Deep Learning Training in San Francisco

Deep Learning Training in San Francisco, USA

Dive into neural networks to build models for image, speech, and text processing.

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Deep Learning Training is a professional training program delivered by ProgNXT in San Francisco, USA. ProgNXT, a globally recognized corporate training provider in 55+ countries, offers this Deep Learning Training course with hands-on, instructor-led sessions. Deep Learning training provides participants with an in-depth understanding of neural networks and their advanced architectures, such as...

Expert Panel

Designed by the ProgNXT AI & Data Science Expert Panel, specializing in Generative AI, Machine Learning, and ChatGPT applications

ProgNXT AI & Data Science Expert Panel

Course Code: PRO04
14 Hrs
Last Updated: April 2026

Course Rating5/5

Deep Learning training provides participants with an in-depth understanding of neural networks and their advanced architectures, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers. The course covers essential topics like activation functions, backpropagation, optimization techniques, and deep learning frameworks like TensorFlow and PyTorch. Participants learn to build, train, and fine-tune models for tasks such as image recognition, natural language processing, and time series forecasting. Hands-on exercises focus on preparing datasets, designing network architectures, and deploying deep learning models in real-world scenarios. By the end of the training, attendees will be equipped to harness the power of deep learning for cutting-edge applications across industries.

Basic understanding of Python programming
Familiarity with basic mathematical concepts such as linear algebra and probability
Basic knowledge of machine learning algorithms

Upon completing the Deep Learning for Beginners course, participants will:

Understand the fundamentals of deep learning and neural networks.
Gain practical experience in implementing deep learning models.
Learn how to use popular deep learning frameworks like TensorFlow and Keras.
Develop and train simple neural networks for image and text classification tasks.

Deep Learning Fundamentals: Understanding key concepts such as neural networks, activation functions, and backpropagation.
Neural Network Architecture: Ability to design, train, and evaluate basic neural networks.
Framework Proficiency: Hands-on experience with TensorFlow and Keras for deep learning model development.
Model Optimization: Skills to improve model performance using techniques like regularization, dropout, and fine-tuning.

Neural Network Architecture: Knowledge of the basic components of a neural network.
Deep Learning Techniques: Ability to train and evaluate a neural network for specific tasks.
Framework Usage: Proficiency in using TensorFlow/Keras for deep learning projects.
Model Optimization: Skills in optimizing models to improve accuracy and performance.

Certificate of Completion: Participants who attend all sessions and successfully complete the course assessments will receive a ProgNXT Certificate of Completion for the Deep Learning for Beginners Training course.

Introduction to Deep Learning and Neural Networks

Introduction to Deep Learning

Overview of deep learning and its applications
How deep learning differs from traditional machine learning
Real-world applications: image classification, NLP, etc.

Neural Networks and Perceptrons

Structure of a neural network
Perceptron model and its limitations
Activation functions: ReLU, Sigmoid, and Tanh

Training Neural Networks

Forward propagation and backpropagation
Loss functions and optimization techniques (Gradient Descent)
Practical exercise

Using TensorFlow and Keras

Introduction to TensorFlow and Keras libraries
Setting up the environment for deep learning projects
Practical exercise

Advanced Topics in Deep Learning

Improving Neural Network Performance

Overfitting and underfitting in neural networks
Techniques to improve model accuracy: Regularization, dropout, and data augmentation
Practical exercise

Convolutional Neural Networks (CNNs)

Introduction to CNNs and their architecture
Applications in image classification and computer vision
Practical exercise

Deep Learning for Text

Introduction to Recurrent Neural Networks (RNNs)
Applications in natural language processing
Practical exercise

Hands-On Exercises and Evaluation

Hands-on Exercises: Build and train a neural network model on a real dataset
Evaluating model performance: metrics like accuracy, precision, and recall

Detailed course slides and lecture notes
Python code examples and datasets for hands-on exercises
Access to TensorFlow and Keras documentation and resources

Participants will be evaluated through practical exercises and a final project.
ProgNXT Certificates of completion will be awarded to participants who successfully complete the course assessments.

Deep Learning Training Events in san-francisco

Online Events

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More Upcoming Session Dates

October 2026

🌐 Online Instructor-Led

11th Oct 2026→12th Oct 2026

🌐 Online Instructor-Led

18th Oct 2026→19th Oct 2026

📍 San Francisco, USA

22nd Oct 2026→23rd Oct 2026

November 2026

📍 San Francisco, USA

5th Nov 2026→6th Nov 2026

🌐 Online Instructor-Led

8th Nov 2026→9th Nov 2026

🌐 Online Instructor-Led

19th Nov 2026→20th Nov 2026

📍 San Francisco, USA

23rd Nov 2026→24th Nov 2026

December 2026

📍 San Francisco, USA

2nd Dec 2026→3rd Dec 2026

🌐 Online Instructor-Led

5th Dec 2026→6th Dec 2026

🌐 Online Instructor-Led

24th Dec 2026→25th Dec 2026

📍 San Francisco, USA

28th Dec 2026→29th Dec 2026

Events for other courses in San Francisco, USA

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Deep Learning Training in San Francisco