Deep Learning with Tensorflow Training in Almaty

Deep Learning with Tensorflow Training in Almaty, Kazakhstan

Gain hands-on experience in building deep learning models using TensorFlowâ€™s flexible framework.

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Deep Learning with Tensorflow Training is a professional training program delivered by ProgNXT in Almaty, Kazakhstan. ProgNXT, a globally recognized corporate training provider in 55+ countries, offers this Deep Learning with Tensorflow Training course with hands-on, instructor-led sessions. The Deep Learning with TensorFlow training equips participants with the skills to build, train, and deploy advanced deep learning models using...

Expert Panel

Designed by the ProgNXT AI & Data Science Expert Panel, specializing in Generative AI, Machine Learning, and ChatGPT applications

ProgNXT AI & Data Science Expert Panel

Course Code: PRQ30
21 Hrs
Last Updated: April 2026

Course Rating5/5

The Deep Learning with TensorFlow training equips participants with the skills to build, train, and deploy advanced deep learning models using TensorFlow. This course covers foundational concepts such as tensor operations, neural network architectures, and model optimization, along with hands-on practice in implementing deep learning solutions for tasks like image recognition, natural language processing, and time-series analysis. Participants will gain a deep understanding of TensorFlow's ecosystem and tools, enabling them to tackle real-world AI challenges effectively.

Welcome to the official Deep Learning with Tensorflow Training certification program in Almaty, Kazakhstan. This comprehensive training is designed to elevate your professional skills and provide you with practical, industry-relevant knowledge locally. As a globally recognized corporate training provider operating in 55+ countries, ProgNXT ensures that our curriculum meets the highest standards of excellence.

Whether you are looking to upskill your team or advance your personal career, our expert-led sessions will guide you through the core concepts of this domain. Upon successful completion of the 21 Hrs program, participants will receive a globally accepted certification, demonstrating their proficiency and readiness to tackle complex challenges in the field.

Basic understanding of Python programming
Familiarity with linear algebra and calculus fundamentals
Awareness of machine learning concepts (helpful but not mandatory)

Upon completing the Deep Learning with TensorFlow for Beginners course, participants will:

Gain a solid foundation in deep learning principles and neural network architectures.
Learn to build, train, and evaluate neural networks using TensorFlow.
Understand the role of activation functions, optimizers, and loss functions in deep learning models.
Develop the skills to work with common deep learning tasks like image classification and text processing.

Deep Learning Foundations: Understand key principles of neural networks and deep learning.
TensorFlow Basics: Proficiency in using TensorFlow for building deep learning models.
Model Training and Optimization: Skills to train and fine-tune neural networks for better performance.
Image and Text Processing: Ability to handle image data and natural language processing tasks.
Practical Applications: Experience implementing real-world use cases of deep learning.

Participants will be assessed on:

TensorFlow Basics: Understanding the TensorFlow library and its components.
Neural Network Construction: Ability to design and implement neural network models.
Model Evaluation: Proficiency in evaluating deep learning models using metrics like accuracy and loss.

Data Preparation: Skills to preprocess and feed data into deep learning models.
Hands-On Problem Solving: Capability to solve beginner-level problems in deep learning.

Certificate of Completion: Participants who attend all sessions and successfully complete the course assessments will receive a ProgNXT Certificate of Completion for the Deep Learning with TensorFlow for Beginners Training Program.

Foundations of Deep Learning and TensorFlow Basics

Introduction to Deep Learning

Overview of deep learning concepts
Understanding neural networks and their components (layers, neurons, weights, and biases)
Key applications of deep learning across industries

Getting Started with TensorFlow

Introduction to the TensorFlow ecosystem
Installing TensorFlow and setting up the environment
Basic operations and TensorFlow computational graph
Practical exercise

Data Preparation for Deep Learning

Loading and preprocessing data with TensorFlow Datasets
Scaling, normalization, and reshaping data for neural networks
Practical exercise

Building and Training Neural Networks

Constructing Neural Networks with TensorFlow

Introduction to Keras API in TensorFlow
Building sequential and functional models
Understanding activation functions and when to use them
Practical exercise

Building and Training Neural Networks

Training Deep Learning Models

Understanding the process of training: forward propagation, backpropagation, and gradient descent
Defining loss functions and optimizers
Techniques to prevent overfitting (e.g., dropout, regularization)
Practical exercise

Evaluating Model Performance

Metrics for evaluating model performance: accuracy, precision, recall, F1-score
Using validation sets and cross-validation to prevent overfitting
Confusion matrix and its interpretation
Practical exercise

Advanced Concepts and Real-World Applications

Convolutional Neural Networks (CNNs) for Image Classification

Understanding CNN architecture and its applications in image processing
Layers in CNN: Convolutional, pooling, and fully connected layers
Transfer learning with pre-trained models (e.g., VGG, ResNet)
Practical exercise

Recurrent Neural Networks (RNNs) for Sequential Data

Introduction to RNNs and their applications in time-series and text data
LSTM and GRU cells for long-term dependencies
Practical exercise

TensorFlow for Text Processing

Preprocessing text data for deep learning: tokenization, padding, and embedding layers
Word embeddings and how they improve NLP models
Practical exercise

Model Deployment and Optimization

Exporting and saving models using TensorFlow’s SavedModel format
Deploying models for inference (on cloud or local servers)
Model optimization techniques: quantization, pruning, and converting to TensorFlow Lite for mobile devices
Practical exercise

Course Recap and Q&A Session

Summary of key concepts covered during the course
Q&A session for addressing queries and challenges faced during practical exercises
Final thoughts on how to further explore and develop deep learning skills

Course slides and detailed reference materials
Access to sample code for hands-on exercises
Practical datasets for model training and evaluation

Participants will be evaluated through hands-on exercises, quizzes, and a final project.
ProgNXT Certificates of completion will be awarded to participants who successfully complete the course assessments.

Deep Learning with Tensorflow Training Events in almaty

Online Events

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More Upcoming Session Dates

October 2026

📍 Almaty, Kazakhstan

26th Oct 2026→28th Oct 2026

🌐 Online Instructor-Led

26th Oct 2026→28th Oct 2026

November 2026

📍 Almaty, Kazakhstan

4th Nov 2026→6th Nov 2026

🌐 Online Instructor-Led

5th Nov 2026→7th Nov 2026

📍 Almaty, Kazakhstan

20th Nov 2026→22nd Nov 2026

🌐 Online Instructor-Led

27th Nov 2026→29th Nov 2026

December 2026

📍 Almaty, Kazakhstan

1st Dec 2026→3rd Dec 2026

🌐 Online Instructor-Led

2nd Dec 2026→4th Dec 2026

📍 Almaty, Kazakhstan

21st Dec 2026→23rd Dec 2026

🌐 Online Instructor-Led

21st Dec 2026→23rd Dec 2026

Events for other courses in Almaty, Kazakhstan

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Deep Learning with Tensorflow Training in Almaty