Master Deep Learning & Build Intelligent Neural Networks
Learn how neural networks power modern Artificial Intelligence. Explore Deep Learning architectures, TensorFlow, PyTorch, Computer Vision, Natural Language Processing and model training through practical projects.
Understand neurons, layers, activation functions and how neural networks learn.
Explore widely used frameworks for building and training deep models.
Learn how Deep Learning processes images, text and sequential data.
Practice training, evaluating and improving Deep Learning models.
Understand the Technology Behind Modern AI.
Deep Learning is a specialized
area of Machine Learning
that uses multi-layered
neural networks to identify
complex patterns in data.
This course explores
neural network fundamentals,
backpropagation, optimization,
Convolutional Neural Networks,
recurrent architectures,
transformers and
practical model development.
Learn to build and evaluate
Deep Learning models using
Python, TensorFlow and PyTorch,
with applications in image
classification, text analysis
and predictive systems.
Deep Learning Skills You'll Develop
Explore neural network architectures, optimization techniques and real-world AI applications.
Artificial Neural Networks
Understand neural network layers, weights, biases, activation functions and feedforward computation.
Backpropagation & Optimization
Learn loss functions, gradients, gradient descent and model training techniques.
Convolutional Neural Networks
Build CNN-based models for image classification, feature extraction and introductory Computer Vision.
RNN & LSTM Networks
Explore recurrent neural networks, sequence processing and long short-term memory architectures.
Transformers & NLP
Understand attention, embeddings and transformer-based language models.
Deep Learning Deployment
Learn model saving, inference workflows, performance evaluation and deployment fundamentals.
Deep Learning Tools You'll Explore
Practice with popular Python frameworks and technologies used in Deep Learning.
Deep Learning Course Syllabus
A structured learning roadmap from neural network fundamentals to CNNs, sequence models, transformers and practical projects.
Module 01 — Introduction to Deep Learning
- What is Deep Learning?
- AI vs Machine Learning vs Deep Learning
- Applications of Deep Learning
- Neural network fundamentals
- Types of neural network architectures
- Understanding datasets and labels
- Deep Learning development workflow
Module 02 — Python & Mathematical Foundations
- Python review for Deep Learning
- NumPy arrays and tensor operations
- Linear algebra fundamentals
- Vectors and matrices
- Derivatives and gradients
- Probability and statistics basics
- Data preprocessing
- Train, validation and test datasets
Module 03 — Artificial Neural Networks
- Perceptron and artificial neurons
- Weights and biases
- Input, hidden and output layers
- Activation functions
- Forward propagation
- Loss functions
- Backpropagation fundamentals
- Building a feedforward neural network
Module 04 — Training & Optimizing Neural Networks
- Gradient descent
- Batch and mini-batch training
- Learning rates
- SGD and Adam optimizers
- Overfitting and underfitting
- Dropout and regularization
- Batch normalization
- Early stopping
- Model evaluation
Module 05 — TensorFlow, Keras & PyTorch
- Introduction to TensorFlow
- Working with Keras
- Building sequential models
- Compiling and training models
- Introduction to PyTorch
- Tensors and automatic differentiation
- Defining neural networks in PyTorch
- Training and evaluation loops
Module 06 — Convolutional Neural Networks
- Introduction to Computer Vision
- Image data representation
- Convolution operations
- Filters and feature maps
- Pooling layers
- CNN architecture
- Image augmentation
- Transfer learning fundamentals
- Image classification project
Module 07 — Recurrent Neural Networks & LSTM
- Introduction to sequential data
- Recurrent Neural Networks
- Sequence modeling
- Vanishing gradient problem
- Long Short-Term Memory networks
- GRU fundamentals
- Time-series prediction concepts
- Sequence classification practice
Module 08 — Transformers & Natural Language Processing
- Text preprocessing
- Tokenization
- Word embeddings
- Attention mechanism
- Transformer architecture overview
- Encoder and decoder concepts
- Introduction to pretrained language models
- Hugging Face Transformers
- Text classification exercise
Module 09 — Advanced Topics & Deployment
- Transfer learning
- Fine-tuning pretrained models
- Introduction to autoencoders
- Generative model concepts
- GPU acceleration overview
- Saving and loading trained models
- Inference pipelines
- Model deployment fundamentals
- Bias, privacy and responsible AI
Module 10 — Deep Learning Capstone Projects
- Choosing a project problem
- Dataset collection and preparation
- Model architecture selection
- Training and validation
- Hyperparameter experimentation
- Performance visualization
- Model testing and limitations
- Preparing a demonstration
- GitHub project documentation
- Final project presentation
Build Real-World Deep Learning Projects
Apply neural networks to practical image, text and sequential data problems.
Image Classification with CNN
Build a Convolutional Neural Network to classify images. Practice preprocessing, model training, validation and evaluation.
Sentiment Analysis with Deep Learning
Develop a neural network model to classify text sentiment using embeddings and sequence-processing techniques.
Time-Series Forecasting Model
Explore an LSTM-based model for forecasting sequential data and compare its predictions against baseline methods.
Develop Skills for Deep Learning Careers.
Deep Learning powers applications
in image recognition,
natural language processing,
speech technology,
recommendation systems
and advanced AI products.
Skills in neural networks,
Python, TensorFlow,
PyTorch and model training
can support specialized
AI and Machine Learning
career paths.
Professional Deep Learning
roles generally require
strong mathematical foundations,
programming expertise
and substantial project experience.
Questions About Deep Learning
Can beginners learn Deep Learning?
What is the difference between Machine Learning and Deep Learning?
Does the course include TensorFlow and PyTorch?
Will I learn CNN, RNN and LSTM?
Is NLP included in this course?
Are practical Deep Learning projects included?
Ready to Build Your First Neural Network?
Explore Deep Learning training with Placify India and develop practical skills in neural networks, TensorFlow, PyTorch, Computer Vision and NLP.