A Deep Net Without Activations Is One Layer

Stack as many layers as you want, but with no activation function in between, all that depth is fake. Multiply the weight matrices together and they collapse into a single matrix, so the whole network is exactly one linear layer in disguise. On a graph it can only ever draw a straight line: stacking another layer just re-tilts the slope, it never bends. Slip a ReLU between the layers, though, and each neuron adds a kink, so the network can finally bend and fold into shapes no single line could reach. That is the entire job of an activation function: depth only counts if you bend it. #shorts *Follow Me* ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ 🐦 X: @datamlistic https://x.com/datamlistic 📸 Instagram: @datamlistic https://www.instagram.com/datamlistic 📱 TikTok: @datamlistic https://www.tiktok.com/@datamlistic 👔 Linkedin: https://www.linkedin.com/company/datamlistic *Channel Support* ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ The best way to support the channel is to share the content. ;) If you'd like to also support the channel financially, donating the price of a coffee is always warmly welcomed! (completely optional and voluntary) ► Patreon: https://www.patreon.com/datamlistic ► Bitcoin (BTC): 3C6Pkzyb5CjAUYrJxmpCaaNPVRgRVxxyTq ► Ethereum (ETH): 0x9Ac4eB94386C3e02b96599C05B7a8C71773c9281 ► Cardano (ADA): addr1v95rfxlslfzkvd8sr3exkh7st4qmgj4ywf5zcaxgqgdyunsj5juw5 ► Tether (USDT): 0xeC261d9b2EE4B6997a6a424067af165BAA4afE1a #shorts #neuralnetworks #deeplearning #machinelearning