Deep Learning with Tensorflow Training in New York, USA

The Deep Learning with TensorFlow training equips participants with the skills to build, train, and deploy advanced deep learning models using TensorFlow. This course covers foundational concepts such as tensor operations, neural network architectures, and model optimization, along with hands-on practice in implementing deep learning solutions for tasks like image recognition, natural language processing, and time-series analysis. Participants will gain a deep understanding of TensorFlow's ecosystem and tools, enabling them to tackle real-world AI challenges effectively.

Foundations of Deep Learning and TensorFlow Basics

1. Introduction to Deep Learning
• Overview of deep learning concepts
• Understanding neural networks and their components (layers, neurons, weights, and biases)
• Key applications of deep learning across industries
2. Getting Started with TensorFlow
• Introduction to the TensorFlow ecosystem
• Installing TensorFlow and setting up the environment
• Basic operations and TensorFlow computational graph
• Practical exercise
3. Data Preparation for Deep Learning
• Loading and preprocessing data with TensorFlow Datasets
• Scaling, normalization, and reshaping data for neural networks
• Practical exercise

Building and Training Neural Networks

4. Constructing Neural Networks with TensorFlow
• Introduction to Keras API in TensorFlow
• Building sequential and functional models
• Understanding activation functions and when to use them
• Practical exercise

Building and Training Neural Networks

5. Training Deep Learning Models
• Understanding the process of training: forward propagation, backpropagation, and gradient descent
• Defining loss functions and optimizers
• Techniques to prevent overfitting (e.g., dropout, regularization)
• Practical exercise
6. Evaluating Model Performance
• Metrics for evaluating model performance: accuracy, precision, recall, F1-score
• Using validation sets and cross-validation to prevent overfitting
• Confusion matrix and its interpretation
• Practical exercise

Advanced Concepts and Real-World Applications

7. Convolutional Neural Networks (CNNs) for Image Classification
• Understanding CNN architecture and its applications in image processing
• Layers in CNN: Convolutional, pooling, and fully connected layers
• Transfer learning with pre-trained models (e.g., VGG, ResNet)
• Practical exercise
8. Recurrent Neural Networks (RNNs) for Sequential Data
• Introduction to RNNs and their applications in time-series and text data
• LSTM and GRU cells for long-term dependencies
• Practical exercise
9. TensorFlow for Text Processing
• Preprocessing text data for deep learning: tokenization, padding, and embedding layers
• Word embeddings and how they improve NLP models
• Practical exercise
10. Model Deployment and Optimization

• Exporting and saving models using TensorFlow’s SavedModel format
• Deploying models for inference (on cloud or local servers)
• Model optimization techniques: quantization, pruning, and converting to TensorFlow Lite for mobile devices
• Practical exercise

Course Recap and Q&A Session

• Summary of key concepts covered during the course
• Q&A session for addressing queries and challenges faced during practical exercises
• Final thoughts on how to further explore and develop deep learning skills