Are you someone who's getting interested in computer vision or any state-of-the-art knowledge in deep learning? Did you know that Tensorflow is an open-source end-to-end platform that is being developed by the Google Brain team which was led by the Google senior fellow and AI researcher Jeff Dean built in November 2015. It can actually perform various tasks focused on training and inference of deep neural networks. This allows the developers to create better machine learning applications using the tools, libraries and community resources. In fact, it is one of the most known deep learning libraries globally which is Google's Tensorflow.

There is no single starting point or path you can follow to become a data scientist. You can start from anywhere -- from a science, engineering, commerce graduate, Ph. D degree and continue your journey with coding any kind of problem you see around, to attending online courses, participating in a Kaggle competition or doing a data science project under a mentor. Even there is no single starting point or path still there is set of common skills and passions that you must possess. Mathematics & reasoning comes first and along that you should have a passion for coding/programming and problem solving.

Saving your trained machine learning models is an important step in the machine learning workflow: it permits you to reuse them in the future. For instance, it's highly likely you'll have to compare models to determine the champion model to take into production -- saving the models when they are trained makes this process easier. The alternative would be to train the model each time it needs to be used, which can significantly affect productivity, especially if the model takes a long time to train. In this post, we will cover 5 different ways you can save your trained models. Pickle is one of the most popular ways to serialize objects in Python; You can use Pickle to serialize your trained machine learning model and save it to a file. At a later time or in another script, you can deserialize the file to access the trained model and use it to make predictions.

Using either framework will reduce the headache that is from-scratch development and will allow the engineer/developer to build faster, and create solutions which are more easily explained thanks to respective profilers and TensorBoard. Simply put: there are too many unofficial tutorials covering the same topics and the code quality varies too greatly; these tutorials are noise and can be ignored. TensorFlow, Keras, and PyTorch all offer awesome, free tutorials; and the docs are organized into a textbook like manner. Familiarizing with a study framework like SQ3R, KWT, or PQRST will aid in using the docs to learn about AI/ML while also learning to use the framework. If needing Python standard examples, the site RealPython seems to be reliable and consistent; however, a docs-first research method is best.

TensorBoard is used for many things, and each type of task uses a different service. We have different services for each type of task; To perform various tasks, TensorBoard provides an API to call…

To make the predictive models more robust and outperforming, we need to use those modules and processes that are lightweight and can work faster. Ktrain is a lightweight python wrapper that provides such features to an extent. It is a lightweight wrapper for the deep learning library TensorFlow Keras that helps in building, training, and deploying neural networks and other machine learning models. In this article, we are going to discuss the ktrain package in detail. We will go through its important features and pre-trained models available with it.

Generator: the generator generates new data instances that are "similar" to the training data, in our case celebA images. Generator takes random latent vector and outputs a "fake" image of the same size as our reshaped celebA image. Discriminator: the discriminator evaluate the authenticity of provided images; it classifies the images from the generator and the original image. Discriminator takes true of fake images and outputs the probability estimate ranging between 0 and 1. Here, D refers to the discriminator network, while G obviously refers to the generator.

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A good deep learning model has a carefully carved architecture. It needs enormous training data, effective hardware, skilled developers, and a vast amount of time to train and hyper-tune the model to achieve satisfactory performance. Therefore, building a deep learning model from scratch and training is practically impossible for every deep learning task. Here comes the power of Transfer Learning. Transfer Learning is the approach of making use of an already trained model for a related task.

Deep Learning is a subset of Machine learning. It was developed to have an architecture and functionality similar to that of a human brain. The human brain is composed of neural networks that connect billions of neurons. Similarly, a deep learning architecture comprises artificial neural networks that connect a number of mathematical units called neurons. Deep Learning is capable of modeling complex problems that, in some cases, exceed human performance!