain actions in specific states. Here is a step-by-step implementation of Q-learning using Python: Image by Author 1. Import the necessary libraries: import numpy as np import random 2. Define the environment: # Define the environment env = np.array([[ 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 ], [ 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 ], [ 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 ], [ 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 ], [ 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 ], [ 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 ], [ 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 ], [ 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 ], [ 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0
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List of Deep Learning Algorithms you Should Know in 2023

 Deep learning is a branch of machine learning that uses artificial neural networks to perform complex calculations on large datasets. It mimics the structure and function of the human brain and trains machines by learning from examples. Deep learning is widely used by industries that deal with complex problems, such as health care, eCommerce, entertainment, and advertising. This post explores the basic types of artificial neural networks and how they work to enable deep learning algorithms. Defining Neural Networks A neural network is modeled after the human brain and has artificial neurons or nodes. These nodes are arranged in three layers: The input layer  The hidden layer(s) The output layer  The input layer feeds each node with data as inputs. The node then applies random weights, a bias, and a nonlinear function, also called an activation function, to the inputs. The activation function decides which neuron to activate. How Deep Learning Works!   Deep learning is a type of machin

Tokenization Basics

 Tokenization is a fundamental step in Natural Language Processing (NLP) that involves breaking down text into smaller parts called tokens. These tokens are then used as input for a language model. Tokenization is a crucial step in NLP because it helps machines understand human language by breaking it down into bite-sized pieces, which are easier to analyze. In this blog post, we will explore the concept of tokenization in detail, including its types, use cases, and implementation. What is Tokenization? Tokenization is the process of converting a sequence of text into smaller parts, known as tokens. These tokens can be as small as characters or as long as words. The primary reason this process matters is that it helps machines understand human language by breaking it down into bite-sized pieces, which are easier to analyze. Tokenization is akin to dissecting a sentence to understand its anatomy. Just as doctors study individual cells to understand an organ, NLP practitioners use tokeni

Building a Blog Application with Django

In this tutorial, we will build a Blog application with Django that allows users to create, edit, and delete posts. The homepage will list all blog posts, and there will be a dedicated detail page for each individual post. Django is capable of making more advanced stuff, but making a blog is an excellent first step to get a good grasp over the framework.  Pre-Requirements Django is an open-source web framework, written in Python, that follows the model-view-template architectural pattern. So Python is needed to be installed on your machine. Unfortunately, there was a significant update to Python several years ago that created a big split between Python versions, namely Python 2 the legacy version and Python 3 the version in active development. Since Python 3 is the current version in active development and addressed as the future of Python, Django rolled out a significant update, and now all the releases after Django 2.0 are only compatible with Python 3.x. Therefore this tutorial is s

On The Use of Large Language Models in Medicine

Large language models (LLMs) are artificial intelligence systems that can generate natural language texts based on a given input, such as a prompt, a keyword, or a query. LLMs have been applied to various domains, including medicine, where they can potentially assist doctors, researchers, and patients with tasks such as diagnosis, treatment, literature review, and health education. However, the use of LLMs in medicine also poses significant challenges and risks , such as: Data quality and privacy: LLMs are trained on massive amounts of text data, which may not be reliable, representative, or relevant for the medical domain. Moreover, the data may contain sensitive personal information that needs to be protected from unauthorized access or disclosure. Ethical and legal implications: LLMs may generate texts that are inaccurate, misleading, biased, or harmful for the users or the recipients. For example, an LLM may suggest a wrong diagnosis or a harmful treatment, or it may violate the p

What is RAG (Retrieval Augmented Generation)

  Retrieval Augmented Generation (RAG) is a technique that combines an information retrieval component with a text generator model. RAG can be fine-tuned and its internal knowledge can be modified in an efficient manner and without needing retraining of the entire model.  RAG takes an input and retrieves a set of relevant/supporting documents given a source (e.g., Wikipedia). The documents are concatenated as context with the original input prompt and fed to the text generator which produces the final output. RAG is used to improve the quality of generative AI by allowing large language model (LLMs) to access external knowledge to supplement their internal representation of information. RAG provides timeliness, context, and accuracy grounded in evidence to generative AI, going beyond what the LLM itself can provide. RAG has two phases: retrieval and content generation. In the retrieval phase, algorithms search for and retrieve snippets of information relevant to the user’s prompt or q

Vector databases

  Vector databases are becoming increasingly popular for building AI-powered applications, including LLM apps. In this tutorial, we will cover the basics of vector databases, how they are used, their benefits, and their implementation in Python for LLM. What is a Vector Database? A vector database is a type of database that stores data as numeric vectors in a coordinate space. This allows similarities between vectors to be calculated via operations like cosine similarity. By encoding data as vectors, developers can leverage the mathematical properties of vector spaces to achieve fast similarity search across very large datasets How are Vector Databases Used? Vector databases are used to enable fast similarity search and scale across data points. For LLM apps, vector indexes can simplify architecture over full-text search. Developers can build AI-powered applications in Python on vector databases by encoding data as vectors and using them to search for similar data points. Benefits of

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