A TensorFlow training allows you to learn how to master Google's open-source framework, which is essential for Data Science and Deep Learning. Discover how and why to pursue such a course. Machine Learning has become more accessible in recent years, thanks to the emergence of frameworks that have greatly simplified the implementation of models.
Programmers can use TensorFlow to build Machine Learning applications with a wide variety of resources and tools. They can collect data, train models, make predictions, and improve future results.
Among the various Deep Learning platforms, TensorFlow is undoubtedly the most widely used. Originally, Google created this framework internally to harness its vast data and powerful computers.
The Google Brain Team developed TensorFlow and released it under the Apache Open Source License in 2015. It can be used with multiple CPUs and GPUs and various mobile operating systems.
The front-end API is based on the Python language, enabling the creation of applications with this framework. Applications run in C++.
By using TensorFlow, it’s possible to train and run neural networks for digit classification, image recognition, neural networks, sequence-to-sequence models for automatic translation, and natural language processing.
How does TensorFlow work?
TensorFlow operates based on Tensors, which are inputs represented as multidimensional arrays. This allows for the creation of dataflow structures and graphs to specify how data is connected through a graph. Users can create a flowchart of operations that can be performed on the inputs.
The three main components of TensorFlow’s structure are data preprocessing, model construction, and model training and estimation.
The training phase of the AI occurs on a computer and can make use of GPUs and CPUs. The rest of the process can be carried out on Windows, macOS, Linux, in the Cloud, or on mobile OSes like iOS and Android.
TensorBoard is another significant element of TensorFlow, enabling graphical and visual monitoring of the Tensor flow.
Since October 2019, TensorFlow 2.0 has brought numerous improvements based on user feedback. These enhancements have made it more efficient and user-friendly, particularly with the simplified Keras API for model training.
A new API also simplifies distributed training. Support for TensorFlow Lite allows models to be deployed on a wider variety of platforms. However, code written for previous versions of the framework may need to be rewritten to fully utilize these innovations.
What are the different components of TensorFlow?
A Tensor is a matrix or a vector that can represent any form of data. The dimensionality of the matrix or array determines the shape of the data.
Tensors can originate from input data or the result of a computation. All TensorFlow operations are performed within a graph, which is a sequence of calculations occurring in a specific order.
The graph depicts the operations and their relationships but doesn’t contain the actual values. The portability of graphs allows you to save computations for future use.
What are the advantages of TensorFlow?
TensorFlow is designed to be user-friendly, bringing together various APIs for creating large-scale Deep Learning architectures.
It can be described as a graph-based programming language, enabling developers to visualize the structure of their neural networks using TensorBoard.
The debugging tool is highly useful and appreciated. For all these reasons, TensorFlow is the most popular Deep Learning framework on GitHub.
What is TensorFlow used for? Who uses it?
The primary use case for TensorFlow is artificial intelligence, including applications in Machine Learning and Deep Learning. For example, Google uses it for its RankBrain system, which enhances the search results of its web search engine.
The company has also utilized this framework for applications such as automatic email generation, image classification, optical character recognition, and drug discovery in partnership with researchers from Stanford University.
Several globally recognized companies use TensorFlow, including Airbnb, Coca-Cola, eBay, Intel, Qualcomm, SAP, Twitter, Uber, and Snap Inc. Even in the sports industry, this framework is used to analyze the movements and performances of professional players. It is also harnessed by autonomous vehicle manufacturers.
Mastering TensorFlow can, therefore, open up numerous professional opportunities, as companies worldwide are actively seeking experts in this field.
What is a TensorFlow developer?
A TensorFlow developer is a specialist who can design and train neural networks using TensorFlow. They create and manage systems and applications.
To fulfill this role, it’s essential to obtain the TensorFlow developer certification. This certification is a crucial step for students, developers, and Data Scientists looking to demonstrate their Machine Learning skills through model construction and training using the TensorFlow framework.
What is Google developer certification?
A Google developer certification allows you to demonstrate your Machine Learning skills and your mastery of TensorFlow. To obtain it, you must pass a qualification test.
To succeed in this test, you need to possess several skills. Knowledge of Machine Learning and algorithms is essential, as well as a grasp of statistics, probability, matrices, or linear algebra.
- You should also be familiar with programming languages such as Python, R, C++, and Java. You must also have a good understanding of key neural network concepts and know how to train models from data.
In general, an experienced programmer can obtain their certification in about three weeks, while a complete beginner may need approximately six months of preparation.
How much does a TensorFlow expert earn?
Various professions require proficiency in TensorFlow, and generally, these are highly paid roles.
On average, a TensorFlow developer earns $148,508 in the United States. This salary range can vary from $94,000 to $204,000 per year and can easily increase with experience.
In France, a Data Scientist earns between €35,000 and €55,000 per year. Therefore, having expertise in TensorFlow can lead to a high salary in this field.
How do I take a TensorFlow training course?
If you want to learn how to master TensorFlow or prepare for certification, you can choose DataScientest. Our training programs enable you to become proficient in this Machine Learning framework.
Our Data Scientist program covers TensorFlow, Keras, and neural networks within the Deep Learning module. You will also be introduced to Python programming, Machine Learning, Data Visualization, and databases.
This training is available in an intensive BootCamp mode or as a Continuing Education program. It is entirely conducted online, with 85% individual coaching on our online platform and 15% MasterClass sessions.
Upon completion of the program, you will receive a certificate awarded by MINES ParisTech / PSL Executive Education. You will have all the tools you need to become a Data Scientist, with the opportunity to find employment immediately, just like 80% of our alumni. This training is eligible for funding through the Personal Training Account or the Bildungsgutschein.
If you are already a Data Scientist and want to further enhance your skills or focus on TensorFlow, you can choose our expert Deep Learning course. This program teaches you how to master Keras and TensorFlow, programming, and AI techniques like NLP and Computer Vision. This program can be completed in 15 weeks.
By following our programs, you can become an expert in Deep Learning or Data Science. Don’t wait any longer and discover DataScientest’s training programs.
You now have all the information about TensorFlow training. Explore our comprehensive guide on Deep Learning and our introduction to the Python programming language.