Course Overview
In this course, you'll gain knowledge on how to design and construct a TensorFlow input data pipeline, develop machine learning models using Keras and TensorFlow, increase the accuracy of ML models, write ML models for scaled application, and create specialised ML models.
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Pre-requisites
Some knowledge with the fundamentals of machine learning a working knowledge of a scripting language; Python is preferable.
Audience Profile
- Data Analysts
- Data Engineers
- Data Scientists
- ML Engineers
- ML Software Engineers
Learning Objectives
- Create machine learning models using TensorFlow and Keras, and be familiar with their essential elements.
- Utilize the tf. data library to work with massive amounts of data.
- For the development of basic and complex models, use the Keras Sequential and Functional APIs.
- Vertex AI's training, deployment, and productional ML models allow for scale.
Content Outline
This module provides an outline of the course and its goals and objectives.
The TensorFlow framework is introduced in this module, along with a glimpse of its primary elements and overall API structure.
An essential part of a machine-learning model is data. It takes more than just obtaining accurate data. To guarantee that the model can absorb the greatest amount of signal possible, you must also make sure that the proper procedures are in place to clean, analyse, and change the data as necessary. The preparation of the data for training, working with in-memory files, and training on huge datasets with tf Data are all covered in this subject. After that, embeddings are covered. Finally, a summary of scaling data with tf. Keras preprocessing layers follows.
We go over activation functions in this lesson and how deep neural networks cannot capture data nonlinearities without them. With the help of the Keras Sequential and Functional APIs, we then give a general overview of Deep Neural Networks. We then go over model subclassing, which provides more freedom when designing models. A regularisation lesson concludes the module.
This module describes how to train TensorFlow models at scale using Vertex AI.
This module is a summary of the TensorFlow on Google Cloud course.
FAQs
A: Tensorflow has APIs support for many languages, such as Matlab and C++. Researchers are continuously trying to make it better. A javascript library, tensorflow.js, has also been introduced for training and deploying machine learning models.
A: To perform various tasks on a tensor board, different types of dashboards are present and i.e.
- Scalar Dashboard
- Histogram Dashboard
- Distributor Dashboard
- Image Dashboard
- Audio Dashboard
- Graph Explorer
- Projector
- Text Dashboard
A: The components of deploying the lite model file in Tensorflow are listed below:-
- Java API: Java API is a wrapper around C++ API on Android.
- C++ API: C++ API loads the TensorFlow Lite model and calls the interpreter.
Interpreter: The interpreter can be used to execute the model. It uses particular kernel loading, a unique feature of TensorFlow Lite.
A: Loading the data into TensorFlow is the initial step before training a machine learning algorithm. There are generally two ways to load the data:
- Load data in memory: It is the easiest method. All the data is loaded into memory as a single array. One can write Python code that is unrelated to TensorFlow.
Tensorflow data pipeline: TensorFlow has built-in APIs that help load the data, perform the operations, and easily feed the machine learning algorithm. This method is mainly used when there is a large dataset.
A: Radiant has highly intensive selection criteria for Technology Trainers & Consultants who deliver training programs. Our trainers & consultants undergo a rigorous technical and behavioral interview and assessment process before they are on board the company.
Our Technology experts/trainers & consultants carry deep-dive knowledge in the technical subject & are certified by the OEM.
Our training programs are practically oriented with 70% – 80% hands-on training technology tools. Our training program focuses on one-on-one interaction with each participant, the latest content in the curriculum, real-time projects, and case studies during the training program.
Our faculty will quickly provide you with the knowledge of each course from the fundamental level, and you are free to ask your doubts at any time from your respective faculty.
Our trainers have the patience and ability to explain complex concepts simplistically with depth and width of knowledge.
To ensure quality learning, we provide support sessions even after the training program.
A: To attend the training session, you should have operational Desktops or Laptops with the required specification and a good internet connection to access the labs.
A: We recommend you attend the live session to practice & clarify the doubts instantly and get more value from your investment. However, if, due to some contingency, you have to skip the class, Radiant Techlearning will help you with the recorded session of that particular day. However, those recorded sessions are not meant only for personal consumption and NOT for distribution or any commercial use.
A: Radiant Techlearning has a data center containing a Virtual Training environment for participants’ hand-on-practice.
Participants can easily access these labs over Cloud with the help of a remote desktop connection.
Radiant virtual labs allow you to learn from anywhere and in any time zone.
A: The learners will be enthralled as we engage them in the natural world and Oriented industry projects during the training program. These projects will improve your skills and knowledge and give you a better experience. These real-time projects will help you a lot in your future tasks and assignments.