AI or Artificial Intelligence is a buzzword across the world these days. Several industries are prospering with AI implementation, and many others are gearing up to adopt this latest technology to start a journey of steady progress. Accurate executions and quick operations with automated labor-intensive procedures are helping the companies to get their work done at low cost and in less time. Companies are using Artificial Intelligence to better understand their consumers and gauge their behavior and preferences by analyzing the available data. This allows them to optimize their offerings and prices accordingly.
We have covered each and every topic in detail and also learned to apply them to real-world problems. There are lots and lots of exercises for you to practice and also 2 bonus NLP Projects "Sentiment analyzer" and "Drugs Prescription using Reviews". In this Sentiment analyzer project, you will learn how to Extract and Scrap Data from Social Media Websites and Extract out Beneficial Information from these Data for Driving Huge Business Insights. In this Drugs Prescription using Reviews project, you will learn how to Deal with Data having Textual Features, you will also learn NLP Techniques to transform and Process the Data to find out Important Insights. You will make use of all the topics read in this course. You will also have access to all the resources used in this course. Enroll now and become a master in machine learning.
Companies can engage in different approaches to model development. From fully managed ML services, all the way to custom models. Depending on business requirements, available expertise, and planning constraints, they must make a choice: should they develop custom solutions from scratch? Or should they choose an off-the-shelf service? For all stages of ML workloads, a decision must be met concerning how the different puzzle pieces will fit together.
Back in the 1950s, the fathers of the field, Minsky and McCarthy, described artificial intelligence as any task performed by a machine that would have previously been considered to require human intelligence. That's obviously a fairly broad definition, which is why you will sometimes see arguments over whether something is truly AI or not. Modern definitions of what it means to create intelligence are more specific. Francois Chollet, an AI researcher at Google and creator of the machine-learning software library Keras, has said intelligence is tied to a system's ability to adapt and improvise in a new environment, to generalise its knowledge and apply it to unfamiliar scenarios. "Intelligence is the efficiency with which you acquire new skills at tasks you didn't previously prepare for," he said.
In a previous module, we examined language models and explored n-gram and neural approaches. We found that the n-gram approach is generally better for higher values of N but this may be constrained by available compute resources. There was also the concern about the lack of representation for n-grams not present in the training corpus. On the other hand, applying subword tokenization methods such as Byte Pair Encoding and Wordpiece, recent neural approaches are able to resolve the issues with n-gram language models and show impressive results. We also traced the development of neural language models from feedforward networks that rely on word embeddings and fixed input length to recurrent neural networks which allowed for variable length input but struggled to capture long term dependencies.
Entering the 22nd of 150 epochs after 10 hours of training, I realized the 3000 wav file dataset was a bit tough to swallow for my 5 year old MacBook Pro. The Free Spoken Digit Dataset contains recordings from 6 speakers and 50 of each digit per speaker in 8kHz .wav As I was following along the outstanding video series on Sound Generation With Neural Networks by Valerio Velardo, I found myself stuck in an endless training phase. The goal is to train a custom-made Variational Auto-Encoder to generate sound digits. The preprocessing of the FSDD wav files was performed locally and generated a training dataset of 3000 spectrograms in .npy
Do you understand how your machine learning model works? Despite the ever-increasing usage of machine learning (ML) and deep learning (DL) techniques, the majority of companies say they can't explain the decisions of their ML algorithms . This is, at least in part, due to the increasing complexity of both the data and models used. It's not easy to find a nice, stable aggregation over 100 decision trees in a random forest to say which features were most important or how the model came to the conclusion it did. This problem grows even more complex in application domains such as computer vision (CV) or natural language processing (NLP), where we no longer have the same high-level, understandable features to help us understand the model's failures.
In a recent post on BERT, we discussed BERT transformers and how they work on a basic level. The article covers BERT architecture, training data, and training tasks. However, we don't really understand something before we implement it ourselves. So in this post, we will implement a Question Answering Neural Network using BERT and a Hugging Face Library. In this task, we are given a question and a paragraph in which the answer lies to our BERT Architecture and the objective is to determine the start and end span for the answer in the paragraph.
AI systems are becoming increasingly popular and central in many industries. They decide who might get a loan from the bank, whether an individual should be convicted, and we may even entrust them with our lives when using systems such as autonomous vehicles in the near future. Thus, there is a growing need for mechanisms to harness and control these systems so that we may ensure that they behave as desired. One important issue that has been gaining popularity in the last few years is fairness. While usually ML models are evaluated based on metrics such as accuracy, the idea of fairness is that we must ensure that our models are unbiased with regard to attributes such as gender, race and other selected attributes.