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🔬 Advanced AI Engineering 📍 Dilsukhnagar, Hyderabad ✅ Completion Terms Apply

Deep Learning Course

Go deep into the technology powering all modern AI — neural networks, CNNs, RNNs, LSTMs, GANs and Transformer architectures. Implemented in TensorFlow and PyTorch with real datasets and production-grade projects. Taught by an active MNC AI engineer who deploys deep learning systems professionally.

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DURATION

10 Weeks

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LEVEL

Intermediate to Advanced

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MODE

Online · Offline · Hybrid

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CERTIFICATE

Industry Certificate

Tools & Technologies You Will Learn

Python TensorFlow 2.x Keras PyTorch CUDA Hugging Face OpenCV NLTK W&B (Weights & Biases) MLflow Docker Google Colab Pro

Enroll on WhatsApp → View Curriculum

Deep Learning Course at LearnAI Tech Hub

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Free Demo Available
Contact us for current batch fees & EMI options

⏱ Duration: 10 Weeks
🎯 Level: Intermediate to Advanced
💻 Online · Offline · Hybrid
🏆 Completion certificate after meeting course requirements
📁 Guided project and portfolio preparation
🧭 Prerequisites explained before enrollment
📞 Current trainer and batch details available before payment

Ask About Enrollment Book Free Demo Class Call: +91 93900 23585

Ask for the current syllabus, trainer, schedule, fees and support terms before enrolling.

About This Course

Deep Learning is the engine of every serious AI system in production today

Every major AI breakthrough of the last decade — image recognition, language translation, speech synthesis, generative image creation, large language models — was built on deep learning. If you want to understand how ChatGPT, Claude, Stable Diffusion, AlphaFold and autonomous vehicles work at an engineering level, you need to understand deep neural networks. This is not optional knowledge for a serious AI career — it is foundational.

At LearnAI Tech Hub, this 10-week course takes you through every major deep learning architecture — feedforward networks, convolutional networks for vision, recurrent networks for sequences, generative models and the Transformer architecture that powers modern LLMs. You implement each architecture from scratch in both TensorFlow and PyTorch, build 6 real projects across computer vision, NLP and generative modelling, and finish with the architecture knowledge to contribute to any serious AI engineering team.

What makes this course different

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The deepest technical AI course at LearnAI Tech Hub

This is not a survey course. You implement neural networks from mathematical first principles, then build production-grade systems. The depth prepares you for senior AI engineering roles.

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Both TensorFlow and PyTorch — industry standard coverage

TensorFlow dominates in enterprise production. PyTorch dominates in research. This course gives you full proficiency in both — so you are ready for any team.

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Transformer architecture taught in full depth

The attention mechanism, multi-head attention, positional encoding, BERT, GPT architecture — explained clearly with code. Understanding Transformers is non-negotiable for any serious AI engineer in 2026.

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Projects on real GPU infrastructure

Deep learning requires GPU compute. This course includes access to cloud GPU environments so you train real models on real hardware — not just learn concepts on CPU.

Course Curriculum

8 modules · 10 Weeks · Guided practical work

The sequence below describes the current course plan. Ask for the dated batch syllabus because tools and module order may change.

