Master Deep Learning with ENCODE-IT’s Comprehensive Course

Deep Learning is revolutionizing industries with its ability to solve complex problems, from image
and speech recognition to autonomous vehicles. If you want to unlock the power of neural
networks, ENCODE-IT’s Deep Learning course is the perfect starting point. This course is designed for
individuals interested in understanding and applying deep learning techniques to solve real-world
problems. With a focus on both theory and practical implementation, this course equips you with
the essential tools and skills needed to excel in the rapidly growing field of deep learning.

In this comprehensive course, you will learn about the building blocks of deep learning models,
explore popular architectures like Convolutional Neural Networks (CNNs) and Recurrent Neural
Networks (RNNs), and gain hands-on experience with frameworks such as TensorFlow, Keras, and
PyTorch. Whether you're a beginner or a professional, this course will enhance your understanding
of deep learning and help you apply it to various domains like computer vision, natural language
processing (NLP), and time series forecasting.

Course Overview
The Deep Learning course at ENCODE-IT covers all the essential concepts and techniques used in
deep learning, from the basic fundamentals to advanced deep learning architectures. You will learn
about artificial neural networks, backpropagation, optimization algorithms, and how to train and
deploy deep learning models. The course also includes practical applications and hands-on projects
that will allow you to solve real-world problems using deep learning.

By the end of the course, you will be proficient in building and deploying deep learning models, and
you will be equipped with the skills to pursue careers in data science, machine learning, and AI
research.

Salary Scale in India
Professionals with expertise in Deep Learning are in high demand across industries, including
technology, healthcare, finance, and entertainment. In India, the average salary for a Deep Learning
Engineer ranges from ₹8 lakh to ₹15 lakh per year for entry-level positions. With more experience,
professionals can earn between ₹15 lakh to ₹25 lakh, while senior roles and specialized positions
can offer salaries upwards of ₹30 lakh per year. With the increasing demand for AI and deep
learning solutions, skilled professionals in this field have excellent career prospects.

Placement Assistance & Certification in India
ENCODE-IT provides placement assistance to help you secure your dream job after completing the
course. We offer personalized resume-building services, interview coaching, and job placement
support to ensure you're ready for the competitive job market. Upon successful completion of the
course, you will receive an ENCODE-IT Certificate of Completion, which is a valuable addition to your
resume. Our certification, coupled with hands-on experience and real-world projects, will enhance
your employability in the fast-evolving field of deep learning.

Course Curriculum

1. Introduction to Deep Learning and Neural Networks
o What is Deep Learning? Overview and Applications
o Understanding Artificial Neural Networks (ANN)
o Perceptron Model and Activation Functions
o The Architecture of Deep Neural Networks
o Forward Propagation and Backpropagation
o Introduction to Gradient Descent and Optimization

2. Building Blocks of Deep Learning
o Deep Learning Frameworks: TensorFlow, Keras, and PyTorch
o Understanding Tensors and Matrix Operations
o Implementing Neural Networks in Keras and TensorFlow
o Building Your First Neural Network Model
o Model Evaluation: Accuracy, Precision, Recall, and Loss Functions

3. Training Deep Neural Networks
o Loss Functions: Cross-Entropy, Mean Squared Error
o Optimizers: Stochastic Gradient Descent, Adam, RMSProp
o Overfitting and Underfitting: Techniques to Avoid Overfitting
o Regularization Techniques: Dropout, L2 Regularization
o Learning Rate Scheduling and Early Stopping

4. Convolutional Neural Networks (CNNs)
o Introduction to CNNs and Their Applications
o Convolution and Pooling Layers
o Building CNN Architectures for Image Classification
o Transfer Learning with Pre-trained CNN Models (VGG, ResNet, etc.)
o Object Detection and Image Segmentation with CNNs
o Fine-Tuning and Hyperparameter Optimization for CNNs

5. Recurrent Neural Networks (RNNs) and LSTM
o Introduction to RNNs and Their Applications in Sequence Data
o Backpropagation Through Time (BPTT) and Vanishing Gradient Problem
o Long Short-Term Memory (LSTM) Networks and Gated Recurrent Units (GRU)

o Sequence Modeling for Text, Speech, and Time Series Forecasting
o Building and Training RNN, LSTM, and GRU Models
o Applications of RNNs in Natural Language Processing (NLP)

6. Generative Models: GANs and Autoencoders
o Introduction to Generative Adversarial Networks (GANs)
o Training GANs: Generator and Discriminator
o Applications of GANs in Image Generation, Style Transfer, etc.
o Autoencoders for Data Compression and Dimensionality Reduction
o Building Variational Autoencoders (VAEs) for Data Generation
o Applications of Autoencoders in Anomaly Detection

7. Natural Language Processing with Deep Learning
o Introduction to NLP and Deep Learning for Text Data
o Text Preprocessing: Tokenization, Lemmatization, Stopword Removal
o Word Embeddings: Word2Vec, GloVe, FastText
o Recurrent Neural Networks for Text Generation and Sentiment Analysis
o Attention Mechanisms and Transformers (BERT, GPT, etc.)
o Building a Text Classification Model with Deep Learning

8. Advanced Deep Learning Techniques
o Advanced Architectures: ResNet, Inception, EfficientNet
o Transfer Learning and Fine-Tuning Pretrained Models
o Model Deployment: Saving and Loading Models for Production
o Serving Models with Flask or TensorFlow Serving
o Distributed Training with Multiple GPUs and TPUs
o Hyperparameter Tuning with Grid Search and Random Search

9. Ethics in Deep Learning and AI
o Understanding Bias in Deep Learning Models
o Ethical Considerations in AI and Deep Learning
o Privacy and Security in AI Systems
o Responsible AI Development and Fairness in Models

o The Future of Deep Learning and AI Research

10. Real-World Deep Learning Projects

o Image Classification with CNNs: Build a Model to Classify Objects
o Sentiment Analysis of Tweets Using RNN/LSTM
o Time Series Forecasting for Stock Market Prediction
o Text Generation Using Recurrent Neural Networks
o Generating Artwork with GANs
o Building a Speech Recognition System with Deep Learning

11. Final Project and Certification Exam
o Final Project: Solving a Real-World Problem Using Deep Learning
o Model Evaluation, Optimization, and Deployment
o Final Exam: Comprehensive Evaluation of Deep Learning Skills
o Certification of Completion from ENCODE-IT and Job Placement Assistance

Key Features
 Tools & Platforms: TensorFlow, Keras, PyTorch, Jupyter Notebooks, CUDA, GPU, Colab
 Real-World Projects: Work on practical projects such as image classification, sentiment
analysis, time series forecasting, and GAN-based data generation.
 Certification & Placement Support: Get certified by ENCODE-IT and receive job placement
assistance to help you start or advance your career in AI and deep learning.
 Expert Instructors: Learn from instructors who are experienced in deep learning and AI, with
real-world industry expertise.
 Career Advancement: Learn cutting-edge techniques and frameworks used by top AI and
deep learning professionals.

ENCODE-IT’s Deep Learning course offers you the opportunity to explore the transformative field of
deep learning, from basic concepts to advanced applications. Whether you are looking to break into
the AI field or enhance your existing skills, this course will provide you with the tools and knowledge
to succeed. Join today and take your first step toward becoming a deep learning expert!