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Humans do not start learning everything from the beginning; they basically relate the things to each other to make inferences about the new thing in their mind. For example, when they learn how to ride a motorcycle and they already know how to cycle, they don't need to learn about braking or any other basic things because they already know them. Traditional neural networks can't do this. They are networks with various loops to persist the information and LSTM(long short term memory) are a special kind of recurrent neural networks. Which are very useful when dealing with sequential data like time series data and NLP data.

Distributed training in TensorFlow is built around data parallelism, where we can replicate the same model architecture on multiple devices and run different slices of input data on them. Here the device is nothing but a unit of CPU + GPU or separate units of GPUs and TPUs. This method follows like; our entire data is divided into equal numbers of slices. These slices are decided based on available devices to train; following each slice, there is a model to train on that slice.

Data collected over a certain period of time is called Time-series data. These data points are usually collected at adjacent intervals and have some correlation with the target. There are certain datasets that contain columns with date, month or days that are important for making predictions like sales datasets, stock price prediction etc. But the problem here is how to use the time-series data and convert them into a format the machine can understand? Python made this process a lot simpler by introducing a package called Darts.

"Hey Siri", "okay Google" and "Alexa" is something we say almost every day to quickly get information without having to type in the search box. These devices are great to listen and understand your voice and give a suitable output. They are designed in a highly efficient speech recognition software that can understand multiple accents and a natural language processing algorithm to convert this speech into text. But before these smart devices find the information you asked for, they need to understand what you are saying. Let us implement a speech to text converter using Python and a google API. In this article, we will build a simple speech to text converter with Python and the google cloud API.

Automated Machine learning or autoML is used for automating the complete process of machine learning for real-world problems to make the process easier and more efficient. Over the years researchers have developed ways of automating processes by developing tools like AutoKeras, AutoSklearn and even no-coding platforms like WEKA and H2o. One such area of automation is in the field of natural language processing. With the development of AutoNLP, it is now super easy to build a model like sentiment analysis with very few basic lines of code and get a good output. With automation like these, it allows everyone to be a part of the machine learning community and does not restrict machine learning to only developers and engineers.

Our editors have compiled this directory of the best Python books based on Amazon user reviews, rating, and ability to add business value. There are loads of free resources available online (such as Solutions Review's Data Analytics Software Buyer's Guide, visual comparison matrix, and best practices section) and those are great, but sometimes it's best to do things the old fashioned way. There are few resources that can match the in-depth, comprehensive detail of one of the best Power BI books. The editors at Solutions Review have done much of the work for you, curating this comprehensive directory of the best Python books on Amazon. Titles have been selected based on the total number and quality of reader user reviews and ability to add business value. Each of the books listed in the first section of this compilation have met a minimum criteria of 15 reviews and a 4-star-or-better ranking. Below you will find a library of titles from recognized industry analysts, experienced practitioners, and subject matter experts spanning the depths of Python coding for beginners all the way to advanced data science best practices for Python users. This compilation includes publications for practitioners of all skill levels. "Python Crash Course is the world's best-selling guide to the Python programming language. In the first half of the book, you'll learn basic programming concepts, such as variables, lists, classes, and loops, and practice writing clean code with exercises for each topic. You'll also learn how to make your programs interactive and test your code safely before adding it to a project. In the second half, you'll put your new knowledge into practice with three substantial projects: a Space Invaders-inspired arcade game, a set of data visualizations with Python's handy libraries, and a simple web app you can deploy online."

The performance of a deep learning model is influenced by large datasets and diversity of the dataset. But, there might be situations where the dataset is simply not large enough or diverse enough. In such cases, data augmentation is used. Data augmentation is a technique that enables you to significantly increase the diversity of data available for training models, without actually collecting new data. Although deep learning models come with inbuilt methods to augment the data, these can be inefficient or lacking some required functionality.

Generative Adversarial Networks (GANs) are a trend nowadays in various unsupervised learning applications. They are applied in animation and gaming with a full swing due to their capability to produce new images when trained on a set of similar but different images. This model is basically a deep generative model composed of two networks – a generator and a discriminator. The Deep Convolutional Neural Network is one of the variants of GAN where convolutional layers are added to the generator and discriminator networks. In this article, we will train the Deep Convolutional Generative Adversarial Network on Fashion MNIST training images in order to generate a new set of fashion apparel images.

OpenAI Gym is a well known RL community for developing and comparing Reinforcement Learning agents. OpenAI Gym doesn't make assumptions about the structure of the agent and works out well with any numerical computation library such as TensorFlow, PyTorch. The gym also provides various types of environments. In this hands-on guide, we will develop a tic-tac-toe environment from scratch using OpenAI Gym. To start with, let's create the desired folder structure with all the required files.