Deep Learning with TensorFlow & Keras
By the end of Level 3, participants will understand neural networks, train Deep Learning models using TensorFlow/Keras, evaluate model performance, and build introductory Computer Vision and NLP applications.
What you'll master in Level 2
By the end of Level 3, participants will understand neural networks, train Deep Learning models using TensorFlow/Keras, evaluate model performance, and build introductory Computer Vision and NLP applications.
Training Objectives & Core Deliverables
Understand Deep Learning concepts and neural networks.
Build and train Artificial Neural Networks (ANNs).
Work with TensorFlow and Keras.
Build image classification models using CNNs.
Understand sequence models and introductory NLP.
Apply Deep Learning best practices and transfer learning.
Day-by-Day Training Schedule & Live Labs
Deep Learning Foundations
Build a simple neural network for tabular data.
TensorFlow & Keras Essentials
Customer churn prediction using TensorFlow/Keras.
Computer Vision with CNN
Build an image classifier using the Fashion MNIST dataset.
Deep Learning for Text (NLP Basics)
Sentiment analysis using a text dataset.
Transfer Learning & End-to-End Project
Build an end-to-end image classification project using a pretrained CNN.
Detailed Module-wise Curriculum
Tensors and operations in TensorFlow
TensorFlow ecosystem overview
Keras Sequential and Functional APIs
Building neural networks layer by layer
Training, validation, and loss tracking
Early stopping and model checkpoint callbacks
Saving and loading models for inference
Image representation in digital tensors
Convolution layer, filters, and feature maps
Pooling layers (Max, Average)
Flatten and Dense classification layers
CNN architecture design & tuning
Loading image datasets & normalization
Data augmentation (conceptual)
NLP Foundations & text cleaning pipelines
Stop words, Stemming, and Lemmatization overview
Tokenization and vocabulary indexing
Word embeddings (Dense vector representations)
Sequence Models: RNN and LSTM architecture
Text classification workflow
Why pretrained models are standard practice
MobileNet, ResNet, and EfficientNet architectures
Fine-tuning pretrained models with frozen backbones
Model evaluation, confusion matrix, and inference
Exporting trained models for production
Certificate of Technical Competency — Deep Learning with TensorFlow & Keras
Complete all live hands-on labs to earn this verifiable competency badge. This credential unlocks progression to Generative AI, Large Language Models & Prompt Engineering.
Enroll in Deep Learning with TensorFlow & Keras
Reserve your seat for upcoming live batches, syllabus consultation & fee structure.Frequently Asked Questions
Frequently Asked Questions: Generative AI Training Plan
Find clear answers to common questions about our live interactive classes, cohort schedules, project mentoring, and career assistance.
You can choose to enroll in the complete 4-track master program (recommended for end-to-end expertise) or consult our academic team to join specific tracks matching your current skills.
Track 1: Scikit-learn, MLOps, ML Pipelines. Track 2: TensorFlow, Keras, CNN, NLP. Track 3: LangChain, Chroma/FAISS, RAG, Fine-Tuning. Track 4: LangGraph, MCP, ReAct, Multi-Agent Systems.
Yes, all classes are live, interactive online sessions with senior practitioners. Session recordings, code notebooks, datasets, and reading references are shared after each class.
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