Applied Deep Learning with TensorFlow & PyTorch

Deep Learning (DL) represents the frontier of Artificial Intelligence, driving innovations in image recognition, natural language processing, autonomous systems, and predictive analytics. This 7-day intensive corporate training program equips professionals with advanced knowledge of neural networks and their implementation using TensorFlow and PyTorch—the two most powerful frameworks for DL development. Participants will learn how to design, train, and optimize deep neural networks for real-world business and enterprise applications. The training emphasizes both the theoretical understanding and hands-on experience required to deploy deep learning solutions in production environments.

Objectives of the Training

Understand the fundamental building blocks of deep learning and neural networks.
Gain proficiency in TensorFlow and PyTorch frameworks for model building.
Explore advanced architectures like CNNs, RNNs, LSTMs, and Transformers.
Learn to train, optimize, and deploy models on cloud platforms.
Apply DL concepts to real-world domains like computer vision, NLP, and time series forecasting.
Learn best practices for model interpretability, scalability, and performance tuning.

Prerequisites

Strong knowledge of Python programming.
Understanding of basic machine learning algorithms.
Familiarity with linear algebra, calculus, and probability concepts.
Prior exposure to data preprocessing and analytics tools (NumPy, Pandas, Matplotlib).

What You Will Learn

Design and train neural networks from scratch.
Implement CNNs for image processing and computer vision tasks.
Apply RNNs and LSTMs for sequential and time series data.
Work with advanced architectures such as GANs and Transformers.
Optimize training performance and fine-tune models for enterprise deployment.
Integrate DL workflows with cloud and edge environments.

Target Audience

This program is ideal for Data Scientists, AI Engineers, Machine Learning Practitioners, and Software Developers interested in mastering advanced deep learning techniques for enterprise-grade AI projects. Technical Managers seeking to lead AI-driven teams and understand DL systems at scale will also benefit.

Detailed 7-Day Curriculum

Day 1 – Introduction to Deep Learning & Frameworks (6 Hours)

Session 1: Deep Learning Foundations – From Machine Learning to Neural Networks.
Session 2: Understanding Tensors, Activation Functions, and Backpropagation.
Session 3: TensorFlow & PyTorch Overview – Setup, Syntax, and Workflow.
Hands-on: Building and Training a Simple Feedforward Neural Network.

Day 2 – Neural Network Architectures and Optimization (6 Hours)

Session 1: Deep Network Architectures – Layers, Parameters, and Loss Functions.
Session 2: Gradient Descent, Optimizers, and Learning Rate Scheduling.
Session 3: Regularization and Dropout Techniques for Preventing Overfitting.
Hands-on: Training Deep Networks and Analyzing Loss Curves.

Day 3 – Convolutional Neural Networks (CNNs) for Computer Vision (6 Hours)

Session 1: CNN Fundamentals – Convolutions, Filters, and Pooling.
Session 2: Building CNNs using TensorFlow and PyTorch.
Session 3: Transfer Learning and Fine-Tuning Pretrained Models (ResNet, VGG).
Case Study: Image Classification and Object Detection for Manufacturing.

Day 4 – Recurrent Neural Networks (RNNs) and Sequence Models (6 Hours)

Session 1: Understanding Sequential Data and RNN Mechanics.
Session 2: LSTM and GRU Architectures for Time Series and NLP.
Session 3: Sequence-to-Sequence Learning and Attention Mechanisms.
Case Study: Stock Price Prediction and Text Generation.

Day 5 – Advanced Deep Learning Architectures (6 Hours)

Session 1: Introduction to Generative Adversarial Networks (GANs).
Session 2: Autoencoders and Variational Autoencoders (VAEs).
Session 3: Transformers and Self-Attention Mechanisms for NLP.
Hands-on: Building and Training a Simple GAN for Image Generation.

Day 6 – Model Optimization and Deployment (6 Hours)

Session 1: Hyperparameter Tuning and Early Stopping.
Session 2: Distributed Training and Mixed Precision Optimization.
Session 3: Model Export and Deployment with TensorFlow Serving and TorchScript.
Workshop: Deploying Deep Learning Models on Cloud Platforms (AWS, Azure).

Day 7 – Capstone Project & Enterprise Integration (6 Hours)

Session 1: Designing a Deep Learning Pipeline for an Enterprise Problem.
Session 2: Capstone Project Development and Evaluation.
Session 3: Presentation and Review of Final Solutions.
Group Discussion: Future of Deep Learning in Industry Applications.

Capstone Project

The capstone project enables participants to apply deep learning models to solve a real-world enterprise challenge. Examples include building an image classification system for quality inspection, an NLP chatbot for customer service, or a forecasting model for financial prediction. Participants will document their workflow, hyperparameter choices, model performance, and deployment strategy as part of the final presentation.

Evaluation & Certification Framework

Daily hands-on exercises and code assignments (30%).
Participation in workshops and case discussions (20%).
Final capstone project implementation and presentation (50%).

Participants who complete the program will receive an ‘Enterprise Deep Learning Specialist’ certificate from Anika Technologies.

Future Trends in Deep Learning

The future of Deep Learning lies in efficiency, explainability, and accessibility. Emerging trends include transformer-based architectures beyond NLP, multimodal learning (combining text, image, and audio), and real-time inference on edge devices. As enterprises adopt AI at scale, the focus will shift towards energy-efficient training, federated learning, and automated model optimization. The professionals who master these advancements will lead the next wave of innovation in AI-driven enterprises.