Deep Learning

Deep Learning — Course #404 · 50% YoY Growth

Architect Neural

Intelligence 🧠

Deep Learning powers GPT-4, DALL·E, self-driving cars, medical imaging, and voice assistants. Master CNNs, RNNs, Transformers, GANs, and reinforcement learning with TensorFlow and PyTorch — the skills behind every AI breakthrough.

Neural Networks CNNs RNNs & LSTMs Transformers GANs TensorFlow PyTorch Transfer Learning Reinforcement Learning Model Deployment

Enroll Now Brochure

Deep Learning — CNN Architecture

import torch.nn as nn
class VisionNet(nn.Module):
  def __init__(self):
    super().__init__()
    self.conv = nn.Sequential(
      nn.Conv2d(3,64,kernel_size=3),
      nn.ReLU(),
      nn.MaxPool2d(2),
      nn.Dropout(0.25)
    )

30K+

Jobs

DL roles

50 hrs

Duration

Video content

Modules

Full syllabus

Cert

Included

Industry recognised

24/7

Support

Expert guidance

What You Master

The Complete Deep Learning Skill Set

From neural network fundamentals to cutting-edge Transformers, GANs, and production deployment.

🧠

Neural Networks to Transformers

Master the complete deep learning stack — from perceptrons and MLPs to CNNs for image processing, RNNs/LSTMs for sequences, and Transformer architectures that power GPT-4, BERT, and DALL·E. Build and train production models with TensorFlow 2.x and PyTorch using GPU acceleration.

MLP CNNs RNNs/LSTMs Transformers Attention Backprop Adam/SGD GPU Training

🎨

Generative AI & GANs

Generative Adversarial Networks, Variational Autoencoders, and Diffusion Models — create images, video, and audio with AI.

🎮

Reinforcement Learning

DQN, Policy Gradient, Actor-Critic methods. Build AI agents that learn from interaction — from games to robotics.

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Model Deployment & Edge AI

Deploy models with Flask/FastAPI, cloud (AWS, GCP, Azure), and edge devices with TFLite and ONNX for mobile/IoT.

TFLite ONNX FastAPI

Syllabus

Course Curriculum

10 advanced modules — from neural network basics to cutting-edge architectures and deployment.

Overview of AI, Machine Learning, and Deep Learning
Applications — Vision, NLP, Healthcare, Robotics
Basics of Neural Networks — neurons, weights, bias
Setting Up Environments — TensorFlow, PyTorch, CUDA

Perceptron and Multi-Layer Perceptron (MLP)
Activation Functions — ReLU, Sigmoid, Tanh, Softmax
Loss Functions — MSE, Cross-Entropy, Huber
Forward and Backpropagation Algorithm
Optimization — SGD, Adam, RMSprop, Learning Rate Scheduling

Vanishing and Exploding Gradients
Batch Normalization & Layer Normalization
Dropout and Regularization — L1, L2
Weight Initialization — Xavier, He, Glorot
Hyperparameter Tuning Strategies

Introduction to CNNs — Convolution, Pooling Layers
Popular Architectures — LeNet, AlexNet, VGG, ResNet, EfficientNet
Transfer Learning and Fine-Tuning with Pre-trained Models
Object Detection — YOLO, SSD, Faster R-CNN
Image Segmentation — U-Net, Mask R-CNN

RNN Basics & the Vanishing Gradient Problem
Long Short-Term Memory (LSTM) Networks
Gated Recurrent Units (GRUs)
Seq2Seq Models for Machine Translation
Time Series Forecasting with LSTMs

Self-Attention & Multi-Head Attention
Transformer Architecture — Encoder, Decoder
BERT — Pre-training and Fine-Tuning
GPT Architecture & Language Generation
Hugging Face Transformers Library

Introduction to Generative Models
Autoencoders (AE) & Variational Autoencoders (VAE)
Generative Adversarial Networks (GANs)
Conditional GANs & StyleGAN
Diffusion Models — DALL·E, Stable Diffusion

Markov Decision Processes & Bellman Equation
Q-Learning & Deep Q-Networks (DQN)
Policy Gradient Methods — REINFORCE, PPO
Actor-Critic Methods (A3C, A2C)
Applications — Games, Robotics, Autonomous Systems

Model Serialization — ONNX, TorchScript, TF SavedModel
API Deployment — Flask, FastAPI, Streamlit
Cloud Deployment — AWS, Google Cloud, Azure
Edge AI — TFLite, CoreML for Mobile & IoT
Model Monitoring & Retraining Pipelines

Self-Supervised & Contrastive Learning
Few-Shot and Zero-Shot Learning
Multimodal AI — Text + Images + Audio
Explainable AI (XAI) & Model Interpretability
AI Ethics and Bias in Deep Learning

Career Outcomes

Deep Learning Careers in 2026

Deep Learning engineers are among the highest-paid professionals in tech. Google, DeepMind, OpenAI, Meta AI, and Nvidia pay top dollar for specialists who can design, train, and deploy production-grade neural networks.

₹14–45L

India Avg LPA

$120K–$250K

USA Avg

30K+

Active Jobs

50% Growth

Year-on-Year

Deep Learning Engineer

Design and implement neural network architectures for production AI systems at scale

₹15–40 LPA · $125K–$200K USA

Computer Vision Engineer

Build image/video AI systems — YOLO, segmentation, face recognition for real-world products

₹16–42 LPA · $130K–$210K USA

NLP / LLM Engineer

Build NLP systems, fine-tune LLMs, and deploy transformer-based AI applications

₹18–50 LPA · $140K–$230K USA

AI Research Scientist

Research novel deep learning architectures, publish at top venues (NeurIPS, ICML, ICLR)

₹25–80 LPA · $160K–$350K USA

Architect Neural

Intelligence 🧠

The Complete Deep Learning Skill Set

Course Curriculum

Deep Learning Careers in 2026

Ready to Master Deep Learning?