Deep Learning Essentials
Designed for ambitious professionals seeking to master the foundations of deep learning, this program blends essential neural concepts, practical coding, and real-world applications to equip participants with the skills to build and apply advanced AI models in today’s evolving landscape.
4.9 (6)
Language
English
Why Bakkah?
Money Guaranteed
Global Accreditation
Flexible Learning
About this Course
What to Expect From This Deep Learning Essentials Course?
By the end, participants will be able to:
- Understand the structure and function of neural networks.
- Apply activation functions, layers, and loss functions effectively.
- Utilize TensorFlow and Keras for building AI models.
- Develop Convolutional Neural Networks (CNNs) for image classification tasks.
- Explore Recurrent Neural Networks (RNNs) for sequence-based data.
- Integrate deep learning models into practical business and technical solutions.
Who Should Enroll in this Deep Learning Essentials Course?
- Data scientists and AI enthusiasts are aiming to deepen their technical expertise.
- Software engineers and developers seeking to build deep learning applications.
- Professionals in IT and business looking to apply AI solutions in real-world contexts.
- Students and researchers interested in advanced machine learning concepts.
- Anyone aspiring to specialize in neural networks and deep learning.
What are the acquired skills from this Deep Learning Essentials Course?
- Designing and training neural networks.
- Applying activation and loss functions in deep learning models.
- Building AI solutions using TensorFlow and Keras.
- Developing CNNs for computer vision tasks.
- Implementing RNNs for sequential and time-series data.
- Translating deep learning models into real-world business applications.
Learn your way, at your pace.
Get the skills you need with a flexible learning experience designed to fit your lifestyle.
Deep Learning Essentials - Self Study
Best for busy learners who need flexibility.
Course Inclusions
- Biological inspiration & artificial neurons
- Structure: input, hidden, output layers
- Weights, biases, and forward propagation
- Backpropagation explained step by step
- Activation functions overview
- Saudi use case: Arabic handwriting recognition
- Sigmoid, ReLU, Tanh, Softmax explained
- Layer types: fully connected, convolutional, recurrent
- Loss functions: MSE, cross-entropy
- Why loss functions matter
- Saudi healthcare example: diagnosing X-ray images
- Why frameworks? TensorFlow vs. PyTorch
- Installing TensorFlow/Keras
- Building your first neural network step by step
- Model compilation, fitting, and evaluation
- Hands-on example: classifying simple images
- Saudi/Gulf example: product demand prediction
- What CNNs are & why they work for images
- Convolution & filters explained
- Pooling layers and feature maps
- Building a CNN with Keras
- Case study: Facial recognition in Saudi airports
- Exercise: Build CNN for CIFAR-10 dataset
- Sequential data explained (time-series, text, speech)
- RNN structure & how it remembers past inputs
- Vanishing gradient problem & solutions (LSTM, GRU)
- Saudi example: predicting Tadawul stock prices
- Exercise: Build a simple RNN for text prediction
Our Happy Clients Say
I have a busy job...
With a demanding job, I thought exam prep was impossible. But self-study learning fit into my life perfectly—I studied anytime, anywhere. It was clear, well-structured, and I passed the exam on my first try.
I needed real interaction...
I was looking for a learning experience where I could truly engage with. Live sessions gave me clarity, motivation, and real-time support. The trainer and group sessions kept me focused and made tough topics easier to digest
Staying on track was...
Starting was easy—but staying consistent wasn’t. The live schedule and trainer check-ins gave just the push I needed. I stayed on track and actually finished the course and got certified!
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