TensorFlow is an open-source software library from Google Brain Team that offers strong algorithms for both machine learning and deep learning. One of the most popular ML development platforms has been available since 2015. Working with ML increases the ease of using Python's numerical computation and data flow. Two words were combined to form TensorFlow's name; 'tensor,' denoting multidimensional arrays, and 'flow,' representing data flow.
This platform allows users to define how data flows between operations on tensors. Its ease of use, flexibility across various CPUs or GPUs, mobile operating systems and user-friendly syntax. TensorFlow can support complex neural networks (i.e., convolutional neural networks and recurrent neural networks) and predict image recognition, video detection and natural language processing without complications.
TensorFlow gives an introduction to a range of tools and resources for developing, deploying, and refining machine learning models. Its comprehensive operations library, and flexible architecture make it an ideal choice for developing modern AI applications. Common use cases include deep neural network training, deep learning research, natural language processing (NLP), computer vision, reinforcement learning and machine learning.
TensorFlow allows developers to build various types of neural networks like convolutional neural networks, recurrent neural networks, and GANs for tasks such as image recognition, NLP tasks such as text classification or sentiment analysis, and robotics or autonomous systems based on reinforcement learning. In order to unlock the full potential of data-driven technologies, it offers a number of capabilities that make it invaluable.
Simplilearn's TensorFlow courses are designed to give students an in-depth knowledge of the framework and its core concepts. As the program progresses, trainees will also learn how to craft models for complex neural networks such as convolutional neural networks and recurrent neural networks, machine learning and deep learning to gain familiarity with the working text, images and time series data. Participants should be well-prepared to learn Tensorflow and use their new skills in real-world applications after completing this program. They can develop solutions for tasks like image classification, object detection and natural language processing (NLP). At the end of these online courses, participants will also learn the ability of how to effectively utilize TensorFlow for developing deep learning, neural network and machine learning applications.
TensorFlow courses don't have any prerequisites. However, it's recommended to have familiarity with mathematics, statistics and machine learning concepts. Understanding the Python and Juptyer Notebooks is strongly recommended before taking the course. It is also highly beneficial to have foundational knowledge and hands-on experience with machine learning and deep learning in order to engage with the courses more effectively.
For those interested in using TensorFlow, Python is the primary language used. With its simple API, developers can define, train and evaluate machine learning and artificial intelligence models with Python. Additionally, a large set of libraries complement Tensorflow - such as NumPy, Pandas and Matplotlib - allowing one to tackle data manipulation and visualization tasks easily. Altogether, these features make Python crucial for anyone working with TensorFlow.
Simplilearn offers a variety of TensorFlow courses, such as