Neural Networks from Mathematical Foundations

12 topics · Weeks 1–2

Biological vs artificial neurons — the intuition

Perceptrons, multi-layer networks and the universal approximation theorem

Activation functions: sigmoid, tanh, ReLU, GELU — why each exists

Forward propagation: matrix multiplication and computation graphs

Loss functions: MSE, cross-entropy, custom losses

Backpropagation derivation — the chain rule implemented step by step

Gradient descent variants: batch, mini-batch, stochastic

TensorFlow and PyTorch: building the same network in both frameworks

Project: Train a neural network from scratch — no library magic

Training Deep Networks — The Hard Part

10 topics · Week 3

Vanishing and exploding gradients — causes and solutions

Weight initialisation: Xavier, He, orthogonal initialisation

Batch normalisation: how it works and why it helps

Dropout: regularisation through random deactivation

Learning rate schedules: cosine annealing, warm restarts

Optimisers deep dive: Adam, AdamW, RAdam, LAMB

Early stopping, model checkpointing, best practices

Weights & Biases for experiment tracking and visualisation

Project: Diagnose and fix a broken training pipeline — real debugging scenario

Convolutional Neural Networks — Computer Vision

12 topics · Weeks 4–5

Convolutions: filters, padding, stride — implemented from scratch

Pooling layers, feature maps and spatial hierarchies

Classic CNN architectures: LeNet, AlexNet, VGGNet

ResNet: residual connections and why depth suddenly became possible

EfficientNet, MobileNet — efficient architectures for production

Object detection: YOLO v8, Faster R-CNN, SSD

Semantic segmentation: U-Net for biomedical and industrial applications

Transfer learning: fine-tuning pre-trained models on custom datasets

OpenCV for pre and post-processing computer vision pipelines

Project: Real-time object detection system with YOLO v8

Recurrent Neural Networks & Sequential Data

10 topics · Week 6

RNN architecture: hidden state, backpropagation through time

Vanishing gradient in RNNs — why standard RNNs fail on long sequences

LSTM: cell state, forget gate, input gate, output gate — from scratch

GRU: simplified gating mechanism and when to use it

Bidirectional RNNs for context from both directions

Sequence-to-sequence models and encoder-decoder architecture

Attention mechanism — the intuition before Transformers

Project: LSTM time series forecasting — predicting stock price movements

Transformer Architecture — The Foundation of Modern AI

12 topics · Week 7

Why Transformers replaced RNNs: parallelism and long-range dependencies

Self-attention: queries, keys, values — the mathematical intuition

Multi-head attention: attending to different representation subspaces

Positional encoding: giving Transformers sequence awareness

The full Transformer: encoder, decoder, encoder-decoder variants

BERT: masked language modelling and next sentence prediction

GPT: autoregressive language modelling and generation

Fine-tuning BERT and GPT on custom datasets with Hugging Face

Project: Custom BERT fine-tuned sentiment classifier for product reviews

Generative Models — GANs & VAEs

10 topics · Week 8

Generative vs discriminative models — the fundamental difference

Variational Autoencoders (VAEs): encoder, reparametrisation, decoder

GAN training dynamics: generator vs discriminator

DCGAN, Conditional GAN (cGAN), Pix2Pix — practical implementations

StyleGAN concepts — how modern image generation works

Diffusion models — the architecture behind Stable Diffusion

Evaluating generative models: FID, IS, perceptual quality

Project: Conditional image generation GAN on a custom dataset

Advanced Topics — Vision-Language & Multimodal AI

8 topics · Week 9

CLIP: connecting vision and language with contrastive learning

ViT (Vision Transformer): treating images as sequences of patches

DALL-E and Stable Diffusion — architecture overview

Whisper for speech recognition — architecture and fine-tuning

Multimodal models: combining text, image and audio

Efficient inference: quantisation, pruning, knowledge distillation

ONNX and TensorRT for production optimisation

Project: Vision-language model for image captioning

Production DL Systems & Career Preparation

10 topics · Week 10

Model serving: TorchServe, TF Serving, Triton Inference Server

Docker and Kubernetes for deep learning deployment

Model monitoring and drift detection in production

A/B testing ML models in production

Capstone: End-to-end deep learning application from training to API

Deep learning interview preparation: theory, coding and system design

HR placement referral and portfolio documentation review

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Career Relevance

Roles where these skills may be useful

Training does not guarantee a job, salary, promotion or internship. Outcomes depend on prior experience, project quality, assessment performance and employer requirements.

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Deep Learning Engineer

AI product companies, AI labs, autonomous systems, healthcare AI, computer vision companies

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Computer Vision Engineer

Manufacturing AI, autonomous vehicles, healthcare imaging, security surveillance tech

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NLP Engineer / LLM Engineer

EdTech, legal tech, healthcare AI, customer service automation, AI product companies

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Generative AI Engineer

AI creative tools, entertainment, gaming, AI art platforms, marketing tech companies

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AI Research Scientist

AI labs, university research, Fortune company R&D, defence and aerospace AI

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ML Platform Engineer

Cloud companies, ML infrastructure providers, large tech companies

Ask for current career-support terms

We can explain the included resume, portfolio and interview support before enrollment. Employer decisions remain independent.

Who Is This For

This course is designed for you if…

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Data Scientists and ML engineers wanting engineering depth

If you build ML models but want to understand and build deep learning systems — this is the exact course. It takes your existing skills to a fundamentally higher technical level.

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Software engineers targeting AI research or senior ML roles

Deep learning engineering proficiency is the fastest path from senior software engineer to AI engineer or ML engineer in research or product teams.

