72 Hours · 8 Modules · TensorFlow · Keras · PyTorch

Deep LearningTraining

Take your Python, NumPy and Pandas foundation into artificial neural networks. Build models with Keras and TensorFlow; apply CNN, RNN and LSTM architectures to image, sequential data and prediction problems.

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Python-Based
Model & Project Focused
MEB & International Certificate
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Start truly understanding the learning logic behind ready-made models.

Start the application process to join the hands-on program focused on neural networks, TensorFlow, CNN and LSTM.

  • In-person or online training
  • MEB-Approved and International Certificate

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Curriculum

A clear learning path that moves from fundamentals to practice.

The curriculum runs under eight main headings. Starting with a Python and data analysis review, the path combines the logic of neural networks with modern deep learning architectures.

MODULE 01Python, Pandas, NumPy & Matplotlib

Refresh your data and code foundation.

Python, Pandas, NumPy & Matplotlib
  • Python review
  • Pandas review
  • NumPy review
  • Matplotlib review
MODULE 02Artificial Neural Networks & Perceptron

Learn how neural networks fundamentally work.

Artificial Neural Networks & Perceptron
  • What is an artificial neural network?
  • Weights and bias
  • The perceptron approach
  • How it works
MODULE 03Activation & Backpropagation

Examine how learning takes place.

Activation & Backpropagation
  • Sigmoid
  • Softmax
  • ReLU
  • Tanh
  • Linear
  • Leaky ReLU
  • Backpropagation
MODULE 04Keras & TensorFlow

Set up the model training loop.

Keras & TensorFlow
  • Hyperparameters
  • Batch
  • Epoch
  • Learning rate
  • L1 / L2
  • Optimizers
MODULE 05CNN — Image Processing

Build models that learn patterns in visual data.

CNN — Image Processing
  • Image processing fundamentals
  • CNN model theory
  • Building the model
  • Training and testing
  • Project
MODULE 06RNN

Apply the sequential data modeling approach.

RNN
  • RNN model theory
  • Building the model
  • Training and testing
  • Project
MODULE 07LSTM

Work with networks that model long dependencies.

LSTM
  • LSTM model theory
  • Building the model
  • Training and testing
  • Time-dependent model applications
MODULE 08Model Interfaces

Take the model into an application that interacts with users.

Model Interfaces
  • Giving a deep learning model an interface
  • Presenting prediction outputs
  • Application and project approach
8-MODULE TECHNICAL PATH

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By the End of the Program

Turn knowledge into real business output.

Turn the concepts you learn into technical, practical skills you can use in real scenarios.

✓
Understand how neural networks work

Interpret the concepts of weight, bias, activation and backpropagation.

✓
Build models with TensorFlow and Keras

Manage the model architecture and training parameters.

✓
Build image models with CNN

Model image recognition and interpretation scenarios.

✓
Use RNN and LSTM

Choose the right architecture for sequential and time-dependent data problems.

✓
Take the model into an application

Present the output of the model you trained through a user interface.

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Go beyond using ready-made models. Build your own neural network.

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