Deep Learning & Neural Networks Training in Vizag
Study how neural networks learn from data with Deep Learning training in Vizag at Softenant Technologies. This course for learners in Visakhapatnam connects model architectures with experiment design and evaluation beyond a single accuracy number. It covers data preparation, train/validation/test splits, loss functions, optimisation, regularisation, convolutional and sequence-model concepts, and practical experiment tracking.
How to build and evaluate deep learning models
Deep learning projects often fail because of data leakage, class imbalance, weak baselines, overfitting or a poor evaluation choice—not because a model has too few layers. Learners therefore develop an evidence-based workflow: define the prediction task, inspect data, establish a baseline, train carefully, compare experiments and explain limitations. The course connects neural-network theory to real image, text and tabular-data decisions.
Deep Learning and Neural Networks syllabus
Neural-network mechanics
Understand layers, weights, activations, forward passes, loss functions, backpropagation and gradient-based optimisation.
Data and experimental design
Prepare features and labels, split data correctly, prevent leakage, handle imbalance and choose evaluation metrics for the task.
Training stability and regularisation
Study learning rates, batch size, epochs, dropout, early stopping, normalisation and signs of overfitting or underfitting.
CNNs for visual data
Learn convolution, pooling, feature maps, data augmentation and image-classification workflow concepts.
RNN and sequence-model concepts
Explore sequence inputs, recurrent-state intuition, LSTM/GRU concepts and when modern transformer approaches are more suitable.
Evaluation and explainable projects
Use confusion matrices, precision/recall, learning curves, error analysis and experiment records to support a defensible conclusion.
Prerequisites and a practical learning sequence
Basic Python, arrays, functions and elementary statistics are useful preparation. Refresh vectors, matrices and the meaning of a gradient before studying backpropagation. You can review the introduction to neural networks to connect these ideas with layers, activations and learned weights.
Begin with a clearly defined prediction task and a simple baseline. Keep training, validation and test data separate; use the validation set to compare settings, then evaluate the selected approach on the held-out test set. Record preprocessing decisions so the experiment can be reproduced.
Use the Machine Learning course for broader modelling workflows. Choose Computer Vision with AI training when your next goal is a more focused visual-data learning path.
Tools, concepts and working methods
Python
Data and model-training workflow concepts.
Neural networks
Layers, activations, loss and optimisation.
CNN concepts
Visual feature extraction and classification.
RNN concepts
Sequence modelling and temporal data.
Portfolio projects with technical evidence
Image-classification experiment
Compare a baseline and CNN-style approach, document augmentation choices and analyse incorrect predictions.
Customer-churn neural baseline
Prepare tabular data, evaluate class imbalance and explain why a neural model may or may not be appropriate.
Sequence-prediction study
Create a time-ordered dataset workflow and discuss RNN/LSTM-style modelling considerations and limitations.
Career direction
Deep-learning trainee, machine-learning project intern, AI research support trainee and computer-vision or NLP foundation learner. Training improves technical preparation; employment outcomes depend on portfolio quality, interviews, experience and available roles.
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Read Our Google ReviewsDeep Learning and Neural Networks FAQs
Are CNN and RNN covered?
Yes. The course covers CNN concepts for images and RNN/LSTM/GRU concepts for sequential data, including where each architecture fits.
Is accuracy enough to evaluate a deep-learning model?
No. Metric choice depends on the task. Learners use confusion matrices, precision/recall, loss curves and error analysis where relevant.
What should I know before learning deep learning?
Basic Python, arrays, functions and elementary statistics are useful preparation. Familiarity with vectors, matrices and the idea of a derivative helps when studying weights, gradients and backpropagation. Review these foundations before attempting a larger neural-network project.
How is deep learning different from machine learning?
Deep learning uses neural networks with multiple layers to learn representations from data. Machine learning also includes methods such as decision trees and linear models. A simpler baseline remains useful when deciding whether a neural network is appropriate.
What projects can I build while studying this syllabus?
The listed project areas are image classification, a customer-churn neural baseline and sequence prediction. A useful portfolio includes the data-split method, baseline comparison, experiment settings, evaluation results and an explanation of errors.
How do learners recognise overfitting?
Compare training and validation loss across epochs. Improving training performance alongside worsening validation performance can indicate overfitting. The syllabus covers regularisation, dropout and early stopping, with test data reserved for final evaluation.
Discuss your learning goals
Contact Softenant Technologies to discuss the syllabus, your current skills and suitable project goals. Call +91 9393969628 or send a WhatsApp enquiry.
Visit: Flat No. 101, Geetha Mansion II, Junction, opposite Andhra Bank, Akkayyapalem, Visakhapatnam, Andhra Pradesh 530016, India.