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AI course graduates wanting specialisation

After the AI, ML or Data Science course, this is the natural deep-dive that takes you from practitioner to engineer on the most technically demanding AI systems.

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M.Tech and PhD students in CS, AI, EEE or ECE

Deep learning is central to most current CS/EE research. This course gives you the implementation depth to work on cutting-edge research problems and publish results.

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Developers interested in generative models and creative AI

GANs, VAEs, Stable Diffusion architecture — understanding how generative models work technically opens up the most creative and innovative AI engineering roles.

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Engineers in computer vision-heavy industries

Manufacturing, healthcare imaging, autonomous vehicles, surveillance — if your industry uses cameras and AI, deep learning is the core skill that unlocks the highest-value roles.

Prerequisites

Intermediate Python programming Basic Machine Learning understanding (having built at least simple ML models) Mathematics: linear algebra basics, calculus intuition GPU laptop recommended (or cloud GPU access provided)

FAQs

Questions about Deep Learning Course?

Not finding your answer? Ask for the current syllabus, prerequisites, trainer, fees and batch schedule.

Ask on WhatsApp →

Do I need to take ML or Data Science first?

You need basic ML understanding — knowing what training, testing, overfitting and a neural network is. Our ML or Data Science courses are the ideal preparation. If you have taken an equivalent course elsewhere and can build simple ML models in Python, you are ready.

Is GPU required for this course?

GPU makes training dramatically faster, but it is not mandatory. The course provides access to cloud GPU environments (Google Colab Pro setups) for computationally intensive projects. A laptop with 8GB RAM and internet is the minimum requirement.

TensorFlow or PyTorch — which does industry use?

Both. TensorFlow (with Keras) dominates in enterprise production deployments. PyTorch dominates in AI research and is increasingly used in production. This course teaches both — you graduate proficient in both frameworks, which makes you employable across all team types.

How is Deep Learning different from Machine Learning?

ML includes all algorithms that learn from data — linear regression, decision trees, SVMs, neural networks. Deep Learning specifically refers to neural networks with many layers. Deep learning is the dominant approach for complex tasks: image recognition, language processing, speech and generative AI. Understanding DL means understanding the architecture behind all modern AI systems.

What is the Transformer architecture and why is it so important?

The Transformer (introduced in the 2017 paper "Attention Is All You Need") is the architecture that powers BERT, GPT, Claude, Gemini and virtually every modern large language model. It replaced RNNs for sequential tasks by using self-attention to process all positions in a sequence simultaneously. This course teaches it in complete mathematical and implementation depth.

Will I get placement support after the course?

Yes. Deep learning engineers are among the most consistently placed graduates from our programmes. Our HR team places DL course graduates as Deep Learning Engineers, Computer Vision Engineers, NLP Engineers and AI Research Engineers across India, USA, UAE and UK.

Deep Learning Course

Deep Learning is the engine of every serious AI system in production today

The deepest technical AI course at LearnAI Tech Hub

Both TensorFlow and PyTorch — industry standard coverage

Transformer architecture taught in full depth

Projects on real GPU infrastructure

8 modules · 10 Weeks · Guided practical work

Tools used during guided practice

Roles where these skills may be useful

This course is designed for you if…

Data Scientists and ML engineers wanting engineering depth

Software engineers targeting AI research or senior ML roles

AI course graduates wanting specialisation

M.Tech and PhD students in CS, AI, EEE or ECE

Developers interested in generative models and creative AI

Engineers in computer vision-heavy industries

Prerequisites

Questions about Deep Learning Course?

Review the Deep Learning Course before you enroll

Deep Learning Course

Deep Learning is the engine of every serious AI system in production today

The deepest technical AI course at LearnAI Tech Hub

Both TensorFlow and PyTorch — industry standard coverage

Transformer architecture taught in full depth

Projects on real GPU infrastructure

8 modules · 10 Weeks · Guided practical work

Tools used during guided practice

Roles where these skills may be useful

This course is designed for you if…

Data Scientists and ML engineers wanting engineering depth

Software engineers targeting AI research or senior ML roles

AI course graduates wanting specialisation

M.Tech and PhD students in CS, AI, EEE or ECE

Developers interested in generative models and creative AI

Engineers in computer vision-heavy industries

Prerequisites

Questions about Deep Learning Course?

Review the Deep Learning Course before you enroll

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