Upcoming Batches for Deep Learning with TensorFlow
Not compatible with the above dates?
Deep Learning with TensorFlow Course Details
Welcome to a journey where the complexities of deep learning unfold with simplicity and excitement! ZX Academy invites you to our Deep Learning with TensorFlow online course, a unique blend of expert knowledge and practical learning. Whether you're starting fresh or enhancing your skills, this course is your gateway to becoming a deep learning enthusiast.
Imagine diving into a learning adventure with industry leaders, where every lesson brings you closer to mastering TensorFlow. Our course is more than just theoretical knowledge; it's about getting your hands dirty with real-world problems and solutions. It's an experience tailored for dreamers and doers, like you, who aim to lead in the AI-driven world.
Deep learning is a technology that can completely change careers—it's more than just a talent. Taking the ZX Academy online course Deep Learning with TensorFlow entails delving deeply into the field of artificial intelligence and neural networks. With TensorFlow, you will acquire a thorough understanding of deep learning methods, from their fundamental ideas to their real-world uses.
Our course prepares you for a variety of professions in the artificial intelligence field by skillfully integrating theory and practical training. You will be an expert in creating, refining, and implementing deep learning models by the end of the course, putting you at the vanguard of AI-driven decision-making.
Course Highlights:
Foundations of Deep Learning: Embark on your educational journey by delving into the essentials of deep learning and its pivotal role in the realm of artificial intelligence.
Mastering TensorFlow: Gain practical expertise in TensorFlow, an esteemed and extensively utilized framework in deep learning.
Neural Network Exploration: Delve into the core of deep learning by studying neural networks. Learn to skillfully design and train these networks for optimal performance.
Convolutional Neural Networks (CNNs): Investigate CNNs and their significant applications in image processing and computer vision, a key area in modern AI.
Recurrent Neural Networks (RNNs): Achieve proficiency in RNNs and understand their effectiveness in analyzing sequential data, particularly in natural language processing.
Transfer Learning Techniques: Explore the strategy of employing pre-trained models to enhance efficiency and resource management in deep learning initiatives.
Arguments for Selecting the TensorFlow Deep Learning Course from ZX Academy?
Professional Development: You will greatly improve your chances of landing a job after completing our course and receiving a deep learning certification. A lot of fascinating prospects in the AI industry are accessible with this certificate.
Industry Recognition: Choose a certification that highlights your deep learning expertise—especially with TensorFlow—to set yourself apart in the cutthroat AI profession.
Practical Learning through Real-World Projects: This course gives you the skills and confidence to tackle challenging AI problems.
Keeping Up: To stay relevant and progressive in the ever changing field of artificial intelligence, stay up to date with the newest methods and resources for deep learning.
Prerequisites for Taking the Deep Learning with TensorFlow Course:
The course is structured to accommodate learners with varying levels of experience. Whether you're a beginner or have some background in AI, our program is designed to enrich your expertise.
Are you ready to investigate deep learning with TensorFlow?
Your journey towards becoming a deep learning specialist in TensorFlow has just begun. Enrolling in ZX Academy can begin a transformative educational journey; don't pass this opportunity up. Discover the limitless potential of artificial intelligence by enrolling in our TensorFlow and Deep Learning courses.
Salary Trends:
The average salary for someone with TensorFlow knowledge in India is around ₹23.7 lakhs, with a range of ₹17 lakhs to ₹52 lakhs.Are you excited about this?
Deep Learning with TensorFlow Curriculum
What is Deep Learning?
Limitations of Machine Learning
Advantage of Deep Learning over Machine learning
3 Reasons to go for Deep Learning
Real-Life use cases of Deep Learning
Review of Machine Learning: Regression, Classification, Clustering, Reinforcement Learning,
Underfitting and Overfitting, Optimization
Activation Functions
Illustrate Perceptron
Training a Perceptron
Important Parameters of Perceptron
What is TensorFlow?
TensorFlow code-basics
Graph Visualization
Constants, Placeholders, Variables
Creating a Model
Step by Step - Use-Case Implementation
Understand Neural Networks in Detail
Illustrate Multi-Layer Perceptron
Backpropagation – Learning Algorithm
Understand Backpropagation – Using Neural Network Example
MLP Digit-Classifier using TensorFlow
TensorBoard
Why Deep Networks give better accuracy?
Use-Case Implementation on SONAR dataset
Understand How Deep Network Works?
How Backpropagation Works?
Illustrate Forward pass, Backward pass
Different variants of Gradient Descent
Types of Deep Networks
CNNs Application
Architecture of a CNN
Convolution and Pooling layers in a CNN
Understanding and Visualizing a CNN
Application use cases of RNN
Modelling sequences
Training RNNs with Backpropagation
Long Short-Term Memory (LSTM)
Recursive Neural Tensor Network Theory
Recurrent Neural Network Model
Applications of RBM
Collaborative Filtering with RBM
Introduction to Autoencoders
Autoencoders applications
Understanding Autoencoders
How to compose Models in Keras
Sequential Composition
Functional Composition
Predefined Neural Network Layers
What is Batch Normalization
Saving and Loading a model with Keras
Customizing the Training Process
Using TensorBoard with Keras
Use-Case Implementation with Keras
Composing Models in TFLearn
Sequential Composition
Functional Composition
Predefined Neural Network Layers
What is Batch Normalization
Saving and Loading a model with TFLearn
Customizing the Training Process
Using TensorBoard with TFLearn
Use-Case Implementation with TFLearn
Real-time Deep Learning with TensorFlow project
Project demonstration
Expert evaluation and feedback
Like the curriculum?
Projects on Deep Learning with TensorFlow
Exploration of CNNs for Picture Classification:
Project Description: Put yourself in the shoes of a passionate deep learner exploring the field of picture classification. Developing an intelligent algorithm to classify photographs into different groups is your interesting challenge. A model will be trained to identify and comprehend various visual features in photos, so get ready to get your hands dirty using Convolutional Neural Networks (CNNs).
Skills to Learn: Gaining mastery of CNNs using TensorFlow is the goal of this journey, not only learning about them. It will allow you to experiment with picture preprocessing, learn how to extract important features, and adjust the parameters of your model to detect details with remarkable precision.
Time Series Forecasting Adventure with RNNs:
Project Description: Assume the role of a futuristic data scientist, and your mission is to forecast future events. Recurrent neural networks (RNNs) will be used to forecast future situations and delve into the fascinating world of time series data, such as weather patterns or stock market movements.
Competencies You'll Gain: LSTM networks and RNNs are cousins; this project is your key to understanding them. Gain control over managing time-based data, delve into the subtleties of sequential modeling, and master the art of making predictions about the future based on historical trends by utilizing TensorFlow.
Project Resources
Deep Learning with TensorFlow Certification
Career Prospects: A greater variety of work prospects in the AI and deep learning fields should be available to you with this qualification.
Pay Negotiation: Since employers frequently honor and reward trained experts, you may have an advantage in this situation.
Professional Recognition: It boosts your stature in the data science and artificial intelligence fields, making you a desirable candidate.
Resource Dive: Delve into course materials, textbooks, online modules, and suggested readings to build a strong foundation.
Hands-On Learning: Apply your knowledge to real-life deep learning projects to reinforce your understanding.
Mock Exams: Practice with sample exams to get a feel for the certification exam format and identify areas where you need improvement.
Community Engagement: Join AI and deep learning communities and forums to learn, ask questions, share knowledge, and grow alongside fellow enthusiasts.
Get certification or Get ahead with Zx Academy’s Certification or
Faq’s for Deep Learning with TensorFlow
Reviews
Bhumika Shinde
