Memuat
Memuat
Deep Learning is a branch of artificial intelligence that uses layered neural networks to solve complex problems, from image recognition (image classification, object detection), natural language processing (NLP), to speech recognition and generative AI.
CID's Deep Learning Course is designed for data scientists, students, and researchers who want to build and train neural network models using TensorFlow/Keras and PyTorch, from mathematical foundations to model deployment.
The Deep Learning curriculum is designed to cover the entire workflow from basics to job-ready in industry.
Perceptron, activation, backpropagation, gradient descent, regularization, dropout.
Convolutional layer, pooling, image classification, object detection with YOLO/SSD.
Sequence modeling, time series, basic NLP, sentiment analysis with LSTM.
Fine-tuning pretrained model (ResNet, EfficientNet, BERT) for specific tasks.
Confusion matrix, ROC, cross-validation, hyperparameter tuning, and model interpretation.
TensorFlow Serving, ONNX, Flask API, or integration into web/mobile apps.
Our instructors are actively working on real projects using this software, not just theoretical teachers.
Every course level ends with a mini project that can directly be added to your portfolio.
Curriculum is updated to follow the development of software and current industry needs.
Class size is limited so that every question can be answered directly by the instructor.
We also accept Deep Learning project execution services for thesis, final projects, and professional needs.
Lihat Jasa Deep LearningIt is recommended to have basic Python and statistics. If not, we recommend starting with the Machine Learning or Python course first.
Both - TensorFlow/Keras for production and deployment, Py, for research and experiments.
For basic CNN image classification, 4–6 weeks. For complex architectures and deployment, 3–4 months.
Not mandatory, you can use Google Colab (free) or Kaggle for training in the cloud. But a local GPU is much faster.
Yes, online via Zoom.
Yes.
Advanced class covers fine-tuning LLM models and integration with Hugging Face transformers.
Very suitable. Experienced instructors guide the implementation of deep learning for research topics.
Consult your learning goals, we'll help you choose the most suitable class based on your level and needs.
Free Consultation