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Tensorflows
With Techpratham, you can learn how to use TensorFlow to build, train, and deploy deep learning models for real-world use. Use Google's open-source framework to learn about AI and neural networks by doing.
5/5(4,890 Reviews)
Level
Advanced
Duration
8 weeks
What is Tensorflows?
Techpratham's TensorFlow training gives you a full understanding of deep learning and machine learning with TensorFlow. Students will learn about neural networks, training models, transfer learning, and putting them on cloud and edge devices. Computer vision, natural language processing, and recommendation systems are some of the real-world uses that the course covers. By the end, you'll know how to use TensorFlow to make and deploy AI solutions that can grow.
Training Plan
About trainer
Working professional who is carrying more then 10 years of industry experience.
Decks
The candidate will get access to the presentation taken-up during the session.
Notes
Consolidated notes in word document.
Assignments
Assignments for every module will be covered.
MCQs
MCQs for every module covered in the session would be provided.
Video Recording
Daily Session would be recorded and shared to the candidate.
Dumps for Certification
We will provide dumps for the certification exam, which will help you to prepare for the exam.
Updated Content
We provide Generative AI Driven content By experts.
Projects
3 Live Projects will be provided for practice.
Tensorflows Course Curriculum
.
Introduction to TensorFlow
Section 1
Learn the basics of TensorFlow and its ecosystem.
TensorFlow Overview
Installation & Setup
TensorFlow Ecosystem
Tensors & Operations
Eager Execution
.
TensorFlow Environment Setup
Section 2
Set up TensorFlow on local and cloud environments, including installation, configurations, and IDE integration.
Installation Methods
GPU/TPU Setup
Anaconda & Jupyter
Cloud Setup
.
TensorFlow Core Concepts
Section 3
Understand TensorFlow’s architecture, computation graphs, tensors, and operations essential for model building.
Tensors & Operations
Computation Graphs
Sessions & Variables
Eager Execution
.
Data Handling with TensorFlow
Section 4
Learn how to preprocess, load, and manage datasets efficiently using TensorFlow Data APIs.
tf.data API
Data Preprocessing
Augmentation
Pipelines
.
Building Neural Networks with Keras
Section 5
Use TensorFlow’s high-level API (Keras) to build, train, and evaluate deep learning models easily.
Sequential API
Functional API
Layers & Activation
Model Training
.
Convolutional Neural Networks (CNNs)
Section 6
Develop image classification and recognition models using TensorFlow and Keras CNN architectures.
Conv Layers
Pooling Layers
Image Augmentation
Transfer Learning
.
Recurrent Neural Networks (RNNs) & NLP
Section 7
Work on sequence modeling and natural language processing tasks using RNNs, LSTMs, and GRUs.
RNN Basics
LSTM & GRU
Text Preprocessing
Embeddings
.
TensorFlow for Computer Vision
Section 8
Apply TensorFlow for computer vision applications like object detection, segmentation, and image recognition.
Image Classification
Object Detection
Image Segmentation
Pretrained Models
.
TensorFlow for Natural Language Processing
Section 9
Build NLP applications with TensorFlow, leveraging embeddings, transformers, and advanced architectures.
Word2Vec / GloVe
Seq2Seq Models
Transformers
BERT Models
.
Model Optimization & Deployment
Section 10
Optimize TensorFlow models for performance and deploy them on cloud, mobile, and edge devices.
Model Quantization
TensorFlow Lite
TensorFlow Serving
TensorFlow.js
Key Projects
Tensorflows
Image Classification with CNNs
Train and build a convolutional neural network to sort images from datasets like MNIST or CIFAR-10. Add more data and test performance on data that hasn't been seen before. Use TensorFlow Serving to put the model into use.
Text Sentiment Analysis with RNNs/LSTMs
Make a deep learning model that can tell the difference between good and bad text reviews. Use LSTMs and embeddings to model sequences, and then make the model available as an API for real-time predictions.
Recommendation System using TensorFlow
Use TensorFlow to make a recommendation engine that uses collaborative filtering. Train the model on data about how users interact with items, and then put it on a web interface so that it can give personalized recommendations.
Tensorflows – Associate Training Program
Category
Associate
Exam Name:
Tensorflows – Associate
Exam Code:
N.A.
Exam Duration:
N.A.
Exam Format:
N.A.
Passing Score:
N.A.
Our Testimonials
Frequently Asked Questions (FAQs)
Is TensorFlow only used for deep learning?
No, TensorFlow can do both machine learning and deep learning. It works with both traditional machine learning algorithms and neural networks, so it can be used for a variety of AI tasks.
What is TensorFlow?
TensorFlow is an open-source machine learning and deep learning framework developed by Google. It provides tools for building, training, and deploying models efficiently. TechPratham offers guided training on TensorFlow with hands-on projects for real-world applications.
Why should I learn TensorFlow?
TensorFlow is widely used in AI for tasks like image recognition, NLP, and predictive analytics. Its popularity ensures career opportunities across industries. TechPratham helps learners gain practical expertise to apply TensorFlow in professional projects.
Is TensorFlow beginner-friendly?
Yes, TensorFlow provides high-level APIs like Keras that make it easy for beginners. It also offers advanced customization for professionals. TechPratham structures training for both beginners and experienced learners step by step.
How does TensorFlow compare with PyTorch?
TensorFlow is known for scalability and deployment, while PyTorch is favored for research flexibility. Both are powerful deep learning frameworks. TechPratham explains these differences with real use cases so learners can choose wisely.
What kind of models can I build with TensorFlow?
You can build models for computer vision, NLP, recommendation systems, and time-series forecasting. TensorFlow also supports reinforcement learning. TechPratham provides domain-based projects to master these applications.
Do I need strong math skills to use TensorFlow?
A good grasp of linear algebra, statistics, and calculus helps, but is not mandatory at the start. TensorFlow abstracts much complexity with pre-built functions. TechPratham ensures learners build both conceptual and practical confidence gradually.
Can TensorFlow be used with cloud platforms?
Yes, TensorFlow integrates with Google Cloud AI, AWS, and Azure for scalable ML solutions. This makes deployment easier for production systems. TechPratham provides cloud-integrated TensorFlow training with real-world scenarios.
What programming languages does TensorFlow support?
TensorFlow primarily supports Python, but also offers APIs for JavaScript, C++, and Swift. This flexibility makes it useful across platforms. TechPratham focuses mainly on Python but introduces multi-language integration in projects.
What are the career opportunities after learning TensorFlow?
Learning TensorFlow opens roles like AI Engineer, Data Scientist, Machine Learning Engineer, and Deep Learning Specialist. Demand is growing across multiple domains. TechPratham prepares learners with industry-based projects and interview readiness.
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